This tutorial teaches everything you need to get started with Python programming for the fast-growing field of data analysis. Daniel Chen tightly links each new concept with easy-to-apply, relevant examples from modern data analysis.
Unlike other beginner's books, this guide helps today's newcomers learn both Python and its popular Pandas data science toolset in the context of tasks they'll really want to perform. Following the proven Software Carpentry approach to teaching programming, Chen introduces each concept with a simple motivating example, slowly offering deeper insights and expanding your ability to handle concrete tasks.
Each chapter is illuminated with a concept map: an intuitive visual index of what you'll learn -- and an easy way to refer back to what you've already learned. An extensive set of easy-to-read appendices help you fill knowledge gaps wherever they may exist. Coverage includes:
Setting up your Python and Pandas environment
Getting started with Pandas dataframes
Using dataframes to calculate and perform basic statistical tasks
Plotting in Matplotlib
Cleaning data, reshaping dataframes, handling missing values, working with dates, and more
Building basic data analytics models
Applying machine learning techniques: both supervised and unsupervised
Creating reproducible documents using literate programming techniques
Daniel Chen is a graduate student in the interdisciplinary PhD program in Genetics, Bioinformatics & Computational Biology (GBCB) at Virginia Tech. He is involved with Software Carpentry as an instructor and lesson maintainer. He completed his master's degree in public health at Columbia University Mailman School of Public Health in Epidemiology, and currently works at the Social and Decision Analytics Laboratory under the Biocomplexity Institute of Virginia Tech where he is working with data to inform policy decision-making. He is the author of Pandas for Everyone and Pandas Data Analysis with Python Fundamentals LiveLessons.
Part I. Introduction 0. Setting Up 1. Introduction to Panda's Dataframes 2. Dataframe Components 3. Performing Statistics and Calculations on Sliced and Grouped Dataframes 4. Plotting in Matplotlib Part II. Data Munging 5. Basic Data Cleaning 6. Reshaping Dataframes 7. Missing Values 8. Working with Dates 9. Working with Multiple Dataframes 10. Working with Databases Part III. Modeling 11. Basic Statistics 12. Linear Models and Regression 13. Survival Analysis 14. Model Selection and Diagnostics 15. Time Series Part IV. Machine Learning 16. Supervised Learning 17. Unsupervised Learning Part V. Reproducible Documents (Literate Programming) 18. Jupyter Notebook 19. Pweave Appendices