Write Python code for data analysis tasks.
School of Data & AIData AnalyticsBeginner to Intermediate
Python for Data Analytics
Clean, explore, analyze, automate, and visualize data with Python.
Learn Python for real analytics work, including notebooks, pandas, NumPy, data cleaning, transformation, exploratory analysis, automation, and visual summaries using practical datasets.
Duration
8 weeks - 6-8 hours/week
Project
Write Python code for data analysis tasks.
Support
Mentorship and review options available
Overview
A practical Short Course built around a visible project.
Learn Python for real analytics work: data cleaning, exploration, transformation, automation, and visual insight generation.
Use notebooks for structured exploratory analysis.
Import CSV, Excel, and structured data files.
Clean missing, duplicated, inconsistent, and messy data.
Use pandas to filter, group, merge, reshape, and transform datasets.
Perform exploratory data analysis with Python.
Create visual summaries and basic charts.
Automate repetitive data preparation tasks.
Analyze business datasets using repeatable Python workflows.
Build portfolio-ready Python analytics projects.
Course roadmap
What you will work through.
The sequence below is specific to this course. It shows the phases, modules, lessons, and page outlines that move you toward Write Python code for data analysis tasks..
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
Tools and skills
Build skill with the tools used in the work.
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.Use pandas to filter, group, merge, reshape, and transform datasets.Perform exploratory data analysis with Python.Create visual summaries and basic charts.Automate repetitive data preparation tasks.Analyze business datasets using repeatable Python workflows.Build portfolio-ready Python analytics projects.
Projects and exercises
- Write Python code for data analysis tasks.
- Structured exercises
- Portfolio practice
Resources included
- Course resources
- Project guidance
- Learners building practical tech skills
- A willingness to practice consistently
Career relevance
Python for Data Analytics supports practical career readiness.
Support options
Choose the level of feedback that matches your pace.
Compare the project, price, and feedback level before choosing the support option that fits your pace.
Self-Paced Only
₦85,000
Upfront Payment
₦85,000due today
- ₦85,000 at enrollment
Pay once or split into fixed installments.
Access starts after your first confirmed payment.
Related Professional Diploma
View Professional DiplomaData 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.
FAQ
Questions about this Short Course.
Short Course answers about scope, projects, support, and next steps.
No. The course starts with Python basics for data work before moving into pandas, data cleaning, and analysis workflows.
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Move beyond manual analysis by learning how to clean, explore, transform, automate, and visualize data with Python workflows used by modern analysts.