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Foundational Python for Data Science

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Foundational Python for Data Science

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  • About eBook Formats
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    ePub EPUB The open industry format known for its reflowable content and usability on supported mobile devices.

    MOBI MOBI The eBook format compatible with the Amazon Kindle and Amazon Kindle applications.

    Adobe Reader PDF The popular standard, used most often with the free Adobe® Reader® software.

    This eBook requires no passwords or activation to read. We customize your eBook by discreetly watermarking it with your name, making it uniquely yours.

About

Features

  • Master Google colab notebook Data Science programming
  • Manipulate data with popular Python libraries such as: pandas and numpy
  • Apply Python Data Science recipes to real world projects
  • Learn functional programming essentials unique to Data Science
  • Access case studies, chapter exercises, learning assessments, comprehensive Jupyter based Notebooks, and a complete final project

Description

  • Copyright 2022
  • Dimensions: 7" x 9-1/8"
  • Pages: 256
  • Edition: 1st
  • Book
  • ISBN-10: 0-13-662435-9
  • ISBN-13: 978-0-13-662435-6

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.

Sample Content

Table of Contents

I. Learning Python in a Notebook Environment
1. Python Past and Future
2. Introduction to Colab
3. Fundamentals of Python
4. Sequences
5. Other Python Data Structures
6. Data Conversion Recipes
7. Execution Control
8. Functions in Python

II. Data Science Libraries
9. What is numpy?
10. Learn sklearn
11. Learn pandas
12. Learn TensorFlow & Keras
13. Use seaborn for 2D Plots
14. Use Plotly for Interactive Plots
15. Specialized Visualization Libraries
16. Learn Natural Language Processing Libraries

III. Fundamentals of Math & Statistics for Data Science
17. Math Python Standard Library
18. Scipy for Statistics & Probability
19. Statistics Python Standard Library

IV. Visualization
20. Interactive Data Visualization
21. Publishing Data Visualizations

V. Fundamentals of Machine Learning for Data Science
22. Learn the Basics of Machine Learning
23. Unsupervised Machine Learning
24. Understand the Fundamentals of Unsupervised Learning
25. Supervised Machine Learning
26. EDA (Exploratory Data Analysis)

VI. Intermediate Python
27. Working on a Python Project in an Editor
28. Functional Programming
29. Lazy Evaluation/Generators
30. Object-Oriented Programming
31. Sorting
32. Pattern Matching
33. I/O

VII. Applied Topics in Data
34. Deep Learning Keras PyTorch
35. Big Data Spark
36. Open Source AutoML Ludwig H2O Auto-Keras
37. Building a Portfolio

Appendices
Appendix A: Answers to Selected Exercises
Appendix B: Instructor Manual

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