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9780136624356

Foundational Python for Data Science

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  • ISBN13:

    9780136624356

  • ISBN10:

    0136624359

  • Format: Nonspecific Binding
  • Copyright: 2021-10-12
  • Publisher: Pearson Technology Group
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Summary

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.

Author Biography

Kennedy Behrman is a veteran software and data engineer. He first used Python writing asset management systems in the Visual Effects industry. He then moved into the startup world, using Python at startups using machine learning to characterize videos and predict the social media power of athletes.

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

Supplemental Materials

What is included with this book?

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