How to Learn Data Analytics

A structured path through Data Analytics — from first principles to confident mastery. Check off each milestone as you go.

Data Analytics Learning Roadmap

Click on a step to track your progress. Progress saved locally on this device.

Estimated: 38 weeks

Foundations of Data and Statistics

3-4 weeks

Build a solid grounding in basic statistics (mean, median, standard deviation, distributions, probability), data types, and the overall analytics lifecycle. Understand the difference between descriptive, diagnostic, predictive, and prescriptive analytics.

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Khan Academy Statistics and Probability course(free)'Naked Statistics' by Charles Wheelan(free)Google Data Analytics Professional Certificate (Coursera) - Module 1(free)

Spreadsheets and Data Manipulation

2-3 weeks

Develop proficiency with spreadsheet tools like Excel or Google Sheets for data cleaning, pivot tables, VLOOKUP, conditional formatting, and basic charting. These skills form the everyday toolkit for many analysts.

Excel Skills for Business Specialization (Coursera)(free)Google Sheets official documentation and tutorials(free)Chandoo.org Excel tutorials(free)

SQL for Data Analysis

4-5 weeks

Learn SQL to query relational databases, including SELECT, JOIN, GROUP BY, HAVING, subqueries, window functions, and CTEs. SQL is the lingua franca of data analytics and essential for interacting with data warehouses.

Mode Analytics SQL Tutorial(free)SQLZoo interactive exercises(free)'Learning SQL' by Alan Beaulieu (O'Reilly)(free)

Data Visualization and Storytelling

3-4 weeks

Master principles of effective data visualization and learn tools such as Tableau, Power BI, or Looker. Practice building dashboards that tell a clear story and communicate insights to non-technical stakeholders.

'Storytelling with Data' by Cole Nussbaumer Knaflic(free)Tableau Public free training videos(free)Power BI Guided Learning (Microsoft)(free)

Python or R for Analytics

5-6 weeks

Pick up a programming language for data manipulation and analysis. Learn Python (pandas, NumPy, matplotlib, seaborn) or R (dplyr, ggplot2, tidyr). Practice cleaning real-world datasets, performing exploratory data analysis, and automating repetitive tasks.

'Python for Data Analysis' by Wes McKinney(free)DataCamp Python or R tracks(free)Kaggle Learn micro-courses(free)

Statistical Modeling and Hypothesis Testing

4-5 weeks

Deepen your statistical knowledge with regression analysis, hypothesis testing, confidence intervals, A/B testing methodology, and experimental design. Learn to distinguish correlation from causation and evaluate model performance.

'Introduction to Statistical Learning' by James, Witten, Hastie, and Tibshirani(free)Statistics with Python Specialization (Coursera - University of Michigan)(free)Seeing Theory - Brown University interactive statistics(free)

Data Engineering Fundamentals and Cloud Tools

4-5 weeks

Understand data pipelines, ETL/ELT processes, data warehousing concepts (star schema, slowly changing dimensions), and cloud platforms like BigQuery, Snowflake, or Redshift. Learn the basics of data governance and quality assurance.

Google Cloud BigQuery documentation and sandbox(free)Snowflake free trial and hands-on labs(free)'Fundamentals of Data Engineering' by Joe Reis and Matt Housley(free)

Portfolio Projects and Real-World Practice

4-6 weeks (ongoing)

Apply everything you have learned by completing end-to-end analytics projects. Source public datasets, define business questions, clean and analyze data, build visualizations, and present findings. Build a portfolio on GitHub or a personal blog to demonstrate your skills to employers.

Kaggle Datasets and Competitions(free)Google Dataset Search(free)Maven Analytics free data playground(free)

Explore your way

Choose a different way to engage with this topic — no grading, just richer thinking.

Explore your way — choose one:

Explore with AI →

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