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Hands-On Data Analysis with Pandas - Second Edition

You're reading from Hands-On Data Analysis with Pandas - Second Edition

Product type Book

Published in Apr 2021

Publisher Packt

ISBN-13 9781800563452

Pages 788 pages

Edition 2nd Edition

Languages

Concepts

Author (1):

Stefanie Molin

Table of Contents (21) Chapters

Preface

1. Section 1: Getting Started with Pandas

2. Chapter 1: Introduction to Data Analysis

3. Chapter 2: Working with Pandas DataFrames

4. Section 2: Using Pandas for Data Analysis

5. Chapter 3: Data Wrangling with Pandas

6. Chapter 4: Aggregating Pandas DataFrames

7. Chapter 5: Visualizing Data with Pandas and Matplotlib

8. Chapter 6: Plotting with Seaborn and Customization Techniques

9. Section 3: Applications – Real-World Analyses Using Pandas

10. Chapter 7: Financial Analysis – Bitcoin and the Stock Market

11. Chapter 8: Rule-Based Anomaly Detection

12. Section 4: Introduction to Machine Learning with Scikit-Learn

13. Chapter 9: Getting Started with Machine Learning in Python

14. Chapter 10: Making Better Predictions – Optimizing Models

15. Chapter 11: Machine Learning Anomaly Detection

16. Section 5: Additional Resources

17. Chapter 12: The Road Ahead

18. Solutions

19. Other Books You May Enjoy

Appendix

Further reading

The following are some resources that you can use to become more familiar with Jupyter:

Jupyter Notebook Basics: https://nbviewer.jupyter.org/github/jupyter/notebook/blob/master/docs/source/examples/Notebook/Notebook%20Basics.ipynb
JupyterLab introduction: https://blog.jupyter.org/jupyterlab-is-ready-for-users-5a6f039b8906
Learning Markdown to make your Jupyter Notebooks presentation-ready: https://medium.com/ibm-data-science-experience/markdown-for-jupyter-notebooks-cheatsheet-386c05aeebed
28 Jupyter Notebook Tips, Tricks, and Shortcuts: https://www.dataquest.io/blog/jupyter-notebook-tips-tricks-shortcuts/

Some resources for learning more advanced concepts of statistics (that we won't cover here) and carefully applying them are as follows:

A Gentle Introduction to Normality Tests in Python: https://machinelearningmastery.com/a-gentle-introduction-to-normality-tests-in-python/
How Hypothesis Tests Work: Confidence Intervals and Confidence Levels: https://statisticsbyjim.com/hypothesis-testing/hypothesis-tests-confidence-intervals-levels/
Intro to Inferential Statistics (Making Predictions with Data) on Udacity: https://www.udacity.com/course/intro-to-inferential-statistics--ud201
Lesson 4: Confidence Intervals (Penn State Elementary Statistics): https://online.stat.psu.edu/stat200/lesson/4
Seeing Theory: A visual introduction to probability and statistics: https://seeing-theory.brown.edu/index.html
Statistics Done Wrong: The Woefully Complete Guide by Alex Reinhart: https://www.statisticsdonewrong.com/
Survey Sampling Methods: https://stattrek.com/survey-research/sampling-methods.aspx

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Published in: Apr 2021 Publisher: Packt ISBN-13: 9781800563452

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Authors (1)

Stefanie Molin

Stefanie Molin is a data scientist and software engineer at Bloomberg LP in NYC, tackling tough problems in information security, particularly revolving around anomaly detection, building tools for gathering data, and knowledge sharing. She has extensive experience in data science, designing anomaly detection solutions, and utilizing machine learning in both R and Python in the AdTech and FinTech industries. She holds a B.S. in operations research from Columbia University's Fu Foundation School of Engineering and Applied Science, with minors in economics, and entrepreneurship and innovation. In her free time, she enjoys traveling the world, inventing new recipes, and learning new languages spoken among both people and computers.

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