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The Chicago Guide to Data Science

Part I: Exploring Data

1. What is Data Science?
2. Data Science Case Study
3. Programming in Python
4. Collections of Data
5. Randomness and Control Statements
6. DataFrames
7. DataFrame Methods and Operations
8. Data Visualization
9. Data Collection
10. Probability
11. Empirical and Probability Distributions
12. Hypothesis Testing
13. Estimation and Confidence Intervals
14. Ethics and Pitfalls in Data Science
15. Traffic Stops Case Study
- 15.1. Study Background
- 15.2. Investigating Traffic Stops

Part II: Using Data to Understand Our World

16. Prediction and The Data Science Pipeline
- 16.1. Prediction
- 16.2. Correlation
17. Simple Linear Regression
18. Multiple Linear Regression
19. Introduction to Machine Learning
- 19.1. Types of Machine Learning
20. Feature Engineering and Feature Selection
21. Regularization and Cross Validation
- 21.1. Cross-Validation and Regularization
22. Classification with Logistic Regression
23. Classification with Nearest Neighbors
24. Neural Networks and Deep Learning
25. Tree Models
26. Unsupervised Learning and Clustering
27. Data Wrangling
- 27.3. How to begin Wrangling
28. Relational Databases and SQL
29. String Data and Fuzzy Matching
30. Reproducibility
31. Case Study

Glossary of Terms
Glossary of Code

.ipynb

Relational Databases and SQL

28. Relational Databases and SQL#

Forthcoming…

New in This Chapter

Terms

Backward Filling
Data Wrangling
Date Shifting
Domain Expert
Forward Filling
Impute

Code

pd.date_range(start=None, end=None, periods=None)
pd.to_datetime()
str.split()

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29. Fuzzy matching

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