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School of EngineeringBackend EngineeringBeginner to Intermediate
Backend Software Engineering with Python & Django
Build secure APIs, databases, authentication systems, background jobs, and backend services that power real applications.
Learn how to build the server-side systems behind modern web, mobile, and desktop applications, including APIs, databases, authentication, permissions, testing, deployment, and production workflows.
24 weeks - 6-8 hours/week
Understand how backend systems power modern applications.
School of EngineeringBackend EngineeringBeginner to Intermediate
Backend Software Engineering with Python & Django
Build secure APIs, databases, authentication systems, background jobs, and backend services that power real applications.
Learn how to build the server-side systems behind modern web, mobile, and desktop applications, including APIs, databases, authentication, permissions, testing, deployment, and production workflows.
24 weeks - 6-8 hours/week
Understand how backend systems power modern applications.
If you understand learning through courses, use this view to see the full sequence behind each path. Some courses are available only inside the path because they depend on the projects, reviews, and outcomes around them.
School of Data & AIBeginner to Intermediate6 courses
Data Analytics
Become a practical data analyst who can clean, analyze, visualize, and communicate business data using Excel, SQL, Python, Power BI, and real-world analytics projects.
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.
Master the Excel skills used by data analysts to clean, organize, calculate, summarize, visualize, and report business data with confidence.
Clean and organize messy spreadsheet data.
Can be started alone, then compounded inside the full path.
Clean and organize messy spreadsheet data.Use essential Excel formulas for analysis and reporting.Apply lookup functions to connect and enrich datasets.Build pivot tables for fast business summaries.
Learn to connect, clean, model, measure, visualize, and present business data using Power BI.
Connect Power BI to different data sources.
Can be started alone, then compounded inside the full path.
Connect Power BI to different data sources.Clean and transform data using Power Query.Build effective data models and table relationships.Write DAX measures for business reporting.
Learn the SQL skills data analysts use to extract, filter, join, group, and analyze data from relational databases.
Understand tables, columns, rows, keys, and relationships.
Can be started alone, then compounded inside the full path.
Understand tables, columns, rows, keys, and relationships.Write SQL queries to retrieve business data.Filter, sort, and structure query results.Join data across multiple tables correctly.
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.
Apply Excel, SQL, Python, Power BI, and storytelling to complete end-to-end analytics projects for your portfolio.
Translate business problems into clear analytics questions.
Available through the path so the work stays connected to the full outcome.
Translate business problems into clear analytics questions.Plan an end-to-end analytics project.Choose the right tool for each stage of analysis.Clean, query, analyze, visualize, and present real datasets.
School of Data & AIBeginner to Intermediate8 courses
Data Engineering
Learn how to build the pipelines, data models, warehouses, orchestration workflows, and cloud data systems that power analytics, reporting, machine learning, and AI products.
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.
Learn the Python skills data engineers use to move, clean, transform, validate, and automate data across files, APIs, databases, and pipeline workflows.
Write Python scripts for data engineering tasks.
Available through the path so the work stays connected to the full outcome.
Write Python scripts for data engineering tasks.Read, write, and process CSV, JSON, Excel, and structured data files.Extract data from APIs and external sources.Clean, transform, and validate data with Python.
Learn the SQL skills data analysts use to extract, filter, join, group, and analyze data from relational databases.
Understand tables, columns, rows, keys, and relationships.
Can be started alone, then compounded inside the full path.
Understand tables, columns, rows, keys, and relationships.Write SQL queries to retrieve business data.Filter, sort, and structure query results.Join data across multiple tables correctly.
Learn how modern teams organize trusted business data for analytics, reporting, and decision-making.
Understand what a data warehouse is and why teams use it.
Available through the path so the work stays connected to the full outcome.
Understand what a data warehouse is and why teams use it.Explain the difference between operational databases and analytical warehouses.Understand warehouse layers such as staging, transformation, and reporting.Design fact and dimension tables.
Learn how to move data from source systems into databases, warehouses, and analytics layers using practical ETL and ELT pipeline workflows.
Understand ETL and ELT pipeline workflows.
Available through the path so the work stays connected to the full outcome.
Understand ETL and ELT pipeline workflows.Extract data from files, APIs, and databases.Load data into databases and warehouse-style systems.Transform raw data into clean, structured datasets.
Learn how to manage reliable data workflows using Airflow for orchestration and dbt for structured, tested, analytics-ready transformations.
Understand data orchestration and workflow scheduling.
Available through the path so the work stays connected to the full outcome.
Understand data orchestration and workflow scheduling.Build Airflow workflows with tasks and dependencies.Schedule and monitor pipeline runs.Handle retries, failures, and workflow visibility.
Learn how data engineering works in the cloud, including storage, compute, managed databases, warehouses, pipeline deployment, access control, monitoring, and cost-aware architecture.
Understand how data engineering works in cloud environments.
Available through the path so the work stays connected to the full outcome.
Understand how data engineering works in cloud environments.Use cloud storage concepts for raw and processed data.Understand managed databases and warehouse-style services.Choose basic compute options for data workloads.
Apply Python, SQL, data modeling, warehousing, pipelines, orchestration, dbt, and cloud workflows to build portfolio-ready data engineering projects.
Plan complete data engineering projects.
Available through the path so the work stays connected to the full outcome.
Plan complete data engineering projects.Extract data from files, APIs, databases, and source systems.Clean, validate, and transform raw datasets.Design data models for analytics and reporting.
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
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.
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.
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.
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.
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.
School of EngineeringBeginner to Intermediate4 courses
Full Stack Software Engineering
Learn how to build complete web applications with backend APIs, databases, authentication, frontend interfaces, deployment, and full-stack product delivery.
Target role
Full Stack Software Engineer, Web Developer, Frontend Software Engineer, Backend Software Engineer
Build the foundation for becoming a software engineer by learning how software works, how developers think, and how modern applications are planned, built, tested, and improved.
Understand how software applications work.
Available through the path so the work stays connected to the full outcome.
Understand how software applications work.Understand the difference between frontend, backend, full-stack, mobile, desktop, cloud, and DevOps roles.Set up a professional development environment.Use the command line for basic developer workflows.
Learn how to build the server-side systems behind modern web, mobile, and desktop applications, including APIs, databases, authentication, permissions, testing, deployment, and production workflows.
Understand how backend systems power modern applications.
Can be started alone, then compounded inside the full path.
Understand how backend systems power modern applications.Build APIs that frontend and mobile apps can consume.Design and work with relational databases.Create user accounts, authentication, and authorization workflows.
Frontend Software Engineering with React, Next.js & TypeScript
Learn how to build beautiful, responsive, interactive web applications using React, Next.js, TypeScript, APIs, forms, authentication flows, and production-ready frontend workflows.
Understand how modern frontend applications work.
Available through the path so the work stays connected to the full outcome.
Understand how modern frontend applications work.Build responsive web pages with clean structure and styling.Create reusable React components.Use props, state, hooks, and component composition effectively.
Bring backend and frontend skills together to build complete, portfolio-ready web applications from idea to deployment.
Plan and build complete full-stack web applications.
Available through the path so the work stays connected to the full outcome.
Plan and build complete full-stack web applications.Translate product requirements into technical tasks.Design backend APIs and database structures.Build frontend screens connected to backend services.