Foundational Python for Data Science
- Length: 256 pages
- Edition: 1
- Language: English
- Publisher: Addison-Wesley Professional
- Publication Date: 2021-10-07
- ISBN-10: 0136624359
- ISBN-13: 9780136624356
- Sales Rank: #4931824 (See Top 100 Books)
Data science and machine learning – two of the world’s hottest fields – are attracting talent from a wide variety of technical, business, and liberal arts disciplines. Python, the world’s #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 you’ve 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.
Cover Page About This eBook Halftitle Page Title Page Copyright Page Dedication Page Contents at a Glance Contents Preface Figure Credits Register Your Book Acknowledgments About the Author I: Learning Python in a Notebook Environment 1 Introduction to Notebooks Running Python Statements Jupyter Notebooks Google Colab Summary Questions 2 Fundamentals of Python Basic Types in Python Performing Basic Math Operations Using Classes and Objects with Dot Notation Summary Questions 3 Sequences Shared Operations Lists and Tuples Strings Ranges Summary Questions 4 Other Data Structures Dictionaries Sets Frozensets Summary Questions 5 Execution Control Compound Statements if Statements while Loops for Loops break and continue Statements Summary Questions 6 Functions Defining Functions Scope in Functions Decorators Anonymous Functions Summary Questions II: Data Science Libraries 7 NumPy Installing and Importing NumPy Creating Arrays Indexing and Slicing Element-by-Element Operations Filtering Values Views Versus Copies Some Array Methods Broadcasting NumPy Math Summary Questions 8 SciPy SciPy Overview The scipy.misc Submodule The scipy.special Submodule The scipy.stats Submodule Summary Questions 9 Pandas About DataFrames Creating DataFrames Interacting with DataFrame Data Manipulating DataFrames Manipulating Data Interactive Display Summary Questions 10 Visualization Libraries matplotlib Seaborn Plotly Bokeh Other Visualization Libraries Summary Questions 11 Machine Learning Libraries Popular Machine Learning Libraries How Machine Learning Works Learning More About Scikit-learn Summary Questions 12 Natural Language Toolkit NLTK Sample Texts Frequency Distributions Text Objects Classifying Text Summary Exercises III: Intermediate Python 13 Functional Programming Introduction to Functional Programming List Comprehensions Generators Summary Questions 14 Object-Oriented Programming Grouping State and Function Special Methods Inheritance Summary Questions 15 Other Topics Sorting Reading and Writing Files datetime Objects Regular Expressions Summary Questions A Answers to End-of-Chapter Questions Index Code Snippets
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