Tutorials & Practice¶
Free, high quality¶
- Kaggle Learn — short, hands-on tracks (Pandas, SQL, ML, Visualization)
- StatQuest YouTube — best stats explanations on the internet
- 3Blue1Brown — visual math (linear algebra, probability)
- Seeing Theory — interactive probability/stats
- Khan Academy — Statistics
- Mode SQL Tutorial — practical SQL with business cases
- SQLZoo — SQL exercises
- Computerphile — computing concepts explained
Certifications¶
- Google Data Analytics Certificate (Coursera) — beginner, free with audit
- Google Advanced Data Analytics Certificate — intermediate; Python, ML
- IBM Data Analyst Professional
- Tableau Desktop Specialist
- Microsoft DA Associate (PL-300)
- AWS Data Analytics Specialty
- GCP Professional Data Engineer
Topical learning¶
Python¶
SQL¶
- PostgreSQL Tutorial
- Use The Index, Luke — SQL performance
Visualization¶
- Tableau Public training
- From Data to Viz — chart selection
- Storytelling with Data podcast
Statistics¶
Communication¶
Blockchain / Web3¶
- Crypto Data Analyst Roadmap (roadmap.sh)
- Basic Wizard Guide to Dune SQL — SQL for Ethereum data analytics
- Understanding Transactions, Traces, and Logs — reading block explorers and onchain data structures
Practice platforms¶
- Stratascratch — interview-style problems
- LeetCode — SQL
- HackerRank — SQL
- DataLemur — analytics interview SQL
- Kaggle Competitions
- DataCamp Projects
- SQL Noir — SQL puzzle games
- SQL Zoo — SQL basics practice
- Neetcode — tech interview prep
Portfolio Evaluation Checklist¶
- [ ] Anything missing? Steps in projects? Details in descriptions?
- [ ] If you have a website, are all pages accounted for?
- [ ] If hosted on a platform, are projects uploaded properly?
- [ ] Is there too much info?
- [ ] Could descriptions be revised for brevity?
- [ ] Places where you include more data than needed? Could anything be cut without losing meaning?
- [ ] Anything you shouldn't include?
- [ ] Have you included references to others' work without citing them? Replace with links.
- [ ] Anything seem extraneous or unprofessional?
- [ ] Is portfolio hosted on the most appropriate platform?
- [ ] Considered options: GitHub, Kaggle, Tableau Public, personal site
Case Study Structure¶
What to include in a case study:
- Introduction — purpose, scenario, real-world relevance (optional: assumptions/theories)
- Problems — major problems identified, how you analyzed, supporting facts
- Solutions — outline solution + alternatives, pros/cons each
- Conclusion — key takeaways, what you learned
- Next steps — chosen solution and recommendation; explain why; specify what/who/when
Optional: AI Implementation if relevant.
Interview Questions¶
Common analyst interview questions:
- What is your process for cleaning data?
- What tools do you use for creating data visualizations?
- How and why do data visualizations enhance the stories data tells?
- What considerations are top of mind when sharing data stories with non-technical stakeholders?
- Walk me through a project you're proud of
- How do you decide which metric to track?
- Describe a time you found an unexpected insight
- How do you handle conflicting requests from stakeholders?
- What's the difference between correlation and causation?
- Explain p-value to a non-technical PM
Build a portfolio in 4 projects¶
A solid analyst portfolio needs breadth more than depth:
- One messy real-world dataset — show cleaning skill (e.g., NYC 311 complaints)
- One business analysis — frame a question, answer with data, give recommendation (e.g., Bellabeat case study)
- One dashboard — Tableau Public or Looker Studio (anyone can click around)
- One end-to-end project — SQL data pull → Python analysis → visualization → write-up
Host on:
- GitHub — code and notebooks
- Personal site (this kind of static site works) — narrative
- Tableau Public — dashboards
- Kaggle — notebooks with built-in audience