Log in to access more pages.
Create an account or log in to continue reading more pages.
Log in
Table of contents
The Data Intelligence Path
From data fundamentals to data science, machine learning, and production data engineering
Read each section in order. Every title can be opened as a TheoryTrace document.
- Cover1
- Copyright2
- How to read this book3
- Introduction4
- Chapter 1: Thinking in Data5
- Chapter 2: Data Types, Structures, and Measurement6
- Chapter 3: Computing Foundations for Data Work7
- Chapter 4: Programming for Data with Python8
- Chapter 5: Data Wrangling and Exploratory Analysis9
- Chapter 6: Databases and SQL10
- Chapter 7: Probability for Data Science11
- Chapter 8: Statistics and Inference12
- Chapter 9: Linear Algebra and Optimization for Models13
- Chapter 10: Data Visualization and Communication14
- Chapter 11: The Data Science Workflow15
- Chapter 12: Supervised Machine Learning16
- Chapter 13: Feature Engineering and Model Improvement17
- Chapter 14: Tree Ensembles and Practical Predictive Modeling18
- Chapter 15: Unsupervised Learning and Representation19
- Chapter 16: Time Series, Forecasting, and Sequential Data20
- Chapter 17: Deep Learning Foundations21
- Chapter 18: Natural Language, Documents, and Modern AI Data22
- Chapter 19: Causal Thinking and Experimentation23
- Chapter 20: Data Engineering Foundations24
- Chapter 21: Data Warehouses, Data Lakes, and Lakehouses25
- Chapter 22: Building Reliable Data Pipelines26
- Chapter 23: Production Machine Learning and MLOps27
- Chapter 24: Ethics, Privacy, Security, and Data Leadership28
- Conclusion29