Data analysis and engineering

Time

27–36 weeks

Before this

Foundations of computer science

Leads to

Data analyst, Analytics engineer, Data engineer

People who like asking questions and finding the answer in numbers.

1. Stage 1: SQL in depth , 4–6 weeks

   What to learn

   • Filtering and sorting
   • Joins
   • Grouping and aggregates
   • Subqueries
   • Window functions

   Free resources

   • SQLBolt Practice, SQLBolt
   • Select Star SQL Book, Zi Chong Kao
   • Intro to SQL Practice, Kaggle

   Build: Answer 15 real questions about a public dataset using only SQL.

   Move on when: You can answer most business questions with one well-written query.

2. Stage 2: Statistics , 6–8 weeks

   What to learn

   • Averages, spread and distributions
   • Sampling
   • Confidence intervals
   • Hypothesis tests and A/B tests
   • Correlation is not causation

   Free resources

   • OpenIntro Statistics Book, OpenIntro (non-profit)
   • Statistics and probability Course, Khan Academy

   Build: Analyse an A/B test dataset and write the conclusion in plain language for a manager.

   Move on when: You can say how sure you are of a result, not just what the result is.

3. Stage 3: Analysis with Python or R , 6–8 weeks

   What to learn

   • Loading and cleaning data
   • Tidy data
   • Reshaping and joining tables
   • Notebooks and reports
   • Reproducible work

   Free resources

   • Python for Data Analysis Book, Wes McKinney
   • R for Data Science Book, Hadley Wickham, Mine Çetinkaya-Rundel and Garrett Grolemund

   Build: A full report that starts from messy raw data and anyone can re-run.

   Move on when: You can go from a raw file to a clear answer in an afternoon.

4. Stage 4: Visualisation and storytelling , 3–4 weeks

   What to learn

   • Choosing the right chart
   • Honest axes and scales
   • Colour and accessibility
   • Dashboards
   • Writing a clear summary

   Free resources

   • Fundamentals of Data Visualization Book, Claus O. Wilke

   Build: A dashboard or one-page report that a non-technical person understands without your help.

   Move on when: Your charts answer a question at a glance.

5. Stage 5: Data engineering , 8–10 weeks

   What to learn

   • ETL and ELT pipelines
   • Data warehouses
   • Scheduling and orchestration
   • Transformations with dbt
   • Big data with Spark

   Free resources

   • Data Engineering Zoomcamp Course, DataTalks.Club
   • dbt Learn Course, dbt Labs
   • Spark quick start Docs, Apache Software Foundation

   Build: A scheduled pipeline that pulls data from an API, stores it in a warehouse and feeds your dashboard.

   Move on when: Your pipeline runs on its own and tells you when it fails.

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