Python for Data Science For Dummies

Python for Data Science For Dummies
  • eBook:
    Python for Data Science For Dummies
  • Author:
    John Paul Mueller, Luca Massaron
  • Edition:
    2 edition
  • Categories:
  • Data:
    February 27, 2019
  • ISBN:
  • ISBN-13:
  • Language:
  • Pages:
    496 pages
  • Format:
    PDF, ePUB

Book Description
The fast and easy way to learn Python programming and statistics
Python is a general-purpose programming language created in the late 1980s—and named after Monty Python—that's used by thousands of people to do things from testing microchips at Intel, to powering Instagram, to building video games with the PyGame library. 
Python For Data Science For Dummies is written for people who are new to data analysis, and discusses the basics of Python data analysis programming and statistics. The book also discusses Google Colab, which makes it possible to write Python code in the cloud.
  • Get started with data science and Python
  • Visualize information
  • Wrangle data
  • Learn from data
The book provides the statistical background needed to get started in data science programming, including probability, random distributions, hypothesis testing, confidence intervals, and building regression models for prediction.


Part 1: Getting Started with Data Science and Python
CHAPTER 1: Discovering the Match between Data Science and Python
CHAPTER 2: Introducing Python’s Capabilities and Wonders
CHAPTER 3: Setting Up Python for Data Science
CHAPTER 4: Working with Google Colab

Part 2: Getting Your Hands Dirty with Data
CHAPTER 5: Understanding the Tools
CHAPTER 6: Working with Real Data
CHAPTER 7: Conditioning Your Data
CHAPTER 8: Shaping Data
CHAPTER 9: Putting What You Know in Action

Part 3: Visualizing Information
CHAPTER 10: Getting a Crash Course in MatPlotLib
CHAPTER 11: Visualizing the Data

Part 4: Wrangling Data
CHAPTER 12: Stretching Python’s Capabilities
CHAPTER 13: Exploring Data Analysis
CHAPTER 14: Reducing Dimensionality
CHAPTER 15: Clustering
CHAPTER 16: Detecting Outliers in Data

Part 5: Learning from Data
CHAPTER 17: Exploring Four Simple and Effective Algorithms
CHAPTER 18: Performing Cross-Validation, Selection, and Optimization
CHAPTER 19: Increasing Complexity with Linear and Nonlinear Tricks
CHAPTER 20: Understanding the Power of the Many

Part 6: The Part of Tens
CHAPTER 21: Ten Essential Data Resources
CHAPTER 22: Ten Data Challenges You Should Take

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