Learners who want a structured route across connected courses
School of Data & AIBeginner to Intermediate
Data Science & Machine Learning
Learn the practical path from data analysis to machine learning.
Build the Python, statistics, machine learning, modeling, evaluation, and storytelling skills needed to solve real-world data science problems and present your work professionally.
Target role
Data Scientist, Applied ML Engineer, Analytics Scientist
Duration
Flexible duration - Flexible weekly pace
Course sequence
5 courses
Support model
Choose your learning support level
Outcome
Built around a clear role target.
Data Scientist, Applied ML Engineer, Analytics Scientist
Course sequence
See how the courses build into the full path.
Each course has a focused job, but the value compounds when you follow the sequence, complete the projects, and use the support model around the full path.
School of Data & AIBeginner to Intermediate5 courses
Data Science & Machine Learning
Build practical data science and machine learning skills by learning Python, statistics, data preparation, model training, evaluation, interpretation, and end-to-end data science projects.
Target role
Data Scientist, Applied ML Engineer, Analytics Scientist
Duration
Flexible duration - Flexible weekly pace
Support
Choose your learning support level
1 Path only2 weeksBeginnerData Foundations
Build the essential foundation for working with data, understanding business problems, and preparing for tools like Excel, SQL, Python, Power BI, machine learning, AI, and data engineering.
Understand how data is used to solve real business problems.Available through the path so the work stays connected to the full outcome.Understand how data is used to solve real business problems.Explain the difference between data, reports, dashboards, insights, and decisions.Understand the major roles across analytics, data science, AI, and data engineering.Identify common tools used by modern data teams.View course outline2 Standalone + path8 weeksBeginner to IntermediatePython for Data Analytics
Learn Python for real analytics work: data cleaning, exploration, transformation, automation, and visual insight generation.
Write Python code for data analysis tasks.Can be started alone, then compounded inside the full path.Write Python code for data analysis tasks.Use notebooks for structured exploratory analysis.Import CSV, Excel, and structured data files.Clean missing, duplicated, inconsistent, and messy data.View course3 Path only6 weeksBeginner to IntermediateStatistics for Data Science
Build the statistical foundation needed to understand data, measure uncertainty, test assumptions, interpret patterns, and prepare for machine learning.
Understand the role of statistics in data science and decision-making.Available through the path so the work stays connected to the full outcome.Understand the role of statistics in data science and decision-making.Use descriptive statistics to summarize datasets.Understand probability, uncertainty, and variation.Interpret distributions, outliers, and data spread.View course outline4 Path only8 weeksIntermediateApplied Machine Learning
Apply machine learning to realistic datasets through feature engineering, model selection, evaluation, tuning, interpretation, and project presentation.
Frame real-world problems as machine learning tasks.Available through the path so the work stays connected to the full outcome.Frame real-world problems as machine learning tasks.Prepare datasets for modeling.Create and select useful features.Train and compare multiple machine learning models.View course outline5 Path only8 weeksIntermediateData Science Studio
Complete end-to-end data science projects that combine problem framing, data cleaning, exploration, statistics, visualization, modeling, evaluation, storytelling, and presentation.
Plan end-to-end data science projects.Available through the path so the work stays connected to the full outcome.Plan end-to-end data science projects.Frame business or product problems as data science questions.Clean, explore, and prepare real datasets.Apply statistics and visualization to understand patterns.View course outline
Curriculum
Follow the courses in sequence.
The path moves toward Data Scientist, Applied ML Engineer, Analytics Scientist through complete course outlines, from phases and modules down to lesson page topics.
1Beginner2 weeksPath onlyData FoundationsBuild the essential foundation for working with data, understanding business problems, and preparing for tools like Excel, SQL, Python, Power BI, machine learning, AI, and data engineering.6 phases7 modules28 lessons88 pages
1Phase 1 - Introduction to Data & AIIntroduce data, organizational data use, and the modern data ecosystem.1 modules3 lessons1 week
Module 1: The World of DataUnderstand what data is, how organizations use it, and how the modern data ecosystem works.3 lessons
Lesson 1: What Is Data?Understand data as recorded facts, observations, events, and signals that can be interpreted to support decisions.75 minarticle5 pages
Welcome and Learning Objectives
Start the lesson and understand the purpose of learning data foundations.
8 min
Data in Plain English
Explain what data is and why context matters.
15 min
Structured, Semi-Structured, and Unstructured Data
Classify the main forms of data students will meet in analytics, data science, AI, and data engineering.
18 min
Data Sources in Everyday Life
Help students see that data is created constantly by normal activities.
15 min
Exercise - Daily Data Source Audit
Students identify real data sources around them and classify them.
19 min
Lesson 2: How Organizations Use DataExplore how businesses and institutions use data for decisions, reporting, forecasting, optimization, and automation.80 minarticle5 pages
Welcome and Learning Objectives
Introduce organizational data use.
8 min
The Five Common Uses of Data
Explain common ways data supports organizations.
22 min
Case Study - Netflix Recommendations
Introduce recommendations as a beginner-friendly example of organizational data use.
20 min
Data Usage Map
Show how one organization can use many kinds of data.
15 min
Exercise - Company Data Usage Map
Students map how data may be used in a real organization.
15 min
Lesson 3: The Modern Data EcosystemUnderstand how data moves from sources into databases, warehouses, dashboards, models, AI systems, and decisions.80 minarticle5 pages
Welcome and Learning Objectives
Introduce the modern data ecosystem.
8 min
The Main Components
Explain core components in the ecosystem.
22 min
Complete Data Flow Example
Show a realistic beginner-friendly data flow.
20 min
Diagram Exercise - Draw Complete Data Flow
Students create a data ecosystem diagram.
20 min
Module Summary
Summarize the first module and prepare students for data roles.
10 min
2Phase 2 - Data Careers & RolesHelp students understand the major data and AI career paths before choosing a specialization.1 modules5 lessons1 week
Module 1: Understanding the Data ProfessionCompare the responsibilities, deliverables, tools, and thinking patterns across modern data roles.5 lessons
Lesson 1: Data AnalystUnderstand what data analysts do, the problems they solve, and the deliverables they create.45 minarticle3 pages
Overview and Learning Objectives
Start the lesson and understand what the student should be able to do.
8 min
Concepts and Examples
Introduce the main concepts with practical examples.
22 min
Practice Activity
Apply the concept through a guided activity.
15 min
Lesson 2: Data ScientistUnderstand how data scientists use statistics, programming, and models to explore patterns and make predictions.45 minarticle3 pages
Overview and Learning Objectives
Start the lesson and understand what the student should be able to do.
8 min
Concepts and Examples
Introduce the main concepts with practical examples.
22 min
Practice Activity
Apply the concept through a guided activity.
15 min
Lesson 3: Data EngineerUnderstand how data engineers build pipelines, warehouses, and infrastructure that make data usable.45 minarticle3 pages
Overview and Learning Objectives
Start the lesson and understand what the student should be able to do.
8 min
Concepts and Examples
Introduce the main concepts with practical examples.
22 min
Practice Activity
Apply the concept through a guided activity.
15 min
Lesson 4: AI EngineerUnderstand AI engineering at a high level, including LLMs, RAG, agents, and AI product systems.45 minarticle3 pages
Overview and Learning Objectives
Start the lesson and understand what the student should be able to do.
8 min
Concepts and Examples
Introduce the main concepts with practical examples.
22 min
Practice Activity
Apply the concept through a guided activity.
15 min
Lesson 5: Choosing Your PathUse a practical decision framework to choose a data or AI learning path.60 minarticle3 pages
Overview and Learning Objectives
Start the lesson and understand what the student should be able to do.
8 min
Concepts and Examples
Introduce the main concepts with practical examples.
30 min
Practice Activity
Apply the concept through a guided activity.
22 min
3Phase 3 - Data ThinkingTeach students how to ask better questions, define useful metrics, investigate causes, and communicate insights.1 modules4 lessons1 week
Module 1: Analytical ThinkingBuild the thinking habits that separate tool users from real data professionals.4 lessons
Lesson 1: Questions Before AnswersLearn to define problems and ask useful analytical questions before touching tools.50 minarticle3 pages
Overview and Learning Objectives
Start the lesson and understand what the student should be able to do.
8 min
Concepts and Examples
Introduce the main concepts with practical examples.
25 min
Practice Activity
Apply the concept through a guided activity.
17 min
Lesson 2: Metrics and KPIsUnderstand metrics, KPIs, vanity metrics, and actionable measures.55 minarticle3 pages
Overview and Learning Objectives
Start the lesson and understand what the student should be able to do.
8 min
Concepts and Examples
Introduce the main concepts with practical examples.
27 min
Practice Activity
Apply the concept through a guided activity.
20 min
Lesson 3: Root Cause AnalysisLearn how to investigate business problems instead of jumping to shallow conclusions.55 minarticle3 pages
Overview and Learning Objectives
Start the lesson and understand what the student should be able to do.
8 min
Concepts and Examples
Introduce the main concepts with practical examples.
27 min
Practice Activity
Apply the concept through a guided activity.
20 min
Lesson 4: Data StorytellingLearn how to communicate insights through clear narratives and decision-focused reporting.55 minarticle3 pages
Overview and Learning Objectives
Start the lesson and understand what the student should be able to do.
8 min
Concepts and Examples
Introduce the main concepts with practical examples.
27 min
Practice Activity
Apply the concept through a guided activity.
20 min
4Phase 4 - Working with DataDevelop core data literacy: data types, data quality, cleaning concepts, and exploratory analysis.1 modules4 lessons1 week
Module 1: Data LiteracyUnderstand datasets, quality issues, cleaning concepts, and beginner exploration.4 lessons
Lesson 1: Data TypesClassify numeric, categorical, time-series, and text data.45 minarticle3 pages
Overview and Learning Objectives
Start the lesson and understand what the student should be able to do.
8 min
Concepts and Examples
Introduce the main concepts with practical examples.
22 min
Practice Activity
Apply the concept through a guided activity.
15 min
Lesson 2: Data QualityIdentify missing values, duplicates, inconsistencies, and their business impact.50 minarticle3 pages
Overview and Learning Objectives
Start the lesson and understand what the student should be able to do.
8 min
Concepts and Examples
Introduce the main concepts with practical examples.
25 min
Practice Activity
Apply the concept through a guided activity.
17 min
Lesson 3: Data Cleaning ConceptsUnderstand validation, standardization, and transformation at a beginner level.50 minarticle3 pages
Overview and Learning Objectives
Start the lesson and understand what the student should be able to do.
8 min
Concepts and Examples
Introduce the main concepts with practical examples.
25 min
Practice Activity
Apply the concept through a guided activity.
17 min
Lesson 4: Exploratory AnalysisLearn how to explore patterns, trends, outliers, and initial questions in a dataset.55 minarticle3 pages
Overview and Learning Objectives
Start the lesson and understand what the student should be able to do.
8 min
Concepts and Examples
Introduce the main concepts with practical examples.
27 min
Practice Activity
Apply the concept through a guided activity.
20 min
5Phase 5 - Statistics for Decision MakingTeach practical, business-focused statistics without heavy mathematics.1 modules4 lessons1 week
Module 1: Practical StatisticsUse basic statistics to summarize data and support better decisions.4 lessons
Lesson 1: Descriptive StatisticsUnderstand mean, median, mode, variance, and how they describe business data.50 minarticle3 pages
Overview and Learning Objectives
Start the lesson and understand what the student should be able to do.
8 min
Concepts and Examples
Introduce the main concepts with practical examples.
25 min
Practice Activity
Apply the concept through a guided activity.
17 min
Lesson 2: Probability ConceptsUnderstand uncertainty, risk, and likelihood using business examples.45 minarticle3 pages
Overview and Learning Objectives
Start the lesson and understand what the student should be able to do.
8 min
Concepts and Examples
Introduce the main concepts with practical examples.
22 min
Practice Activity
Apply the concept through a guided activity.
15 min
Lesson 3: Correlation vs CausationAvoid common mistakes and misleading conclusions when variables move together.50 minarticle3 pages
Overview and Learning Objectives
Start the lesson and understand what the student should be able to do.
8 min
Concepts and Examples
Introduce the main concepts with practical examples.
25 min
Practice Activity
Apply the concept through a guided activity.
17 min
Lesson 4: Making Decisions with DataUse confidence, evidence, and tradeoffs to make better business decisions.55 minarticle3 pages
Overview and Learning Objectives
Start the lesson and understand what the student should be able to do.
8 min
Concepts and Examples
Introduce the main concepts with practical examples.
27 min
Practice Activity
Apply the concept through a guided activity.
20 min
6Phase 6 - Data, AI & EthicsIntroduce responsible data use, privacy, bias, AI ethics, and the future of data work.2 modules8 lessons1 week
Module 1: Responsible Data UseBuild responsible habits around privacy, bias, fairness, transparency, and AI accountability.4 lessons
Lesson 1: Data PrivacyUnderstand personal data, consent, and compliance concepts at a beginner level.45 minarticle3 pages
Overview and Learning Objectives
Start the lesson and understand what the student should be able to do.
8 min
Concepts and Examples
Introduce the main concepts with practical examples.
22 min
Practice Activity
Apply the concept through a guided activity.
15 min
Lesson 2: Bias in DataUnderstand how bias, fairness, and representation affect data conclusions.50 minarticle3 pages
Overview and Learning Objectives
Start the lesson and understand what the student should be able to do.
8 min
Concepts and Examples
Introduce the main concepts with practical examples.
25 min
Practice Activity
Apply the concept through a guided activity.
17 min
Lesson 3: AI EthicsUnderstand hallucinations, transparency, accountability, and safe AI usage.55 minarticle3 pages
Overview and Learning Objectives
Start the lesson and understand what the student should be able to do.
8 min
Concepts and Examples
Introduce the main concepts with practical examples.
27 min
Practice Activity
Apply the concept through a guided activity.
20 min
Lesson 4: Future of Data and AIExplore AI transformation, automation, emerging careers, and personal roadmap planning.50 minarticle3 pages
Overview and Learning Objectives
Start the lesson and understand what the student should be able to do.
8 min
Concepts and Examples
Introduce the main concepts with practical examples.
25 min
Practice Activity
Apply the concept through a guided activity.
17 min
Module 2: Foundations Projects and GraduationPackage learning into mini projects, a final foundations project, and graduation requirements.4 lessons
Lesson 1: Mini Project 1 - Business Metrics AnalysisAnalyze a business domain and design practical metrics and recommendations.60 minarticle3 pages
Overview and Learning Objectives
Start the lesson and understand what the student should be able to do.
8 min
Concepts and Examples
Introduce the main concepts with practical examples.
30 min
Practice Activity
Apply the concept through a guided activity.
22 min
Lesson 2: Mini Project 2 - Data Quality AuditAudit a messy dataset and produce a quality report.60 minarticle3 pages
Overview and Learning Objectives
Start the lesson and understand what the student should be able to do.
8 min
Concepts and Examples
Introduce the main concepts with practical examples.
30 min
Practice Activity
Apply the concept through a guided activity.
22 min
Lesson 3: Final Foundations Project - Data-Driven Business AnalysisAnalyze a real company or product from a data perspective and present recommendations.90 minarticle3 pages
Overview and Learning Objectives
Start the lesson and understand what the student should be able to do.
8 min
Concepts and Examples
Introduce the main concepts with practical examples.
45 min
Practice Activity
Apply the concept through a guided activity.
37 min
Lesson 4: Graduation Requirements and Portfolio OutcomeClarify what students must complete and what they should have in their portfolio.40 minarticle1 pages
Requirements and Portfolio Checklist
Summarize graduation requirements.
40 min
2Beginner to Intermediate8 weeksPython for Data AnalyticsLearn Python for real analytics work: data cleaning, exploration, transformation, automation, and visual insight generation.8 phases12 modules54 lessons164 pages
1Phase 1 - Python Foundations for AnalystsBuild Python foundations for analytics: tool selection, environment setup, Jupyter workflow, syntax, control flow, data structures, reusable functions, and a metrics calculator project.2 modules9 lessons2 weeks
Module 1: Getting Started with Python for DataUnderstand why Python matters for analytics, set up the environment, use notebooks professionally, and learn essential syntax.4 lessons
Lesson 1: Why Python for Data AnalyticsUnderstand why analysts use Python, how it compares with Excel, SQL, and Power BI, and where it fits in a modern analytics workflow.85 minarticle6 pages
Welcome and Learning Objectives
Introduce Python's role in the analytics journey.
8 min
Why Analysts Use Python
Explain Python's practical value for analysts.
18 min
Excel vs SQL vs Power BI vs Python
Compare common analytics tools and when each fits best.
22 min
Where Python Fits in the Analytics Workflow
Show Python's place in end-to-end data analysis.
18 min
Real-World Data Use Cases
Connect Python analytics to real businesses.
18 min
Exercise - Analytics Tool Decision Matrix
Students identify when to use Excel, SQL, Power BI, or Python.
19 min
Lesson 2: Python Environment SetupSet up a working Python analytics environment using Python, Anaconda or Miniconda, Jupyter, VS Code, and package installation tools.90 minarticle5 pages
Welcome and Learning Objectives
Introduce the setup required for Python analytics.
8 min
The Tools You Need
Explain the setup tools in plain English.
20 min
Recommended Environment Structure
Show students how to organize their analytics environment.
18 min
Package Installation and Verification
Introduce package installation and verification commands.
22 min
Exercise - Analytics Environment Setup
Students set up a working Python analytics environment.
22 min
Lesson 3: Working in Jupyter NotebooksLearn how to use Jupyter notebooks for organized, reproducible, and stakeholder-readable data analysis.85 minarticle5 pages
Welcome and Learning Objectives
Introduce notebooks as the main analysis workspace.
8 min
Cells, Markdown and Code
Explain notebook building blocks.
18 min
Notebook Organization
Teach professional notebook structure.
20 min
Reproducible Analysis
Explain why rerunnable notebooks matter.
17 min
Exercise - First Analysis Notebook
Students create their first organized analysis notebook.
22 min
Lesson 4: Python Syntax EssentialsLearn the Python syntax essentials analysts need: variables, data types, operators, comments, and naming conventions.90 minarticle5 pages
Welcome and Learning Objectives
Introduce core syntax for analysis.
8 min
Variables and Data Types
Explain variables and data types through business examples.
20 min
Operators, Comments and Naming
Teach calculations and readability basics.
22 min
Business Calculation Examples
Connect syntax to common analytics metrics.
20 min
Exercise - Business Calculation Challenges
Students practice syntax with real business metrics.
20 min
Module 2: Python Control Flow and Data StructuresUse conditions, loops, Python data structures, and functions to represent business logic and reusable calculations.5 lessons
Lesson 1: ConditionsUse if, elif, else, comparison operators, and business rules in Python.55 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Business Examples
Explain the concept with practical business examples.
27 min
Practice Activity
Apply the lesson through a guided Python exercise.
20 min
Lesson 2: LoopsUse for loops and while loops while understanding when loops are unnecessary in data analysis.55 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Business Examples
Explain the concept with practical business examples.
27 min
Practice Activity
Apply the lesson through a guided Python exercise.
20 min
Lesson 3: Lists, Tuples, Sets and DictionariesRepresent customer and order data using Python collections.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Business Examples
Explain the concept with practical business examples.
30 min
Practice Activity
Apply the lesson through a guided Python exercise.
22 min
Lesson 4: Functions for Reusable AnalysisCreate reusable functions for calculations and business metrics.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Business Examples
Explain the concept with practical business examples.
32 min
Practice Activity
Apply the lesson through a guided Python exercise.
25 min
Lesson 5: Mini Project 1 - Business Metrics CalculatorBuild a Python notebook or script that calculates common business metrics using reusable functions.90 minarticle2 pages
Project Brief
Explain the project scenario and expected output.
20 min
Review Checklist
Checklist for project quality.
20 min
2Phase 2 - NumPy and Numerical AnalysisUse NumPy for numerical arrays, vectorized operations, descriptive statistics, outliers, distributions, and group comparisons.1 modules4 lessons1 week
Module 1: NumPy for AnalystsBuild numerical analysis foundations with arrays and vectorized operations.4 lessons
Lesson 1: Introduction to NumPyUnderstand arrays, why NumPy exists, lists vs arrays, and vectorized operations.55 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Business Examples
Explain the concept with practical business examples.
27 min
Practice Activity
Apply the lesson through a guided Python exercise.
20 min
Lesson 2: Array OperationsUse indexing, slicing, broadcasting, and mathematical operations on arrays.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Business Examples
Explain the concept with practical business examples.
30 min
Practice Activity
Apply the lesson through a guided Python exercise.
22 min
Lesson 3: Descriptive Statistics with NumPyUse NumPy for mean, median, standard deviation, percentiles, min, and max.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Business Examples
Explain the concept with practical business examples.
30 min
Practice Activity
Apply the lesson through a guided Python exercise.
22 min
Lesson 4: Practical Numerical AnalysisAnalyze outliers, distributions, summary statistics, and group comparisons.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Business Examples
Explain the concept with practical business examples.
32 min
Practice Activity
Apply the lesson through a guided Python exercise.
25 min
3Phase 3 - Pandas for Data AnalysisUse Pandas to load, inspect, select, filter, clean, transform, and prepare real datasets.2 modules10 lessons2 weeks
Module 1: Pandas FundamentalsLearn Series, DataFrames, data loading, inspection, selection, and filtering.4 lessons
Lesson 1: Introduction to PandasUnderstand Series, DataFrames, indexes, and why Pandas is central to analytics.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Business Examples
Explain the concept with practical business examples.
30 min
Practice Activity
Apply the lesson through a guided Python exercise.
22 min
Lesson 2: Loading DataLoad data from CSV, Excel, JSON, SQL-style outputs, and file paths.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Business Examples
Explain the concept with practical business examples.
32 min
Practice Activity
Apply the lesson through a guided Python exercise.
25 min
Lesson 3: Inspecting DataUse head, tail, info, describe, shape, columns, and dtypes for first-pass inspection.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Business Examples
Explain the concept with practical business examples.
30 min
Practice Activity
Apply the lesson through a guided Python exercise.
22 min
Lesson 4: Selecting and Filtering DataSelect columns, filter rows, use boolean masks, loc, and iloc.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Business Examples
Explain the concept with practical business examples.
32 min
Practice Activity
Apply the lesson through a guided Python exercise.
25 min
Module 2: Data Cleaning with PandasHandle missing data, duplicates, data type conversion, text cleaning, and date analysis.6 lessons
Lesson 1: Handling Missing DataDetect, drop, fill, and reason about missing data with business judgment.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Business Examples
Explain the concept with practical business examples.
32 min
Practice Activity
Apply the lesson through a guided Python exercise.
25 min
Lesson 2: Removing DuplicatesRemove duplicate rows and duplicate keys while understanding business risks.55 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Business Examples
Explain the concept with practical business examples.
27 min
Practice Activity
Apply the lesson through a guided Python exercise.
20 min
Lesson 3: Data Type ConversionConvert numeric, date, categorical, and string data types.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Business Examples
Explain the concept with practical business examples.
30 min
Practice Activity
Apply the lesson through a guided Python exercise.
22 min
Lesson 4: Text CleaningUse string methods to clean customer names, locations, and product categories.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Business Examples
Explain the concept with practical business examples.
30 min
Practice Activity
Apply the lesson through a guided Python exercise.
22 min
Lesson 5: Date and Time AnalysisUse datetime, extract month/year/day, filter periods, and analyze monthly patterns.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Business Examples
Explain the concept with practical business examples.
30 min
Practice Activity
Apply the lesson through a guided Python exercise.
22 min
Lesson 6: Mini Project 2 - Messy Dataset Cleaning ProjectClean a messy business dataset and produce a documented cleaning report.100 minarticle2 pages
Project Brief
Explain the project scenario and expected output.
20 min
Review Checklist
Checklist for project quality.
20 min
4Phase 4 - Data Transformation and Business AnalysisTransform data with Pandas and perform exploratory data analysis for business insights.2 modules11 lessons2 weeks
Module 1: Transforming Data with PandasUse sorting, ranking, grouping, pivot tables, merging, joins, and feature creation for business analysis.5 lessons
Lesson 1: Sorting and RankingFind top customers, bottom products, and ranked business outputs.55 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Business Examples
Explain the concept with practical business examples.
27 min
Practice Activity
Apply the lesson through a guided Python exercise.
20 min
Lesson 2: Grouping and AggregationUse groupby and aggregate for grouped KPIs.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Business Examples
Explain the concept with practical business examples.
32 min
Practice Activity
Apply the lesson through a guided Python exercise.
25 min
Lesson 3: Pivot Tables in PandasBuild Excel-style summary tables in Python.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Business Examples
Explain the concept with practical business examples.
30 min
Practice Activity
Apply the lesson through a guided Python exercise.
22 min
Lesson 4: Merging and Joining DataFramesCombine customers, orders, and products datasets correctly.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Business Examples
Explain the concept with practical business examples.
35 min
Practice Activity
Apply the lesson through a guided Python exercise.
27 min
Lesson 5: Feature Creation for AnalyticsCreate calculated columns, customer segments, revenue bands, flags, and derived metrics.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Business Examples
Explain the concept with practical business examples.
32 min
Practice Activity
Apply the lesson through a guided Python exercise.
25 min
Module 2: Exploratory Data AnalysisAsk questions, form hypotheses, analyze distributions, relationships, trends, customers, and products.6 lessons
Lesson 1: What Is EDA?Understand exploratory data analysis, questions, hypotheses, patterns, and avoiding premature conclusions.55 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Business Examples
Explain the concept with practical business examples.
27 min
Practice Activity
Apply the lesson through a guided Python exercise.
20 min
Lesson 2: Univariate AnalysisAnalyze one variable at a time using distributions, frequencies, and summary statistics.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Business Examples
Explain the concept with practical business examples.
30 min
Practice Activity
Apply the lesson through a guided Python exercise.
22 min
Lesson 3: Bivariate AnalysisAnalyze relationships between two variables using correlation and group comparisons.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Business Examples
Explain the concept with practical business examples.
32 min
Practice Activity
Apply the lesson through a guided Python exercise.
25 min
Lesson 4: Time-Series ExplorationExplore trends, seasonality, rolling averages, and monthly/weekly analysis.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Business Examples
Explain the concept with practical business examples.
32 min
Practice Activity
Apply the lesson through a guided Python exercise.
25 min
Lesson 5: Customer and Product AnalyticsAnalyze customer segmentation, product performance, repeat customers, and retention basics.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Business Examples
Explain the concept with practical business examples.
35 min
Practice Activity
Apply the lesson through a guided Python exercise.
27 min
Lesson 6: Milestone Project 1 - E-Commerce Exploratory AnalysisPerform a full EDA project on an e-commerce dataset.120 minarticle2 pages
Project Brief
Explain the project scenario and expected output.
20 min
Review Checklist
Checklist for project quality.
20 min
5Phase 5 - Data Visualization with PythonUse Matplotlib, Seaborn, Plotly, and storytelling principles to communicate insights visually.2 modules9 lessons1–2 weeks
Module 1: Visualization FundamentalsCreate clear static, statistical, and interactive visuals for business analysis.5 lessons
Lesson 1: Why Visualization MattersUnderstand charts as communication and choose visuals based on audience and question.50 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Business Examples
Explain the concept with practical business examples.
25 min
Practice Activity
Apply the lesson through a guided Python exercise.
17 min
Lesson 2: Matplotlib BasicsCreate line charts, bar charts, histograms, scatter plots, and labeled visuals.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Business Examples
Explain the concept with practical business examples.
32 min
Practice Activity
Apply the lesson through a guided Python exercise.
25 min
Lesson 3: Seaborn for Statistical VisualizationUse count plots, box plots, distribution plots, heatmaps, and category comparisons.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Business Examples
Explain the concept with practical business examples.
32 min
Practice Activity
Apply the lesson through a guided Python exercise.
25 min
Lesson 4: Plotly for Interactive VisualizationCreate interactive charts, tooltips, dashboard-style visuals, and exported visuals.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Business Examples
Explain the concept with practical business examples.
32 min
Practice Activity
Apply the lesson through a guided Python exercise.
25 min
Lesson 5: Visualization StorytellingUse titles, annotations, chart sequence, and insight-first visuals for executive reporting.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Business Examples
Explain the concept with practical business examples.
32 min
Practice Activity
Apply the lesson through a guided Python exercise.
25 min
Module 2: Reporting with PythonTurn notebooks into professional reports, export outputs, and automate repetitive analysis.4 lessons
Lesson 1: Analytical Notebooks as ReportsStructure notebooks with executive summary, methodology, findings, and recommendations.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Business Examples
Explain the concept with practical business examples.
32 min
Practice Activity
Apply the lesson through a guided Python exercise.
25 min
Lesson 2: Exporting ResultsExport CSV, Excel, charts, and notebooks for stakeholders.55 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Business Examples
Explain the concept with practical business examples.
27 min
Practice Activity
Apply the lesson through a guided Python exercise.
20 min
Lesson 3: Automating Repetitive AnalysisUse reusable scripts, scheduled reporting concepts, parameterized notebooks, and report templates.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Business Examples
Explain the concept with practical business examples.
35 min
Practice Activity
Apply the lesson through a guided Python exercise.
27 min
Lesson 4: Milestone Project 2 - Python Business Reporting ProjectBuild a reusable analysis report for a selected business domain.120 minarticle2 pages
Project Brief
Explain the project scenario and expected output.
20 min
Review Checklist
Checklist for project quality.
20 min
6Phase 6 - Working with Real Data SourcesLoad and analyze data from Excel files, JSON, APIs, SQL databases, and end-to-end analytics workflows.1 modules5 lessons1–2 weeks
Module 1: Data from Files, APIs and DatabasesWork with practical data sources used in real analytics teams.5 lessons
Lesson 1: Working with Excel FilesRead Excel sheets, multiple sheets, write Excel outputs, and format outputs.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Business Examples
Explain the concept with practical business examples.
30 min
Practice Activity
Apply the lesson through a guided Python exercise.
22 min
Lesson 2: Working with JSON DataHandle nested JSON, normalize JSON, and work with API-style records.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Business Examples
Explain the concept with practical business examples.
30 min
Practice Activity
Apply the lesson through a guided Python exercise.
22 min
Lesson 3: Working with APIsUse requests, API responses, status codes, pagination basics, and authentication concepts.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Business Examples
Explain the concept with practical business examples.
35 min
Practice Activity
Apply the lesson through a guided Python exercise.
27 min
Lesson 4: Connecting Python to SQL DatabasesConnect to databases, read SQL into Pandas, write query results, and combine SQL with Python workflows.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Business Examples
Explain the concept with practical business examples.
35 min
Practice Activity
Apply the lesson through a guided Python exercise.
27 min
Lesson 5: End-to-End Data WorkflowBuild a mini pipeline that extracts, cleans, transforms, analyzes, visualizes, and reports data.80 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Business Examples
Explain the concept with practical business examples.
40 min
Practice Activity
Apply the lesson through a guided Python exercise.
32 min
7Phase 7 - Professional Analytics WorkflowBuild professional habits for notebooks, project organization, Git, communication, and stakeholder-ready findings.1 modules4 lessons1 week
Module 1: Code Quality for AnalystsImprove readability, reproducibility, organization, version control, and communication.4 lessons
Lesson 1: Clean Notebook PracticesMake notebooks readable, remove dead code, add comments, improve reproducibility, and clean outputs.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Business Examples
Explain the concept with practical business examples.
30 min
Practice Activity
Apply the lesson through a guided Python exercise.
22 min
Lesson 2: Project OrganizationStructure data projects with folders, raw/processed data, notebooks, reports, src, and README.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Business Examples
Explain the concept with practical business examples.
30 min
Practice Activity
Apply the lesson through a guided Python exercise.
22 min
Lesson 3: Version Control for Data ProjectsUse Git basics for notebooks, committing analysis work, README documentation, and portfolio repositories.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Business Examples
Explain the concept with practical business examples.
30 min
Practice Activity
Apply the lesson through a guided Python exercise.
22 min
Lesson 4: Communicating FindingsWrite stakeholder communication, executive summaries, recommendations, limitations, and next steps.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Business Examples
Explain the concept with practical business examples.
32 min
Practice Activity
Apply the lesson through a guided Python exercise.
25 min
8Phase 8 - Capstone, Graduation and PortfolioComplete an end-to-end Python analytics capstone and package a portfolio-ready repository.1 modules2 lessons1 week
Module 1: End-to-End Python Data Analytics CapstoneStudents complete an industry-based capstone project using Python, Pandas, visualization, reporting, and professional project organization.2 lessons
Lesson 1: Final Capstone - End-to-End Python Data Analytics ProjectBuild a professional Python analytics project for one selected industry.160 minarticle2 pages
Project Brief
Explain the project scenario and expected output.
20 min
Review Checklist
Checklist for project quality.
20 min
Lesson 2: Graduation Requirements and Portfolio OutcomeClarify completion requirements and expected portfolio outputs.45 minarticle1 pages
Requirements and Portfolio Checklist
Summarize graduation requirements and portfolio assets.
45 min
3Beginner to Intermediate6 weeksPath onlyStatistics for Data ScienceBuild the statistical foundation needed to understand data, measure uncertainty, test assumptions, interpret patterns, and prepare for machine learning.8 phases10 modules49 lessons143 pages
1Phase 1 - Statistical Thinking for Data ScienceBuild the mindset required for statistics: evidence, uncertainty, sampling, variables, measurement, question design, and bias awareness.1 modules5 lessons1 week
Module 1: Why Statistics MattersUnderstand statistics as decision support and learn how to define better statistical questions before analysis.5 lessons
Lesson 1: What Statistics Is Really ForUnderstand statistics as decision support under uncertainty, not just formulas or academic theory.85 minarticle6 pages
Welcome and Learning Objectives
Introduce the purpose of statistics for data science.
8 min
Statistics as Decision Support
Explain the real purpose of statistics.
18 min
Data vs Evidence
Clarify why data alone is not enough.
18 min
Uncertainty and Misleading Averages
Introduce uncertainty and average traps.
20 min
Statistics in Analytics, ML and AI
Connect statistics to future learning.
18 min
Exercise - Business Claim Evidence Review
Students review business claims and decide whether data supports them.
21 min
Lesson 2: Populations and SamplesUnderstand populations, samples, sampling frames, representative samples, and sampling bias.85 minarticle4 pages
Welcome and Learning Objectives
Introduce populations and samples.
8 min
Population, Sample and Sampling Frame
Explain core sampling terms.
20 min
Representative Samples and Bias
Explain sample representativeness.
22 min
Exercise - Sampling Bias Detective
Students identify sampling problems.
35 min
Lesson 3: Variables and MeasurementClassify numerical, categorical, ordinal, continuous, and discrete variables while recognizing measurement errors.80 minarticle4 pages
Welcome and Learning Objectives
Introduce variables and measurement.
8 min
Types of Variables
Explain variable categories.
22 min
Measurement Errors
Explain measurement risks.
20 min
Exercise - Variable Classification Lab
Students classify variables across domains.
30 min
Lesson 4: Statistical QuestionsLearn how to rewrite vague business questions into descriptive, comparative, relationship, prediction, and causal statistical questions.85 minarticle4 pages
Welcome and Learning Objectives
Introduce statistical question design.
8 min
Types of Statistical Questions
Explain major question types.
24 min
From Vague to Statistical
Show how to rewrite questions.
22 min
Exercise - Statistical Question Rewrite Lab
Students rewrite vague business questions.
31 min
Lesson 5: Mini Project 1 - Statistical Question DesignStudents choose a dataset and design statistical questions with variable classification, expected analysis approach, risks, and bias notes.90 minarticle2 pages
Project Brief
Explain the project scenario and expected output.
20 min
Review Checklist
Checklist for project quality.
20 min
2Phase 2 - Descriptive Statistics and DistributionsSummarize data correctly, understand distribution shapes, identify outliers, and compare segments.2 modules9 lessons1–2 weeks
Module 1: Summarizing Data CorrectlyUse measures of center, spread, percentiles, quantiles, and distribution shape to summarize data responsibly.4 lessons
Lesson 1: Measures of CenterUse mean, median, and mode while understanding when each is useful or misleading.55 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
27 min
Practice Activity
Apply the lesson through a guided statistics exercise.
20 min
Lesson 2: Measures of SpreadUse range, variance, standard deviation, interquartile range, and coefficient of variation.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
30 min
Practice Activity
Apply the lesson through a guided statistics exercise.
22 min
Lesson 3: Percentiles and QuantilesUse percentiles, quartiles, box plots, and outlier detection.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
30 min
Practice Activity
Apply the lesson through a guided statistics exercise.
22 min
Lesson 4: Shape of DataInterpret skewness, symmetry, long tails, and multimodal distributions.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
30 min
Practice Activity
Apply the lesson through a guided statistics exercise.
22 min
Module 2: Working with DistributionsBuild and interpret common distributions used in data science and business decision-making.5 lessons
Lesson 1: Frequency DistributionsBuild frequency tables, histograms, density views, and distribution comparisons.55 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
27 min
Practice Activity
Apply the lesson through a guided statistics exercise.
20 min
Lesson 2: Normal DistributionUnderstand bell curves, standard deviation rule, z-scores, and why normality matters.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
30 min
Practice Activity
Apply the lesson through a guided statistics exercise.
22 min
Lesson 3: Common Data Science DistributionsUnderstand binomial, Poisson, exponential, and uniform distributions through business examples.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
32 min
Practice Activity
Apply the lesson through a guided statistics exercise.
25 min
Lesson 4: Transforming DataUse log transformation, scaling concepts, standardization, and why transformations help analysis and ML.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
32 min
Practice Activity
Apply the lesson through a guided statistics exercise.
25 min
Lesson 5: Milestone Project 1 - Descriptive Statistics ReportProduce a descriptive statistics report for a selected dataset.110 minarticle2 pages
Project Brief
Explain the project scenario and expected output.
20 min
Review Checklist
Checklist for project quality.
20 min
3Phase 3 - Probability for Data ScienceUse probability to reason about uncertainty, evidence, risk, expected value, and business tradeoffs.1 modules5 lessons1 week
Module 1: Probability FundamentalsUnderstand probability, conditional probability, Bayes intuition, expected value, and risk.5 lessons
Lesson 1: Understanding ProbabilityInterpret probability as uncertainty using events, outcomes, complements, and mutually exclusive events.55 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
27 min
Practice Activity
Apply the lesson through a guided statistics exercise.
20 min
Lesson 2: Conditional ProbabilityUse conditional probability, dependence, independence, and decision-making examples.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
30 min
Practice Activity
Apply the lesson through a guided statistics exercise.
22 min
Lesson 3: Bayes Rule IntuitionUnderstand prior probability, new evidence, updated belief, medical testing, and fraud detection examples.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
32 min
Practice Activity
Apply the lesson through a guided statistics exercise.
25 min
Lesson 4: Expected Value and RiskUse expected value, risk, decision-making under uncertainty, and business tradeoffs.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
32 min
Practice Activity
Apply the lesson through a guided statistics exercise.
25 min
Lesson 5: Mini Project 2 - Probability Decision CaseSolve a business decision problem using probability and expected value.90 minarticle2 pages
Project Brief
Explain the project scenario and expected output.
20 min
Review Checklist
Checklist for project quality.
20 min
4Phase 4 - Sampling, Estimation and ConfidenceUse sampling, sampling distributions, confidence intervals, and interpretation discipline for reliable estimation.1 modules5 lessons1 week
Module 1: Sampling and EstimationDesign sampling approaches, simulate sampling variability, build confidence intervals, and avoid common misinterpretations.5 lessons
Lesson 1: Sampling MethodsUse random sampling, stratified sampling, convenience sampling, sampling bias, and sample size intuition.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
30 min
Practice Activity
Apply the lesson through a guided statistics exercise.
22 min
Lesson 2: Sampling DistributionsUnderstand sample statistics, sampling variability, Central Limit Theorem intuition, and why larger samples help.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
35 min
Practice Activity
Apply the lesson through a guided statistics exercise.
27 min
Lesson 3: Confidence IntervalsBuild confidence intervals and interpret estimates, interval width, confidence level, and margin of error.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
35 min
Practice Activity
Apply the lesson through a guided statistics exercise.
27 min
Lesson 4: Common MisinterpretationsAvoid small sample traps, selection bias, survivorship bias, and wrong confidence interval claims.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
30 min
Practice Activity
Apply the lesson through a guided statistics exercise.
22 min
Lesson 5: Milestone Project 2 - Sampling and Confidence ReportProduce a sampling and confidence report for a business problem.100 minarticle2 pages
Project Brief
Explain the project scenario and expected output.
20 min
Review Checklist
Checklist for project quality.
20 min
5Phase 5 - Hypothesis TestingTest business claims using hypothesis testing, p-values, t-tests, proportion tests, chi-square tests, effect size, and business judgment.1 modules7 lessons1–2 weeks
Module 1: Testing Claims with DataWrite hypotheses, choose tests, interpret p-values properly, and turn statistical results into business recommendations.7 lessons
Lesson 1: Hypothesis Testing IntuitionUnderstand null hypothesis, alternative hypothesis, evidence, statistical significance, and practical significance.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
30 min
Practice Activity
Apply the lesson through a guided statistics exercise.
22 min
Lesson 2: P-values Explained ProperlyInterpret what p-values mean, what they do not mean, thresholds, and misuse of p-values.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
30 min
Practice Activity
Apply the lesson through a guided statistics exercise.
22 min
Lesson 3: One-Sample and Two-Sample TestsUse t-tests, comparing means, before/after analysis, independent vs paired samples.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
35 min
Practice Activity
Apply the lesson through a guided statistics exercise.
27 min
Lesson 4: Tests for ProportionsCompare conversion rates, success rates, and difference in proportions.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
35 min
Practice Activity
Apply the lesson through a guided statistics exercise.
27 min
Lesson 5: Chi-Square TestsAnalyze categorical relationships using independence testing and contingency tables.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
32 min
Practice Activity
Apply the lesson through a guided statistics exercise.
25 min
Lesson 6: Statistical vs Business DecisionsBalance significance, impact, effect size, confidence, cost of wrong decisions, and decision thresholds.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
32 min
Practice Activity
Apply the lesson through a guided statistics exercise.
25 min
Lesson 7: Milestone Project 3 - Business Hypothesis Testing ProjectTest several business claims and produce statistical interpretations and recommendations.120 minarticle2 pages
Project Brief
Explain the project scenario and expected output.
20 min
Review Checklist
Checklist for project quality.
20 min
6Phase 6 - Relationships, Correlation and Regression IntuitionUnderstand relationships, correlation, causation, regression intuition, confounding, and ML readiness.1 modules6 lessons1 week
Module 1: Understanding RelationshipsAnalyze relationships between variables and prepare for regression and machine learning concepts.6 lessons
Lesson 1: CorrelationUnderstand positive, negative, no correlation, Pearson, Spearman, and correlation limitations.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
30 min
Practice Activity
Apply the lesson through a guided statistics exercise.
22 min
Lesson 2: Correlation vs CausationUnderstand confounding variables, reverse causality, spurious correlations, and causal thinking.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
30 min
Practice Activity
Apply the lesson through a guided statistics exercise.
22 min
Lesson 3: Simple Linear Regression IntuitionUnderstand outcome variable, predictor variable, slope, intercept, residuals, and error.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
35 min
Practice Activity
Apply the lesson through a guided statistics exercise.
27 min
Lesson 4: Multiple Regression ConceptsUnderstand multiple predictors, control variables, coefficients, interpretation, and assumptions at a high level.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
35 min
Practice Activity
Apply the lesson through a guided statistics exercise.
27 min
Lesson 5: Preparing for Machine LearningUnderstand features, target variables, prediction vs explanation, training data, and evaluation intuition.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
32 min
Practice Activity
Apply the lesson through a guided statistics exercise.
25 min
Lesson 6: Mini Project 3 - Relationship Analysis ReportProduce a relationship analysis report with correlation, confounding discussion, regression interpretation, recommendation, and ML readiness note.100 minarticle2 pages
Project Brief
Explain the project scenario and expected output.
20 min
Review Checklist
Checklist for project quality.
20 min
7Phase 7 - Experiments and A/B TestingDesign, analyze, interpret, and audit experiments and A/B tests.1 modules6 lessons1 week
Module 1: Experiment DesignUnderstand observational data, experiments, A/B tests, sample size intuition, result interpretation, and experiment pitfalls.6 lessons
Lesson 1: Why Experiments MatterDifferentiate observational data, controlled experiments, random assignment, treatment, and control groups.55 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
27 min
Practice Activity
Apply the lesson through a guided statistics exercise.
20 min
Lesson 2: A/B Testing FundamentalsDesign A/B tests with control group, treatment group, primary metric, secondary metrics, and guardrail metrics.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
32 min
Practice Activity
Apply the lesson through a guided statistics exercise.
25 min
Lesson 3: Sample Size and Test DurationUnderstand minimum detectable effect, statistical power intuition, early stopping, and seasonality risks.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
32 min
Practice Activity
Apply the lesson through a guided statistics exercise.
25 min
Lesson 4: Interpreting Experiment ResultsInterpret lift, confidence intervals, statistical significance, business impact, and segment effects.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
35 min
Practice Activity
Apply the lesson through a guided statistics exercise.
27 min
Lesson 5: Experiment PitfallsAudit peeking, multiple comparisons, biased assignment, novelty effect, and misleading dashboards.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
30 min
Practice Activity
Apply the lesson through a guided statistics exercise.
22 min
Lesson 6: Milestone Project 4 - A/B Test AnalysisProduce an A/B test analysis with design, hypotheses, metrics, results, recommendation, and risks.110 minarticle2 pages
Project Brief
Explain the project scenario and expected output.
20 min
Review Checklist
Checklist for project quality.
20 min
8Phase 8 - Statistical Communication and Portfolio ReadinessCommunicate uncertainty, structure statistical reports, visualize findings, review quality, and complete the final capstone.2 modules6 lessons1–2 weeks
Module 1: Communicating Statistical FindingsExplain statistical findings clearly to non-technical stakeholders.4 lessons
Lesson 1: Explaining Uncertainty SimplyExplain uncertainty to non-technical stakeholders with confidence, without overclaiming or jargon.55 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
27 min
Practice Activity
Apply the lesson through a guided statistics exercise.
20 min
Lesson 2: Statistical ReportsStructure reports around problem, data, method, results, interpretation, recommendation, and limitations.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
30 min
Practice Activity
Apply the lesson through a guided statistics exercise.
22 min
Lesson 3: Visualizing Statistical FindingsUse distribution charts, box plots, error bars, confidence interval plots, and experiment result charts.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
32 min
Practice Activity
Apply the lesson through a guided statistics exercise.
25 min
Lesson 4: Quality Checklist for Statistical AnalysisReview data quality, assumptions, sample size, test choice, interpretation, and limitations.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical examples.
30 min
Practice Activity
Apply the lesson through a guided statistics exercise.
22 min
Module 2: Applied Statistics CapstoneComplete a final applied statistics project that supports a business decision with statistical evidence.2 lessons
Lesson 1: Final Capstone - Applied Statistics for Data Science CapstoneStudents choose a domain and support a business decision using statistical evidence.160 minarticle2 pages
Project Brief
Explain the project scenario and expected output.
20 min
Review Checklist
Checklist for project quality.
20 min
Lesson 2: Graduation Requirements and Portfolio OutcomeClarify completion requirements and portfolio outputs.45 minarticle1 pages
Requirements and Portfolio Checklist
Summarize graduation requirements and portfolio assets.
45 min
4Intermediate8 weeksPath onlyApplied Machine LearningApply machine learning to realistic datasets through feature engineering, model selection, evaluation, tuning, interpretation, and project presentation.10 phases22 modules104 lessons304 pages
1Phase 1 - Machine Learning ThinkingBuild applied ML judgment before coding: what ML is, how it works, problem types, when not to use ML, and problem framing.2 modules9 lessons1–2 weeks
Module 1: Introduction to Applied Machine LearningUnderstand machine learning as a practical pattern-learning workflow, not just scikit-learn model running.4 lessons
Lesson 1: What Is Machine Learning?Understand machine learning as a pattern-learning approach for prediction, classification, segmentation, and decision support.85 minarticle6 pages
Welcome and Learning Objectives
Introduce applied machine learning and its real purpose.
8 min
Machine Learning in Plain English
Explain ML without unnecessary theory.
18 min
ML vs Rules, Analytics and AI
Clarify common confusion.
22 min
Prediction vs Explanation
Teach the difference between predicting and explaining.
18 min
Model as Pattern Learner
Explain the idea of models learning from features and labels.
18 min
Exercise - Scenario Classification
Students classify business scenarios.
19 min
Lesson 2: How Machine Learning WorksUnderstand features, labels, models, training, prediction, evaluation, and generalization in the ML workflow.85 minarticle5 pages
Welcome and Learning Objectives
Introduce the ML workflow.
8 min
The Core ML Workflow
Explain the complete workflow.
22 min
Features, Labels and Predictions
Explain model inputs and outputs.
20 min
Generalization and Evaluation
Explain why testing on unseen data matters.
18 min
Exercise - ML Workflow Mapping
Students map ML workflows across use cases.
25 min
Lesson 3: Types of Machine LearningUnderstand supervised learning, unsupervised learning, regression, classification, clustering, forecasting, recommendation systems, and reinforcement learning at a high level.80 minarticle4 pages
Welcome and Learning Objectives
Introduce ML types.
8 min
Supervised vs Unsupervised Learning
Explain major categories.
20 min
Common ML Problem Types
Explain problem types.
22 min
Exercise - Problem Type Classifier
Students identify ML problem types.
30 min
Lesson 4: When Not to Use Machine LearningLearn when ML is inappropriate because rules, analytics, poor data, low value, cost, risk, or explainability constraints make a simpler approach better.80 minarticle4 pages
Welcome and Learning Objectives
Introduce ML appropriateness.
8 min
When Simpler Is Better
Explain alternatives to ML.
22 min
High-Risk ML Situations
Explain risks.
20 min
Exercise - ML Appropriateness Review
Students decide whether ML is appropriate.
30 min
Module 2: ML Problem FramingConvert business problems into clear ML problem statements with targets, features, timing, metrics, and risk notes.5 lessons
Lesson 1: Business Problem to ML ProblemConvert business objectives into ML objectives, target variables, prediction windows, data availability checks, and success metrics.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
30 min
Practice Activity
Apply the lesson through a guided exercise.
22 min
Lesson 2: Defining TargetsDefine target variables, labels, positive/negative classes, regression targets, and leakage risk in targets.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
32 min
Practice Activity
Apply the lesson through a guided exercise.
25 min
Lesson 3: Defining FeaturesDefine safe input features including numerical, categorical, date/time, text, historical, and leakage-prone features.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
32 min
Practice Activity
Apply the lesson through a guided exercise.
25 min
Lesson 4: ML Success MetricsChoose model and business metrics based on cost of wrong predictions, usefulness, and stakeholder expectations.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
30 min
Practice Activity
Apply the lesson through a guided exercise.
22 min
Lesson 5: Mini Project 1 - ML Problem Framing BriefProduce a professional ML problem framing brief for a selected business scenario.100 minarticle2 pages
Project Brief
Explain the project scenario and expected output.
20 min
Review Checklist
Checklist for project quality.
20 min
2Phase 2 - ML Data Preparation and Leakage PreventionPrepare ML datasets safely with quality review, splitting, leakage prevention, missing values, encoding, scaling, and scikit-learn workflows.2 modules11 lessons2 weeks
Module 1: Preparing ML DatasetsPrepare raw business data for modeling without leakage.6 lessons
Lesson 1: Data Quality Review for MLAudit missing values, duplicates, invalid categories, outliers, date issues, inconsistent IDs, and target quality.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
30 min
Practice Activity
Apply the lesson through a guided exercise.
22 min
Lesson 2: Train Validation and Test SplitsUnderstand training, validation, test, random, stratified, and time-based splits.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
35 min
Practice Activity
Apply the lesson through a guided exercise.
27 min
Lesson 3: Data LeakageIdentify future information, target-derived fields, post-outcome features, duplicate leakage, and aggregation leakage.75 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
37 min
Practice Activity
Apply the lesson through a guided exercise.
30 min
Lesson 4: Missing Value HandlingHandle missingness with dropping, imputation, missing indicators, business meaning, and pipeline-safe imputation.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
32 min
Practice Activity
Apply the lesson through a guided exercise.
25 min
Lesson 5: Categorical EncodingUse one-hot, ordinal, frequency, high-cardinality, rare category handling, and avoid encoding mistakes.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
32 min
Practice Activity
Apply the lesson through a guided exercise.
25 min
Lesson 6: Scaling Numerical FeaturesApply standardization, normalization, and understand when scaling matters.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
30 min
Practice Activity
Apply the lesson through a guided exercise.
22 min
Module 2: Scikit-Learn Workflow FoundationsBuild first models, preprocessing pipelines, reproducible notebooks, and baseline dataset packages.5 lessons
Lesson 1: First Scikit-Learn ModelUse estimators, fit, predict, train/test workflow, model objects, and prediction outputs.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
32 min
Practice Activity
Apply the lesson through a guided exercise.
25 min
Lesson 2: Preprocessing PipelinesUse pipelines, ColumnTransformer, numerical preprocessing, categorical preprocessing, and leakage prevention.75 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
37 min
Practice Activity
Apply the lesson through a guided exercise.
30 min
Lesson 3: ReproducibilityUse random_state, deterministic splits, environment setup, notebook hygiene, and experiment notes.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
30 min
Practice Activity
Apply the lesson through a guided exercise.
22 min
Lesson 4: Baseline Dataset PackagePackage clean dataset, feature list, target definition, split strategy, and preprocessing plan.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
32 min
Practice Activity
Apply the lesson through a guided exercise.
25 min
Lesson 5: Milestone Project 1 - ML-Ready Dataset PackagePrepare a messy dataset into a modeling-ready package.120 minarticle2 pages
Project Brief
Explain the project scenario and expected output.
20 min
Review Checklist
Checklist for project quality.
20 min
3Phase 3 - Baselines, Regression and ClassificationBuild baselines, regression models, and classification models with honest evaluation and business interpretation.3 modules15 lessons2 weeks
Module 1: Baseline ModelsBuild and explain simple baselines before training more complex models.3 lessons
Lesson 1: Why Baselines MatterUse simple, mean, median, majority class, and business-rule baselines.55 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
27 min
Practice Activity
Apply the lesson through a guided exercise.
20 min
Lesson 2: Baseline MetricsEvaluate regression baseline error, classification accuracy, majority class trap, business comparison, and useful-enough threshold.55 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
27 min
Practice Activity
Apply the lesson through a guided exercise.
20 min
Lesson 3: Baseline CommunicationExplain baselines to stakeholders and avoid fake performance claims.50 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
25 min
Practice Activity
Apply the lesson through a guided exercise.
17 min
Module 2: Regression ModelsTrain, evaluate, improve, and explain regression models.6 lessons
Lesson 1: Regression Problem FramingFrame continuous-value prediction problems such as sales, price, revenue, and demand.55 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
27 min
Practice Activity
Apply the lesson through a guided exercise.
20 min
Lesson 2: Linear RegressionTrain and interpret linear regression with coefficients, intercepts, residuals, and assumptions.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
35 min
Practice Activity
Apply the lesson through a guided exercise.
27 min
Lesson 3: Multiple Linear RegressionBuild multi-feature regression and interpret feature impact and multicollinearity intuition.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
35 min
Practice Activity
Apply the lesson through a guided exercise.
27 min
Lesson 4: Regression MetricsUse MAE, MSE, RMSE, R², MAPE where useful, and business interpretation of error.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
32 min
Practice Activity
Apply the lesson through a guided exercise.
25 min
Lesson 5: Regression ImprovementImprove regression using feature selection, transformations, outlier handling, cross-validation preview, and model comparison.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
35 min
Practice Activity
Apply the lesson through a guided exercise.
27 min
Lesson 6: Milestone Project 2 - Sales Price or Demand Prediction ModelBuild and evaluate a regression project.120 minarticle2 pages
Project Brief
Explain the project scenario and expected output.
20 min
Review Checklist
Checklist for project quality.
20 min
Module 3: Classification ModelsTrain and evaluate classification models for churn, risk, fraud, leads, and completion risk.6 lessons
Lesson 1: Classification Problem FramingFrame category prediction problems such as churn, fraud, loan approval, lead scoring, and completion risk.55 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
27 min
Practice Activity
Apply the lesson through a guided exercise.
20 min
Lesson 2: Logistic RegressionTrain and interpret logistic regression with probabilities, thresholds, and coefficients.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
35 min
Practice Activity
Apply the lesson through a guided exercise.
27 min
Lesson 3: Classification MetricsUse accuracy, precision, recall, F1, confusion matrix, ROC-AUC intuition, and cost of errors.75 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
37 min
Practice Activity
Apply the lesson through a guided exercise.
30 min
Lesson 4: Decision TreesUse decision tree splits, rules, interpretability, overfitting, and tree depth.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
32 min
Practice Activity
Apply the lesson through a guided exercise.
25 min
Lesson 5: Random ForestsUse ensembles, bagging, stability, feature importance, and tradeoffs.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
35 min
Practice Activity
Apply the lesson through a guided exercise.
27 min
Lesson 6: Milestone Project 3 - Churn Risk or Lead Classification ModelBuild and compare classification models with business interpretation.120 minarticle2 pages
Project Brief
Explain the project scenario and expected output.
20 min
Review Checklist
Checklist for project quality.
20 min
4Phase 4 - Model Evaluation, Validation and TuningEvaluate, validate, tune, and audit ML models using appropriate metrics and error analysis.2 modules10 lessons1–2 weeks
Module 1: Evaluating Models ProperlyUnderstand overfitting, cross-validation, validation strategy, metrics, and model QA.5 lessons
Lesson 1: Overfitting and UnderfittingUnderstand training/validation/test performance, bias, variance, and model complexity.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
30 min
Practice Activity
Apply the lesson through a guided exercise.
22 min
Lesson 2: Cross-ValidationUse k-fold, stratified folds, cross-validation scores, and metric variability.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
32 min
Practice Activity
Apply the lesson through a guided exercise.
25 min
Lesson 3: Validation StrategyChoose train/validation/test, cross-validation, time-based validation, group-based split, and avoid test misuse.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
32 min
Practice Activity
Apply the lesson through a guided exercise.
25 min
Lesson 4: Choosing the Right MetricChoose metrics based on business error cost and model purpose.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
32 min
Practice Activity
Apply the lesson through a guided exercise.
25 min
Lesson 5: Model Validation ChecklistCheck leakage, splits, metrics, baselines, error analysis, and stakeholder interpretation.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
30 min
Practice Activity
Apply the lesson through a guided exercise.
22 min
Module 2: Hyperparameter TuningTune models carefully and analyze errors after tuning.5 lessons
Lesson 1: What Hyperparameters AreDifferentiate parameters and hyperparameters, model complexity, tuning options, and blind tuning risks.55 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
27 min
Practice Activity
Apply the lesson through a guided exercise.
20 min
Lesson 2: Grid Search and Random SearchUse search spaces, cross-validation, scoring, computational cost, and practical tuning workflow.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
35 min
Practice Activity
Apply the lesson through a guided exercise.
27 min
Lesson 3: Avoiding Tuning MistakesAvoid test set misuse, validation overfitting, too many experiments, wrong metrics, and ignoring business cost.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
30 min
Practice Activity
Apply the lesson through a guided exercise.
22 min
Lesson 4: Error Analysis After TuningReview segment-level errors, false positives, false negatives, residuals, and business cost.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
32 min
Practice Activity
Apply the lesson through a guided exercise.
25 min
Lesson 5: Mini Project 2 - Model Evaluation AuditAudit model outputs and recommend whether the model is usable.90 minarticle2 pages
Project Brief
Explain the project scenario and expected output.
20 min
Review Checklist
Checklist for project quality.
20 min
5Phase 5 - Feature Engineering and Advanced Supervised LearningEngineer useful features and compare stronger supervised model families.2 modules10 lessons1–2 weeks
Module 1: Feature Engineering for Tabular DataCreate leakage-safe numerical, categorical, date/time, and aggregation features.5 lessons
Lesson 1: What Makes a Good FeatureEvaluate predictive signal, business meaning, leakage risk, stability, usefulness, and feature cost.55 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
27 min
Practice Activity
Apply the lesson through a guided exercise.
20 min
Lesson 2: Numerical Feature EngineeringCreate ratios, differences, bins, log transforms, outlier handling, and interaction features.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
32 min
Practice Activity
Apply the lesson through a guided exercise.
25 min
Lesson 3: Categorical Feature EngineeringEngineer categorical features with one-hot, ordinal, frequency, rare category handling, and high-cardinality strategies.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
32 min
Practice Activity
Apply the lesson through a guided exercise.
25 min
Lesson 4: Date and Time FeaturesCreate day, week, month, recency, tenure, seasonality, and time-since-last-event features.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
32 min
Practice Activity
Apply the lesson through a guided exercise.
25 min
Lesson 5: Aggregation FeaturesCreate customer-level, product-level, rolling, group-based, and leakage-safe aggregation features.75 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
37 min
Practice Activity
Apply the lesson through a guided exercise.
30 min
Module 2: Stronger Model FamiliesCompare regularized linear models, tree-based models, gradient boosting, and model selection strategies.5 lessons
Lesson 1: Regularized Linear ModelsUse Ridge, Lasso, Elastic Net, simplicity vs performance, and when linear models win.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
32 min
Practice Activity
Apply the lesson through a guided exercise.
25 min
Lesson 2: Tree-Based Models RevisitedCompare tree depth, random forests, Extra Trees, feature importance, and stability.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
32 min
Practice Activity
Apply the lesson through a guided exercise.
25 min
Lesson 3: Gradient BoostingUnderstand boosting, XGBoost/LightGBM/CatBoost concepts, performance, overfitting risk, and use cases.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
35 min
Practice Activity
Apply the lesson through a guided exercise.
27 min
Lesson 4: Model Selection StrategyBalance accuracy, interpretability, speed, performance, data size, maintenance, and business constraints.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
30 min
Practice Activity
Apply the lesson through a guided exercise.
22 min
Lesson 5: Milestone Project 4 - Feature Engineering and Model Comparison ProjectEngineer features and compare candidate model families.120 minarticle2 pages
Project Brief
Explain the project scenario and expected output.
20 min
Review Checklist
Checklist for project quality.
20 min
6Phase 6 - Imbalanced Data and Real-World ClassificationHandle rare-event classification and design real-world decision workflows.2 modules9 lessons1–2 weeks
Module 1: Imbalanced ClassificationDeal with fraud, churn, medical risk, rare events, metrics, resampling, class weights, and thresholds.4 lessons
Lesson 1: Why Imbalanced Data Is HardUnderstand fraud, churn, medical risk, rare events, and accuracy traps.55 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
27 min
Practice Activity
Apply the lesson through a guided exercise.
20 min
Lesson 2: Metrics for Imbalanced ProblemsUse precision, recall, F1, ROC-AUC, PR-AUC, confusion matrix, and cost matrix.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
32 min
Practice Activity
Apply the lesson through a guided exercise.
25 min
Lesson 3: Resampling and Class WeightsCompare oversampling, undersampling, SMOTE concept, class weights, and risks.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
35 min
Practice Activity
Apply the lesson through a guided exercise.
27 min
Lesson 4: Threshold TuningChoose decision thresholds based on precision/recall tradeoff and business cost.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
35 min
Practice Activity
Apply the lesson through a guided exercise.
27 min
Module 2: Real-World Classification WorkflowsDesign churn, fraud/risk, lead scoring, and education risk workflows responsibly.5 lessons
Lesson 1: Churn Prediction WorkflowDefine churn, observation window, prediction window, retention action, and evaluation.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
32 min
Practice Activity
Apply the lesson through a guided exercise.
25 min
Lesson 2: Fraud or Risk Detection WorkflowDesign rare event workflows with false positives, false negatives, human review, and escalation.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
32 min
Practice Activity
Apply the lesson through a guided exercise.
25 min
Lesson 3: Lead Scoring WorkflowPredict conversion, prioritize leads, rank leads, and map sales action.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
30 min
Practice Activity
Apply the lesson through a guided exercise.
22 min
Lesson 4: Education Risk Prediction WorkflowPredict completion risk with student support, intervention planning, and ethical concerns.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
32 min
Practice Activity
Apply the lesson through a guided exercise.
25 min
Lesson 5: Milestone Project 5 - Fraud Churn or Risk ModelBuild an imbalanced classification model with decision threshold and memo.120 minarticle2 pages
Project Brief
Explain the project scenario and expected output.
20 min
Review Checklist
Checklist for project quality.
20 min
7Phase 7 - Unsupervised Learning and Customer SegmentationUse clustering and segmentation for pattern discovery and business action.2 modules9 lessons1 week
Module 1: Clustering and Pattern DiscoveryLearn unsupervised learning, K-means, cluster evaluation, and dimensionality reduction intuition.4 lessons
Lesson 1: What Is Unsupervised LearningUnderstand no-label pattern discovery, segmentation, exploration, and limitations.55 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
27 min
Practice Activity
Apply the lesson through a guided exercise.
20 min
Lesson 2: K-Means ClusteringUse clusters, centroids, distance, number of clusters, and scaling importance.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
35 min
Practice Activity
Apply the lesson through a guided exercise.
27 min
Lesson 3: Evaluating ClustersUse inertia, elbow method, silhouette intuition, usefulness, and segment interpretation.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
32 min
Practice Activity
Apply the lesson through a guided exercise.
25 min
Lesson 4: Dimensionality Reduction IntroUnderstand PCA intuition, visualization, high-dimensional data, limitations, and when not to overuse PCA.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
30 min
Practice Activity
Apply the lesson through a guided exercise.
22 min
Module 2: Segmentation for Business ActionProfile segments, validate usefulness, and turn segments into strategy.5 lessons
Lesson 1: Customer Segmentation StrategyDesign behavior, value, engagement, lifecycle, and actionable segments.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
30 min
Practice Activity
Apply the lesson through a guided exercise.
22 min
Lesson 2: Segment ProfilingProfile segments by size, revenue behavior, engagement, risk, preferences, and actions.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
32 min
Practice Activity
Apply the lesson through a guided exercise.
25 min
Lesson 3: Segment ValidationAssess stability, business meaning, stakeholder review, actionability, and fake clusters.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
30 min
Practice Activity
Apply the lesson through a guided exercise.
22 min
Lesson 4: From Segments to StrategyTurn clusters into targeting, retention, recommendations, product improvements, and support prioritization.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
30 min
Practice Activity
Apply the lesson through a guided exercise.
22 min
Lesson 5: Milestone Project 6 - Customer Segmentation ProjectBuild a customer segmentation project with business recommendations.120 minarticle2 pages
Project Brief
Explain the project scenario and expected output.
20 min
Review Checklist
Checklist for project quality.
20 min
8Phase 8 - Forecasting, Text ML and RecommendationsBuild applied prototypes for forecasting, text classification, and recommender systems.3 modules15 lessons2 weeks
Module 1: Forecasting and Time-Series MLBuild time-aware forecasting datasets, baselines, models, and evaluation.5 lessons
Lesson 1: Forecasting vs RegressionUnderstand time order, trend, seasonality, lag features, future leakage, and backtesting.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
30 min
Practice Activity
Apply the lesson through a guided exercise.
22 min
Lesson 2: Time-Series Feature EngineeringCreate lag features, rolling averages, expanding windows, calendar features, and event features.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
35 min
Practice Activity
Apply the lesson through a guided exercise.
27 min
Lesson 3: Forecasting BaselinesBuild naive, moving average, seasonal naive, and explain why baselines matter.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
30 min
Practice Activity
Apply the lesson through a guided exercise.
22 min
Lesson 4: ML for ForecastingFrame forecasting as supervised learning with time-based splits and regression models.75 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
37 min
Practice Activity
Apply the lesson through a guided exercise.
30 min
Lesson 5: Forecast EvaluationUse MAE, RMSE, MAPE, forecast bias, backtesting, and business usefulness.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
32 min
Practice Activity
Apply the lesson through a guided exercise.
25 min
Module 2: Text ML and NLP BasicsBuild simple text cleaning, feature extraction, classification, and evaluation workflows.4 lessons
Lesson 1: Working with Text DataClean text with tokenization concept, stop words, n-grams, and label quality.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
30 min
Practice Activity
Apply the lesson through a guided exercise.
22 min
Lesson 2: Text Feature ExtractionUse bag of words, TF-IDF, sparse features, limitations, and feature size.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
32 min
Practice Activity
Apply the lesson through a guided exercise.
25 min
Lesson 3: Text ClassificationBuild sentiment, ticket, spam, or review categorization models.75 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
37 min
Practice Activity
Apply the lesson through a guided exercise.
30 min
Lesson 4: Evaluating Text ModelsUse precision/recall, confusion matrix, misclassified examples, ambiguous labels, and human review.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
32 min
Practice Activity
Apply the lesson through a guided exercise.
25 min
Module 3: Recommendation Systems FoundationsBuild popularity-based and content-based recommender prototypes and understand collaborative filtering concepts.6 lessons
Lesson 1: Why Recommendations MatterUnderstand product, content, course recommendations, personalization, and business value.55 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
27 min
Practice Activity
Apply the lesson through a guided exercise.
20 min
Lesson 2: Popularity-Based RecommendersBuild most popular, trending, top-rated, segment-based, and baseline recommenders.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
30 min
Practice Activity
Apply the lesson through a guided exercise.
22 min
Lesson 3: Content-Based RecommendationsUse item features, similarity, user preferences, matching logic, and cold start.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
35 min
Practice Activity
Apply the lesson through a guided exercise.
27 min
Lesson 4: Collaborative Filtering ConceptsExplain user-item interactions, similar users/items, matrix intuition, cold start, and limitations.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
30 min
Practice Activity
Apply the lesson through a guided exercise.
22 min
Lesson 5: Evaluating RecommendationsPlan relevance, diversity, coverage, click-through, conversion, feedback loops, and offline vs online evaluation.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
30 min
Practice Activity
Apply the lesson through a guided exercise.
22 min
Lesson 6: Mini Project 3 - Applied ML Specialization SprintComplete one specialization sprint: forecasting, text classification, or recommendation prototype.120 minarticle2 pages
Project Brief
Explain the project scenario and expected output.
20 min
Review Checklist
Checklist for project quality.
20 min
9Phase 9 - Interpretation, Responsible ML and Deployment HandoffInterpret models, package ML projects, write handoff documents, and review responsible ML concerns.3 modules13 lessons1–2 weeks
Module 1: Model Interpretation and ExplainabilityExplain model behavior, errors, risk, and stakeholder-ready results.4 lessons
Lesson 1: Feature ImportanceInterpret tree importance, coefficients, permutation importance, and limitations.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
30 min
Practice Activity
Apply the lesson through a guided exercise.
22 min
Lesson 2: Error AnalysisAnalyze bad performance, segment errors, outlier errors, false positives/negatives, and data gaps.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
35 min
Practice Activity
Apply the lesson through a guided exercise.
27 min
Lesson 3: Explainability BasicsUnderstand interpretability vs accuracy, SHAP/LIME high-level, local/global explanations, and when explainability matters.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
30 min
Practice Activity
Apply the lesson through a guided exercise.
22 min
Lesson 4: Communicating ML ResultsWrite business framing, performance summary, risks, limitations, recommended use, and what not to claim.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
32 min
Practice Activity
Apply the lesson through a guided exercise.
25 min
Module 2: ML Packaging and Deployment HandoffPackage models, artifacts, batch scoring, handoff documents, and monitoring notes.4 lessons
Lesson 1: Professional ML Project StructureStructure notebooks, src, data, models, reports, requirements, README, and reproducibility.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
30 min
Practice Activity
Apply the lesson through a guided exercise.
22 min
Lesson 2: Saving Models and ArtifactsSave models, encoders, scalers, feature lists, versions, and reproducibility artifacts.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
32 min
Practice Activity
Apply the lesson through a guided exercise.
25 min
Lesson 3: Batch Prediction WorkflowCreate batch scoring, input validation, output files, prediction reports, and monitoring concepts.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
35 min
Practice Activity
Apply the lesson through a guided exercise.
27 min
Lesson 4: Deployment HandoffDocument input/output contract, feature requirements, model limitations, monitoring notes, and retraining notes.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
32 min
Practice Activity
Apply the lesson through a guided exercise.
25 min
Module 3: Responsible Applied MLReview fairness, privacy, monitoring, and model cards.5 lessons
Lesson 1: Bias and Fairness ReviewAudit sensitive features, proxy variables, segment performance, fairness risks, and human oversight.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
32 min
Practice Activity
Apply the lesson through a guided exercise.
25 min
Lesson 2: Privacy and Data GovernanceReview personal data, consent, minimization, secure storage, and responsible prediction sharing.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
30 min
Practice Activity
Apply the lesson through a guided exercise.
22 min
Lesson 3: Model Monitoring BasicsDesign monitoring for data drift, concept drift, performance decay, monitoring metrics, and retraining triggers.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
30 min
Practice Activity
Apply the lesson through a guided exercise.
22 min
Lesson 4: Model CardsCreate model cards with intended use, training data, metrics, limitations, ethics, and monitoring recommendations.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
32 min
Practice Activity
Apply the lesson through a guided exercise.
25 min
Lesson 5: Milestone Project 7 - Model Explanation and Handoff PackagePackage model explanation, responsible-use review, handoff, and monitoring assets.130 minarticle2 pages
Project Brief
Explain the project scenario and expected output.
20 min
Review Checklist
Checklist for project quality.
20 min
10Phase 10 - CapstoneComplete an end-to-end applied machine learning capstone with professional packaging and presentation.1 modules3 lessons1–2 weeks
Module 1: Applied Machine Learning CapstoneStudents choose one serious ML project and complete it end-to-end.3 lessons
Lesson 1: Capstone OptionsChoose a serious applied machine learning capstone option.60 minarticle1 pages
Choose Your Applied ML Capstone
Review approved capstone options.
60 min
Lesson 2: Final Capstone - Applied Machine Learning CapstoneBuild a complete applied machine learning project from brief to model package and executive presentation.220 minarticle2 pages
Project Brief
Explain the project scenario and expected output.
20 min
Review Checklist
Checklist for project quality.
20 min
Lesson 3: Graduation Requirements and Portfolio OutcomeClarify completion requirements and expected portfolio outputs.55 minarticle1 pages
Requirements and Portfolio Checklist
Summarize graduation requirements and portfolio assets.
55 min
5Intermediate8 weeksPath onlyData Science StudioComplete end-to-end data science projects that combine problem framing, data cleaning, exploration, statistics, visualization, modeling, evaluation, storytelling, and presentation.6 phases10 modules42 lessons124 pages
1Phase 1 - Data Science Project DiscoveryTranslate business requests into scoped data science briefs with problem type, target/objective, metrics, assumptions, risks, and deliverables.1 modules5 lessons1 week
Module 1: From Business Problem to Data Science BriefUnderstand the decision, frame the problem, define success metrics, scope the project, and produce the first brief.5 lessons
Lesson 1: Understanding the DecisionLearn how to identify the decision a data science project should support before choosing analytics, rules, or machine learning.85 minarticle6 pages
Welcome and Learning Objectives
Introduce decision-first data science.
8 min
Decision-First Data Science
Explain why the decision comes before the model.
18 min
Prediction vs Explanation
Clarify a critical project framing difference.
20 min
Automation vs Decision Support
Explain the difference between automated decisions and human-supported decisions.
18 min
When Data Science Is Unnecessary
Explain when not to use ML or data science.
18 min
Exercise - Right Approach Review
Students decide whether analytics, rules, or ML is the right approach.
21 min
Lesson 2: Problem FramingConvert vague business requests into clear data science problem statements across regression, classification, clustering, forecasting, recommendation, and ranking workflows.85 minarticle4 pages
Welcome and Learning Objectives
Introduce data science problem framing.
8 min
Common Data Science Framings
Explain major framing options.
24 min
From Vague Request to Clear Statement
Show how to rewrite vague requests.
22 min
Exercise - Data Science Framing Lab
Students convert vague requests into clear data science problem statements.
31 min
Lesson 3: Defining Success MetricsDefine model metrics and business metrics for churn, fraud, demand, customer segmentation, and lead scoring projects.85 minarticle5 pages
Welcome and Learning Objectives
Introduce success metric design.
8 min
Model Metric vs Business Metric
Explain the difference.
20 min
Cost of Wrong Predictions
Teach false positive and false negative thinking.
22 min
Operational Usefulness and Stakeholder Acceptance
Connect metrics to workflow adoption.
18 min
Exercise - Success Metric Design Studio
Students define metrics for common data science projects.
37 min
Lesson 4: Data Science Project ScopeDefine scope, assumptions, constraints, risks, ethics, and delivery timeline for a data science project.80 minarticle4 pages
Welcome and Learning Objectives
Introduce data science project scope.
8 min
Scope, Assumptions and Constraints
Explain scope control.
20 min
Risks and Ethical Concerns
Explain responsible planning.
20 min
Exercise - One-Page Data Science Brief
Students write a data science project brief.
32 min
Lesson 5: Mini Project 1 - Data Science BriefStudents choose a project domain and produce a professional data science brief.100 minarticle2 pages
Project Brief
Explain the project scenario and expected output.
20 min
Review Checklist
Checklist for project quality.
20 min
2Phase 2 - Data Audit, EDA and Modeling ReadinessAudit data quality, validate target/features, perform modeling-focused EDA, and package modeling-ready datasets.2 modules9 lessons1–2 weeks
Module 1: Data Understanding and AuditReview datasets, quality, target/feature validity, leakage risk, and modeling-focused EDA.4 lessons
Lesson 1: Data InventoryDocument available datasets, tables/files, data sources, granularity, ownership, and refresh frequency.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Professional Workflow
Explain the concept through a professional data science workflow.
30 min
Practice Activity
Apply the lesson through a guided studio activity.
22 min
Lesson 2: Data Quality AuditAudit missing values, duplicates, invalid categories, date problems, outliers, broken relationships, and inconsistent labels.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Professional Workflow
Explain the concept through a professional data science workflow.
35 min
Practice Activity
Apply the lesson through a guided studio activity.
27 min
Lesson 3: Target and Feature ValidationValidate target definition, label quality, feature availability, leakage risks, and historical sufficiency.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Professional Workflow
Explain the concept through a professional data science workflow.
35 min
Practice Activity
Apply the lesson through a guided studio activity.
27 min
Lesson 4: Exploratory Data Analysis for ModelingUse distribution, segment, relationship, target imbalance, outlier, and time-pattern analysis to support modeling decisions.75 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Professional Workflow
Explain the concept through a professional data science workflow.
37 min
Practice Activity
Apply the lesson through a guided studio activity.
30 min
Module 2: Modeling-Ready Dataset PreparationClean, engineer, split, and package datasets for reproducible modeling.5 lessons
Lesson 1: Cleaning for ModelingApply missing data strategy, duplicate handling, type correction, category standardization, and outlier treatment.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Professional Workflow
Explain the concept through a professional data science workflow.
32 min
Practice Activity
Apply the lesson through a guided studio activity.
25 min
Lesson 2: Feature Engineering ReviewCreate numerical, categorical, date/time, aggregation, behavioral, and domain features.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Professional Workflow
Explain the concept through a professional data science workflow.
35 min
Practice Activity
Apply the lesson through a guided studio activity.
27 min
Lesson 3: Split StrategyChoose train/validation/test, time-based, stratified splits, leakage prevention, and reproducibility.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Professional Workflow
Explain the concept through a professional data science workflow.
32 min
Practice Activity
Apply the lesson through a guided studio activity.
25 min
Lesson 4: Baseline Dataset PackagePackage clean data, feature list, target definition, preprocessing plan, and dataset versioning concept.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Professional Workflow
Explain the concept through a professional data science workflow.
32 min
Practice Activity
Apply the lesson through a guided studio activity.
25 min
Lesson 5: Milestone Project 1 - Data Audit and Modeling Readiness ReportProduce a modeling readiness report before training final models.120 minarticle2 pages
Project Brief
Explain the project scenario and expected output.
20 min
Review Checklist
Checklist for project quality.
20 min
3Phase 3 - Modeling, Experimentation and EvaluationBuild baselines, candidate models, pipelines, experiment logs, evaluation reports, and model selection memos.2 modules9 lessons1–2 weeks
Module 1: Building Baselines and Candidate ModelsBuild credible baselines, select candidate models, create pipelines, and track experiments.4 lessons
Lesson 1: Baseline StrategyUse simple statistical baselines, business-rule baselines, majority class baselines, and naive forecasting baselines.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Professional Workflow
Explain the concept through a professional data science workflow.
30 min
Practice Activity
Apply the lesson through a guided studio activity.
22 min
Lesson 2: Candidate Model SelectionSelect models based on model families, interpretability, data size, business constraints, and maintenance concerns.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Professional Workflow
Explain the concept through a professional data science workflow.
32 min
Practice Activity
Apply the lesson through a guided studio activity.
25 min
Lesson 3: Model PipelinesBuild preprocessing, feature, and model pipelines while avoiding leakage and supporting reproducible experiments.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Professional Workflow
Explain the concept through a professional data science workflow.
35 min
Practice Activity
Apply the lesson through a guided studio activity.
27 min
Lesson 4: Experiment TrackingTrack runs, metrics, parameters, dataset versions, and model comparisons.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Professional Workflow
Explain the concept through a professional data science workflow.
32 min
Practice Activity
Apply the lesson through a guided studio activity.
25 min
Module 2: Evaluation and Error AnalysisChoose metrics, validate robustness, analyze errors, and decide the final model.5 lessons
Lesson 1: Choosing Evaluation MetricsChoose regression, classification, forecasting, clustering, and business-aligned metrics.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Professional Workflow
Explain the concept through a professional data science workflow.
32 min
Practice Activity
Apply the lesson through a guided studio activity.
25 min
Lesson 2: Cross-Validation and RobustnessUse cross-validation, stratified CV, time-series validation, metric variability, and robustness concerns.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Professional Workflow
Explain the concept through a professional data science workflow.
35 min
Practice Activity
Apply the lesson through a guided studio activity.
27 min
Lesson 3: Error AnalysisAnalyze false positives, false negatives, residuals, segment errors, edge cases, and outlier behavior.75 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Professional Workflow
Explain the concept through a professional data science workflow.
37 min
Practice Activity
Apply the lesson through a guided studio activity.
30 min
Lesson 4: Model Selection DecisionChoose a usable model based on performance, interpretability, cost, complexity, and deployment readiness.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Professional Workflow
Explain the concept through a professional data science workflow.
35 min
Practice Activity
Apply the lesson through a guided studio activity.
27 min
Lesson 5: Milestone Project 2 - Modeling and Evaluation ReportProduce the modeling and evaluation report for the studio project.130 minarticle2 pages
Project Brief
Explain the project scenario and expected output.
20 min
Review Checklist
Checklist for project quality.
20 min
4Phase 4 - Interpretation, Responsible Use and Business TranslationInterpret models, review risk, translate predictions into action, and prepare stakeholder-ready documentation.2 modules9 lessons1–2 weeks
Module 1: Model InterpretationExplain model behavior, segment performance, limitations, and responsible-use concerns.4 lessons
Lesson 1: Explaining Model BehaviorExplain feature importance, coefficients, tree-based explanations, local/global interpretation, and interpretation limits.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Professional Workflow
Explain the concept through a professional data science workflow.
32 min
Practice Activity
Apply the lesson through a guided studio activity.
25 min
Lesson 2: Segment-Level InsightsReview which groups perform differently, affected users, segment errors, and fairness concerns.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Professional Workflow
Explain the concept through a professional data science workflow.
35 min
Practice Activity
Apply the lesson through a guided studio activity.
27 min
Lesson 3: Risk and LimitationsWrite data limitations, model limitations, bias risk, drift risk, unstable features, and human oversight needs.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Professional Workflow
Explain the concept through a professional data science workflow.
32 min
Practice Activity
Apply the lesson through a guided studio activity.
25 min
Lesson 4: Responsible Data ScienceReview sensitive features, proxy variables, privacy, fairness, misuse of predictions, and ethical review.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Professional Workflow
Explain the concept through a professional data science workflow.
35 min
Practice Activity
Apply the lesson through a guided studio activity.
27 min
Module 2: From Model Output to Business ActionTurn predictions into operational action and communicate results responsibly.5 lessons
Lesson 1: Turning Predictions into DecisionsDesign score thresholds, action rules, recommended interventions, human review, and operational workflow.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Professional Workflow
Explain the concept through a professional data science workflow.
32 min
Practice Activity
Apply the lesson through a guided studio activity.
25 min
Lesson 2: Communicating ResultsWrite technical summaries, executive summaries, recommendation memos, visuals, and avoid overclaiming.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Professional Workflow
Explain the concept through a professional data science workflow.
35 min
Practice Activity
Apply the lesson through a guided studio activity.
27 min
Lesson 3: Model DocumentationDraft model cards, data cards, assumptions, training process, intended use, and limitations.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Professional Workflow
Explain the concept through a professional data science workflow.
32 min
Practice Activity
Apply the lesson through a guided studio activity.
25 min
Lesson 4: Stakeholder PresentationPrepare presentation structure, simple methodology explanation, objections, tradeoffs, and uncertainty.75 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Professional Workflow
Explain the concept through a professional data science workflow.
37 min
Practice Activity
Apply the lesson through a guided studio activity.
30 min
Lesson 5: Mini Project 2 - Model Interpretation and Business Action PackPackage interpretation, responsible-use, model card, and business action artifacts.110 minarticle2 pages
Project Brief
Explain the project scenario and expected output.
20 min
Review Checklist
Checklist for project quality.
20 min
5Phase 5 - Packaging, Portfolio and HandoffPackage the project for portfolio, reproducibility, handoff, case study writing, and interview readiness.2 modules7 lessons1 week
Module 1: Data Science Project PackagingRefactor projects into professional, reproducible, handoff-ready repositories.4 lessons
Lesson 1: Professional Repository StructureStructure README, notebooks, src, raw/processed data, reports, models, requirements, and reproducibility notes.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Professional Workflow
Explain the concept through a professional data science workflow.
30 min
Practice Activity
Apply the lesson through a guided studio activity.
22 min
Lesson 2: ReproducibilityUse environment files, random seeds, notebook order, data paths, rerunnable pipelines, and dependency notes.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Professional Workflow
Explain the concept through a professional data science workflow.
32 min
Practice Activity
Apply the lesson through a guided studio activity.
25 min
Lesson 3: Handoff for Deployment or Business UseWrite input/output contracts, batch scoring notes, dashboard handoff, API concepts, monitoring, and retraining notes.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Professional Workflow
Explain the concept through a professional data science workflow.
35 min
Practice Activity
Apply the lesson through a guided studio activity.
27 min
Lesson 4: Portfolio Case Study WritingWrite the problem, data, approach, results, recommendation, limitations, and reflection.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Professional Workflow
Explain the concept through a professional data science workflow.
35 min
Practice Activity
Apply the lesson through a guided studio activity.
27 min
Module 2: Interview and Career ReadinessPrepare to explain, defend, review, and improve the data science project in interviews.3 lessons
Lesson 1: Explaining Projects in InterviewsExplain problem framing, model choice, validation, improvement ideas, and business impact.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Professional Workflow
Explain the concept through a professional data science workflow.
32 min
Practice Activity
Apply the lesson through a guided studio activity.
25 min
Lesson 2: Data Science Case InterviewsSolve case prompts using framing, metrics, data needed, model options, evaluation, and risks.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Professional Workflow
Explain the concept through a professional data science workflow.
35 min
Practice Activity
Apply the lesson through a guided studio activity.
27 min
Lesson 3: Technical Review PreparationPrepare for code, notebook, metric, assumptions, and model limitations review.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Professional Workflow
Explain the concept through a professional data science workflow.
35 min
Practice Activity
Apply the lesson through a guided studio activity.
27 min
6Phase 6 - Final Capstone and GraduationComplete an end-to-end data science capstone and package final portfolio assets.1 modules3 lessons1–2 weeks
Module 1: End-to-End Data Science CapstoneStudents complete one serious project domain from brief to presentation.3 lessons
Lesson 1: Capstone OptionsChoose a serious data science project domain.55 minarticle1 pages
Choose Your Data Science Capstone
Review approved final capstone options.
55 min
Lesson 2: Final Capstone - End-to-End Data Science CapstoneStudents complete a professional data science capstone from brief to final presentation.180 minarticle2 pages
Project Brief
Explain the project scenario and expected output.
20 min
Review Checklist
Checklist for project quality.
20 min
Lesson 3: Graduation Requirements and Portfolio OutcomeClarify final completion requirements and portfolio assets.55 minarticle1 pages
Requirements and Portfolio Checklist
Summarize graduation requirements and portfolio outcomes.
55 min
Skills and tools
Tools are taught through projects, not isolated checklists.
Use Python for data cleaning, exploration, and analysis.Apply statistics to understand data, uncertainty, and patterns.Frame real-world problems as data science questions.Build and evaluate basic machine learning models.Prepare datasets for modeling workflows.Interpret model results and explain limitations.Communicate insights, model outcomes, and recommendations clearly.Build portfolio-ready data science projects.Prepare for junior data scientist and applied ML practitioner roles.
Projects and portfolio
Use Python for data cleaning, exploration, and analysis.
Use Python for data cleaning, exploration, and analysis.
Apply statistics to understand data, uncertainty, and patterns.
Apply statistics to understand data, uncertainty, and patterns.
Frame real-world problems as data science questions.
Frame real-world problems as data science questions.
Build and evaluate basic machine learning models.
Build and evaluate basic machine learning models.
Prepare datasets for modeling workflows.
Prepare datasets for modeling workflows.
Interpret model results and explain limitations.
Interpret model results and explain limitations.
Communicate insights, model outcomes, and recommendations clearly.
Communicate insights, model outcomes, and recommendations clearly.
Build portfolio-ready data science projects.
Build portfolio-ready data science projects.
Prepare for junior data scientist and applied ML practitioner roles.
Prepare for junior data scientist and applied ML practitioner roles.
Portfolio outcomes
Use Python for data cleaning, exploration, and analysis.Apply statistics to understand data, uncertainty, and patterns.Frame real-world problems as data science questions.Build and evaluate basic machine learning models.Prepare datasets for modeling workflows.Interpret model results and explain limitations.Communicate insights, model outcomes, and recommendations clearly.Build portfolio-ready data science projects.Prepare for junior data scientist and applied ML practitioner roles.
Mentorship
Self-paced learning with feedback options.
TechOga paths are structured for independent progress, with stronger feedback loops available through weekly live-session and premium one-on-one support.
Structured course access for learners who can move independently and want clear lessons, resources, exercises, and portfolio direction.
A stronger support model with weekly instructor-led live sessions, weekly exercises, instructor reviews, and accountability across a path.
Self-paced access plus premium one-on-one sessions/mentorship for learners who want deeper review, private guidance, career assets, and tailored accountability.
Self-Paced Only
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Pay once or split into fixed installments.
Upfront Payment
₦320,000due today
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Two-Part Installment
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Three-Part Installment
₦128,000due today
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Access starts after your first confirmed payment.
Self-Paced + Weekly Instructor-Led Live Sessions
₦420,000
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Upfront Payment
₦420,000due today
- ₦420,000 at enrollment
Two-Part Installment
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- ₦252,000 at enrollment
- ₦168,000 after 30 days
Three-Part Installment
₦168,000due today
- ₦168,000 at enrollment
- ₦126,000 after 30 days
- ₦126,000 after 60 days
Access starts after your first confirmed payment.
Self-Paced + Premium One-on-One Sessions/Mentorship
₦550,000
Pay once or split into fixed installments.
Upfront Payment
₦550,000due today
- ₦550,000 at enrollment
Two-Part Installment
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Three-Part Installment
₦220,000due today
- ₦220,000 at enrollment
- ₦165,000 after 30 days
- ₦165,000 after 60 days
Access starts after your first confirmed payment.
FAQ
Questions about this path.
Path-specific answers keep the enrolment decision practical.
Not necessarily, but the path assumes you are ready to work with data using Python and understand basic analytics thinking.
Start with structure
Build real data science skill beyond tutorials and model demos.
Learn how to clean data, reason statistically, train models, evaluate results, explain limitations, and build data science projects that show practical ability.