Data analyst path
The Data analyst learning path chains 17 courses: 87 lessons, 269 exercises and about 47 hours of content. It starts with “Programming Basics and Algorithms” and ends with “Simple ETL Pipelines”. This is the base order: your personal path is built from it and from your goal.
- 17 courses
- 87 lessons
- 269 exercises
- about 47 h
- Runs in: browser
- Programming Basics and Algorithms
- Coding with an AI Assistant (Prompts, Code Review)
- SQL Fundamentals (SELECT, Filters, Aggregates)
- Intermediate SQL (joins, subqueries)
- Descriptive Statistics
- Python Fundamentals
- Data Analysis with pandas and NumPy
- Data Cleaning and Preparation
- Visualization (Matplotlib, Seaborn, Plotly)
- Probability and Inferential Statistics
- Exploratory Data Analysis
- Business Metrics and KPIs
- Advanced SQL (CTEs, window functions)
- Data Storytelling and Presenting Results
- Automate with Python (Files, Excel, APIs)
- A/B Testing and Experimentation
- Simple ETL Pipelines
- Foundations
- SQL
- Statistics
- Python
- Data analysis
The 17 courses, in order
- Foundations2 courses
- SQL2 courses
- Statistics1 course
- Python4 courses
- 06Python FundamentalsLearn the basics of Python: variables, conditions, loops, lists, dictionaries, functions and error handling.
- 07Data Analysis with pandas and NumPyAnalyze data in Python: NumPy arrays, pandas DataFrames, filters, computed columns, groupby, pivot tables and joins.
- 08Data Cleaning and PreparationClean real-world data with pandas: diagnosis, missing values, types, duplicates, outliers and a reproducible pipeline.
- 09Visualization (Matplotlib, Seaborn, Plotly)Build clear charts in Python with Matplotlib, Seaborn and Plotly: choosing the right visual, formatting, facets and interactivity.
- Statistics1 course
- Data analysis2 courses
- 11Exploratory Data AnalysisExplore a dataset with pandas: profile, missing values, duplicates, distributions, relationships, anomalies, time and summary of findings.
- 12Business Metrics and KPIsDefine, calculate and interpret a company's KPIs in Python: sales, customers, trends, targets, margin and marketing profitability.
- SQL1 course
- Data analysis1 course
- Python1 course
- Statistics1 course
- Data analysis1 course
Optional courses
Suggested depending on your goal, on top of the base path.
- Getting Started with Power BI DesktopGet hands-on with Power BI Desktop: import a file, create your first visuals, filter and format a clear report.
- Power Query: Import and TransformPrepare your data with Power Query: cleaning, calculated columns, merges, unpivoting and best practices, using both the interface and the M language.
- DAX FundamentalsWrite your first DAX measures: aggregations, iterators, logic, CALCULATE, variables and table functions in Power BI.
- Designing Reports and DashboardsDesign clear, interactive Power BI reports: choosing visuals, page layout, navigation, dynamic formatting and accessibility.
- Excel: PivotTablesSummarize your data in a few clicks: structured tables, PivotTables (TCD), grouping, calculated fields, slicers and PivotCharts.
- Data Quality and GovernanceMeasure and improve data quality, write data contracts, document catalog and lineage, and apply GDPR principles.
- Marketing AnalyticsMeasure marketing performance in Python: UTMs and channels, attribution, conversion funnels, ROI, incrementality, and A/B tests.
- Financial Analysis and Management ControlManagement control with Python and Excel: income statement, budget and variances, cash flow, break-even point and dashboard.
- HR AnalyticsAnalyze HR data in Python while protecting people: headcount, turnover, absenteeism, recruitment and pay equity.
- Supply Chain and ForecastingManage inventory in Python: coverage, ABC, stockouts, OTIF, moving-average forecasting, safety stock and economic order quantity.
- E-commerce AnalyticsAnalyze an online store in Python: conversion, average order value, RPV, repurchase, cohorts, products bought together and RFM segmentation.
Other paths
Data scientist
- Programming Basics and Algorithms
- Terminal and Command Line
- Git and GitHub
- Coding with an AI Assistant (Prompts, Code Review)
- SQL Fundamentals (SELECT, Filters, Aggregates)
- Intermediate SQL (joins, subqueries)
- Advanced SQL (CTEs, window functions)
- Descriptive Statistics
- Probability and Inferential Statistics
- Python Fundamentals
- Intermediate Python (Functions, Modules, OOP)
- Data Analysis with pandas and NumPy
- Data Cleaning and Preparation
- Visualization (Matplotlib, Seaborn, Plotly)
- Exploratory Data Analysis
- Introduction to Machine Learning (scikit-learn)
- Supervised Learning: Regression and Classification
- Unsupervised Learning: Clustering and Dimensionality Reduction
- Model Evaluation and Validation
- Feature Engineering
- Time Series and Forecasting
- HTTP and REST APIs
- APIs with FastAPI
- Deep Learning with PyTorch
- Natural Language Processing (NLP) and LLMs
- Deploying a Model (API, MLOps Basics)
- Docker and Deployment
- Foundations
- SQL
- Statistics
- Python
- Data analysis
- Data science
- Back-end
- Python for the web
Power BI
- SQL Fundamentals (SELECT, Filters, Aggregates)
- Intermediate SQL (joins, subqueries)
- Database Modeling
- Descriptive Statistics
- Business Metrics and KPIs
- Getting Started with Power BI Desktop
- Power Query: Import and Transform
- Star Schema Modeling
- DAX Fundamentals
- Advanced DAX (Context, Time Intelligence)
- Data Storytelling and Presenting Results
- Designing Reports and Dashboards
- Power BI Service: Publishing, Sharing and RLS Security
- SQL
- Statistics
- Data analysis
- Power BI
JavaScript web developer
- Programming Basics and Algorithms
- Terminal and Command Line
- Git and GitHub
- Coding with an AI Assistant (Prompts, Code Review)
- HTML Fundamentals
- CSS Fundamentals
- Responsive Layout (Flexbox, Grid)
- JavaScript Fundamentals
- DOM and Events
- Asynchronous JavaScript (Promises, fetch, APIs)
- Accessibility and SEO
- Data Structures and Complexity
- TypeScript
- React
- SQL Fundamentals (SELECT, Filters, Aggregates)
- Intermediate SQL (joins, subqueries)
- Database Modeling
- HTTP and REST APIs
- Node.js Fundamentals
- API with Express
- Automated Testing
- Authentication and Web Security (OWASP)
- Full-Stack React + Node Application
- Docker and Deployment
- Foundations
- Front-end
- SQL
- Back-end
- Server-side JavaScript
Python web developer
- Programming Basics and Algorithms
- Terminal and Command Line
- Git and GitHub
- Coding with an AI Assistant (Prompts, Code Review)
- Python Fundamentals
- Intermediate Python (Functions, Modules, OOP)
- HTML Fundamentals
- CSS Fundamentals
- Responsive Layout (Flexbox, Grid)
- JavaScript Fundamentals
- DOM and Events
- Asynchronous JavaScript (Promises, fetch, APIs)
- SQL Fundamentals (SELECT, Filters, Aggregates)
- Intermediate SQL (joins, subqueries)
- Database Modeling
- HTTP and REST APIs
- APIs with FastAPI
- Django Fundamentals
- Automated Testing
- Authentication and Web Security (OWASP)
- Complete Application with Django
- Docker and Deployment
- Foundations
- Python
- Front-end
- SQL
- Back-end
- Python for the web
PHP web developer
- Programming Basics and Algorithms
- Terminal and Command Line
- Git and GitHub
- Coding with an AI Assistant (Prompts, Code Review)
- HTML Fundamentals
- CSS Fundamentals
- Responsive Layout (Flexbox, Grid)
- JavaScript Fundamentals
- DOM and Events
- Asynchronous JavaScript (Promises, fetch, APIs)
- SQL Fundamentals (SELECT, Filters, Aggregates)
- Intermediate SQL (joins, subqueries)
- Database Modeling
- PHP Fundamentals
- Object-Oriented PHP and Composer
- PHP and Databases (PDO)
- HTTP and REST APIs
- Laravel Fundamentals
- Automated Testing
- Authentication and Web Security (OWASP)
- Advanced Laravel (API, Queues, Tests)
- Docker and Deployment
- Foundations
- Front-end
- SQL
- PHP
- Back-end
Java web developer
- Programming Basics and Algorithms
- Terminal and Command Line
- Git and GitHub
- Coding with an AI Assistant (Prompts, Code Review)
- Data Structures and Complexity
- HTML Fundamentals
- CSS Fundamentals
- Responsive Layout (Flexbox, Grid)
- JavaScript Fundamentals
- DOM and Events
- Asynchronous JavaScript (Promises, fetch, APIs)
- SQL Fundamentals (SELECT, Filters, Aggregates)
- Intermediate SQL (joins, subqueries)
- Database Modeling
- Java Fundamentals
- Object-Oriented Java
- Collections, Streams, and Exceptions
- Testing with JUnit
- HTTP and REST APIs
- Spring Boot: REST API
- Persistence with JPA and Hibernate
- Authentication and Web Security (OWASP)
- Docker and Deployment
- Foundations
- Front-end
- SQL
- Java
- Back-end
Excel and data for the office
- Excel Fundamentals
- Excel: Lookup and Logical Functions
- Excel: PivotTables
- Build and Safeguard a Reliable Excel Workbook
- Using Generative AI at Work
- Power Query and Power Pivot in Excel
- Automating Excel (VBA and Office Scripts)
- Power Automate: Automate Your Tasks
- Excel
- Generative AI
- Power Platform
AI at work
- Using Generative AI at Work
- Cybersecurity: The Basics for Everyone
- GDPR for Data and Product Teams
- AI Governance and the AI Act
- Power Automate: Automate Your Tasks
- Generative AI
- Cross-functional skills
- Power Platform
AI developer
- Programming Basics and Algorithms
- Python Fundamentals
- Intermediate Python (Functions, Modules, OOP)
- Git and GitHub
- Using Generative AI at Work
- HTTP and REST APIs
- Integrating an LLM into an Application
- RAG in Production
- AI Agents and Automation
- Evaluating and Monitoring an AI System
- Docker and Deployment
- Self-Hosting AI Models Locally
- Foundations
- Python
- Generative AI
- Back-end
Data engineer
- Terminal and Command Line
- Git and GitHub
- SQL Fundamentals (SELECT, Filters, Aggregates)
- Intermediate SQL (joins, subqueries)
- Advanced SQL (CTEs, window functions)
- Database Modeling
- Python Fundamentals
- Intermediate Python (Functions, Modules, OOP)
- Data Analysis with pandas and NumPy
- Data Warehouses and Analytical Modeling
- Data Quality and Governance
- Transforming Data with dbt
- Orchestrating Pipelines with Airflow
- Distributed Processing with Spark
- PostgreSQL in Practice (indexes, performance)
- Docker and Deployment
- Cloud Fundamentals
- Deploying an Application to the Cloud
- Foundations
- SQL
- Python
- Data engineering
- Back-end
- Cloud and DevOps
A website for my business
- Build Your Website with WordPress
- Online Store with WooCommerce or Shopify
- Local SEO and Online Presence
- HTML Fundamentals
- Websites and CMS
- Front-end
Marketing analyst
- SQL Fundamentals (SELECT, Filters, Aggregates)
- Intermediate SQL (joins, subqueries)
- Descriptive Statistics
- Business Metrics and KPIs
- Marketing Analytics
- E-commerce Analytics
- Data Storytelling and Presenting Results
- A/B Testing and Experimentation
- Local SEO and Online Presence
- SQL
- Statistics
- Data analysis
- Business analytics
- Websites and CMS
Cloud and DevOps
- Terminal and Command Line
- Git and GitHub
- Linux for Developers
- Docker and Deployment
- Continuous Integration and Deployment
- Cloud Fundamentals
- Deploying an Application to the Cloud
- Kubernetes
- Infrastructure as Code with Terraform
- Cybersecurity: The Basics for Everyone
- Foundations
- Cloud and DevOps
- Back-end
- Cross-functional skills
Mobile developer
- Programming Basics and Algorithms
- Git and GitHub
- JavaScript Fundamentals
- DOM and Events
- Asynchronous JavaScript (Promises, fetch, APIs)
- TypeScript
- React
- HTTP and REST APIs
- Mobile Apps with React Native
- Mobile Apps with Flutter
- Foundations
- Front-end
- Back-end
- Mobile
Digital project manager
- Using Generative AI at Work
- Agile Project Management
- Business Analysis: From Needs to Specifications
- UX Design and Mockups
- GDPR for Data and Product Teams
- SQL Fundamentals (SELECT, Filters, Aggregates)
- Business Metrics and KPIs
- Generative AI
- Cross-functional skills
- SQL
- Data analysis
Say what you want to be able to do, we build your path
Your path is assembled from the catalogue courses. With the Pro plan, every lesson is told with examples from your job.