A book review on Practical Data Science with Python

A datascience book suitable for readers with beginner-intermediate knowledge on Python (at just $5!)

A big thank you to Data Professor (@thedataprof), for introducing me to this data science book, which I bought the eBook copy for just $5. My initial intention of buying this book was to get inspiration and lecture materials for teaching my new module, which aims to teach post-graduate students on the basics of applied bioinformatics. Surprisingly, after reading the first few chapters, I became so hooked to the content that I finished reading the book in a week. In this blog, I will summarise the contents of the book, and what kept me so captivated.

The chapters of the book are as follows:

Chapter 1: Introduction to Data Science
Chapter 2: Getting Started with Python
Chapter 3: SQL and Built-in File Handling Modules in Python
Chapter 4: Loading and Wrangling Data with Pandas
and NumPy
Chapter 5: Exploratory Data Analysis and Visualization
Chapter 6: Data Wrangling Documents and Spreadsheets
Chapter 7: Web Scraping
Chapter 8: Probability, Distributions, and Sampling
Chapter 9: Statistical Testing for Data Science
Chapter 10: Preparing Data for Machine Learning: Feature
Selection, Feature Engineering, and Dimensionality Reduction
Chapter 11: Machine Learning for Classification
Chapter 12: Evaluating Machine Learning Classification
Models and Sampling for Classification
Chapter 13: Machine Learning with Regression
Chapter 14: Optimizing Models and Using AutoML
Chapter 15: Tree-Based Machine Learning Models
Chapter 16: Support Vector Machine (SVM) Machine
Learning Models
Chapter 17: Clustering with Machine Learning
Chapter 18: Working with Text
Chapter 19: Data Storytelling and Automated Reporting/
Dashboarding