Recommended Books for Data Analysts¶
Curated reading list — prioritised by impact and relevance to this course.
Must-Read (Tier 1)¶
Data Analysis and Thinking¶
Storytelling with Data — Cole Nussbaumer Knaflic The single best book on data visualisation for business analysts. No code. Pure design thinking. Read before touching Power BI or Tableau. Teaches you to remove chart junk and communicate with clarity.
Naked Statistics — Charles Wheelan Statistics explained without calculus. Perfect for analysts who need to understand hypothesis testing, regression, and probability without drowning in maths. Covers the concepts from Week 01 Day 05 with real-world stories.
Thinking, Fast and Slow — Daniel Kahneman Not a data book — but essential. Understanding cognitive bias is what separates analysts who get ignored from analysts who change decisions. Explains why stakeholders resist data that contradicts their instincts.
SQL and Databases¶
Learning SQL — Alan Beaulieu (O'Reilly) The most accessible SQL book for beginners. Covers everything in Week 01 Day 02 and more. Free online via many library systems.
SQL Cookbook — Anthony Molinaro (O'Reilly) Problem-solution format. Pick a problem you have, find the query. Excellent for intermediate analysts who know basics but want to see real patterns. Window functions, pivoting, date arithmetic — all covered.
Python and Pandas¶
Python for Data Analysis — Wes McKinney (O'Reilly) Written by the creator of Pandas. The definitive reference. Denser than this course but authoritative. Read alongside Week 01 Day 03.
Automate the Boring Stuff with Python — Al Sweigart (free online) Practical Python without data science. Covers file handling, web scraping, spreadsheet automation — directly useful for analyst automation tasks. Free at automatetheboringstuff.com.
Highly Recommended (Tier 2)¶
The Lean Startup — Eric Ries How A/B testing and data-driven decision making works in a product company. Week 01 Day 05 statistics in a business context.
How Charts Lie — Alberto Cairo Every misleading chart pattern you'll encounter in the wild. Teaches you to spot when data is being used to deceive — and ensures you don't accidentally do the same.
Data Science for Business — Foster Provost & Tom Fawcett (O'Reilly) The bridge between business problems and analytical solutions. Covers classification, regression, clustering — conceptually. Good preparation for Week 02 Day 04 (ML for Analysts).
Competing on Analytics — Davenport & Harris Case studies of how companies like Amazon, UPS, and Capital One built data-driven competitive advantages. Helps you understand where analytics creates business value.
Reference Books (Use When Needed)¶
Python Crash Course — Eric Matthes When Python fundamentals feel shaky. Beginner-friendly, project-based.
Excel 2021 Bible — Michael Alexander Comprehensive Excel reference. Jump to specific functions when you need them.
The Wall Street Journal Guide to Information Graphics — Dona Wong 30-page visual reference card for chart selection. Laminate it.
Online Reading (Free)¶
- Towards Data Science (Medium publication) — practical analytics articles
- Mode Analytics Blog — SQL tutorials and case studies
- Causal Inference: The Mixtape — free online textbook for advanced statistics
- Interpretable Machine Learning (Christoph Molnar) — free online, covers ML concepts for Week 02