Foundational Python for Data Science

Foundational Python for Data Science

by Kennedy Behrman
Released September 2021
Publisher(s): Addison-Wesley Professional
ISBN: 9780136624417

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Book description

Data science and machine learning two of the worlds hottest fields are attracting talent from a wide variety of technical, business, and liberal arts disciplines. Python, the worlds #1 programming language, is also the most popular language for data science and machine learning. This is the first guide specifically designed to help millions of people with widely diverse backgrounds learn Python so they can use it for data science and machine learning.

Leading data science instructor and practitioner Kennedy Behrman first walks through the process of learning to code for the first time with Python and Jupyter notebook, then introduces key libraries every Python data science programmer needs to master. Once youve learned these foundations, Behrman introduces intermediate and applied Python techniques for real-world problem-solving.

Throughout, Foundational Python for Data Science presents hands-on exercises, learning assessments, case studies, and more all created with colab (jupyter compatible) notebooks, so you can execute all coding examples interactively without installing or configuring any software.

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Table of contents

  1. Cover Page
  2. About This eBook
  3. Halftitle Page
  4. Title Page
  5. Copyright Page
  6. Dedication Page
  7. Contents at a Glance
  8. Contents
  9. Preface
  10. Figure Credits
  11. Register Your Book
  12. Acknowledgments
  13. About the Author
  14. I: Learning Python in a Notebook Environment
    1. 1 Introduction to Notebooks
      1. Running Python Statements
      2. Jupyter Notebooks
      3. Google Colab
      4. Summary
      5. Questions
    2. 2 Fundamentals of Python
      1. Basic Types in Python
      2. Performing Basic Math Operations
      3. Using Classes and Objects with Dot Notation
      4. Summary
      5. Questions
    3. 3 Sequences
      1. Shared Operations
      2. Lists and Tuples
      3. Strings
      4. Ranges
      5. Summary
      6. Questions
    4. 4 Other Data Structures
      1. Dictionaries
      2. Sets
      3. Frozensets
      4. Summary
      5. Questions
    5. 5 Execution Control
      1. Compound Statements
      2. if Statements
      3. while Loops
      4. for Loops
      5. break and continue Statements
      6. Summary
      7. Questions
    6. 6 Functions
      1. Defining Functions
      2. Scope in Functions
      3. Decorators
      4. Anonymous Functions
      5. Summary
      6. Questions
  15. II: Data Science Libraries
    1. 7 NumPy
      1. Installing and Importing NumPy
      2. Creating Arrays
      3. Indexing and Slicing
      4. Element-by-Element Operations
      5. Filtering Values
      6. Views Versus Copies
      7. Some Array Methods
      8. Broadcasting
      9. NumPy Math
      10. Summary
      11. Questions
    2. 8 SciPy
      1. SciPy Overview
      2. The scipy.misc Submodule
      3. The scipy.special Submodule
      4. The scipy.stats Submodule
      5. Summary
      6. Questions
    3. 9 Pandas
      1. About DataFrames
      2. Creating DataFrames
      3. Interacting with DataFrame Data
      4. Manipulating DataFrames
      5. Manipulating Data
      6. Interactive Display
      7. Summary
      8. Questions
    4. 10 Visualization Libraries
      1. matplotlib
      2. Seaborn
      3. Plotly
      4. Bokeh
      5. Other Visualization Libraries
      6. Summary
      7. Questions
    5. 11 Machine Learning Libraries
      1. Popular Machine Learning Libraries
      2. How Machine Learning Works
      3. Learning More About Scikit-learn
      4. Summary
      5. Questions
    6. 12 Natural Language Toolkit
      1. NLTK Sample Texts
      2. Frequency Distributions
      3. Text Objects
      4. Classifying Text
      5. Summary
      6. Exercises
  16. III: Intermediate Python
    1. 13 Functional Programming
      1. Introduction to Functional Programming
      2. List Comprehensions
      3. Generators
      4. Summary
      5. Questions
    2. 14 Object-Oriented Programming
      1. Grouping State and Function
      2. Special Methods
      3. Inheritance
      4. Summary
      5. Questions
    3. 15 Other Topics
      1. Sorting
      2. Reading and Writing Files
      3. datetime Objects
      4. Regular Expressions
      5. Summary
      6. Questions
  17. A Answers to End-of-Chapter Questions
  18. Index
  19. Code Snippets

Product information

  • Title: Foundational Python for Data Science
  • Author(s): Kennedy Behrman
  • Release date: September 2021
  • Publisher(s): Addison-Wesley Professional
  • ISBN: 9780136624417