Books¶

Foundational¶

• DAMA-DMBOK 2: Data Management Body of Knowledge — the canonical reference for data management practices. Heavy but authoritative.
• Python for Data Analysis (Wes McKinney) — by the creator of pandas. Free online.
• Storytelling with Data (Cole Knaflic) — the book on data communication.

Statistics¶

• OpenIntro Statistics — free, rigorous, accessible.
• Introduction to Statistical Learning (ISLR) — free; ML/stats with R or Python labs.
• The Elements of Statistical Learning — free, advanced; classic.

Visualization¶

• Edward Tufte — The Visual Display of Quantitative Information — design canon.
• Edward Tufte — Envisioning Information
• Information Dashboard Design (Stephen Few)
• The Functional Art (Alberto Cairo)

SQL & databases¶

• Designing Data-Intensive Applications (Martin Kleppmann) — modern data systems.
• SQL Antipatterns (Bill Karwin) — what not to do.
• The Data Warehouse Toolkit (Kimball) — dimensional modeling classic.

Business intelligence & analytics¶

• Lean Analytics (Croll & Yoskovitz) — startup metrics.
• Competing on Analytics (Davenport & Harris)
• How to Measure Anything (Douglas Hubbard)

Machine learning¶

• Hands-On ML with Scikit-Learn, Keras & TensorFlow (Géron)
• Pattern Recognition and Machine Learning (Bishop)
• Probabilistic Machine Learning (Murphy) — free.

Communication & soft skills¶

• How to Win Friends and Influence People (Carnegie)
• Made to Stick (Heath brothers)

R-specific¶

• R for Data Science (Wickham & Grolemund) — free.
• Tidy Data (Wickham, paper)

Reading order suggestion¶

For a brand-new analyst:

1. Storytelling with Data (Knaflic)
2. Python for Data Analysis (McKinney)
3. OpenIntro Statistics
4. Lean Analytics (or domain-specific equivalent)
5. Designing Data-Intensive Applications (when you need scale)