Book Image

Python for Data Science For Dummies - Second Edition

By : John Paul Mueller, Luca Massaron
Book Image

Python for Data Science For Dummies - Second Edition

By: John Paul Mueller, Luca Massaron

Overview of this book

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. The book begins by discussing how Python can make data science easy. You’ll learn how to work with the Anaconda tool suite that makes coding in Python easy. You’ll also learn to write code using Google Colab. As you progress, you'll discover how to perform interesting calculations and data manipulations using various Python libraries, such as pandas and NumPy. You’ll learn how to create data visualizations with MatPlotLib. While learning the advanced concepts, you’ll learn how to wrangle data by using techniques, such as hierarchical clustering. Finally, you’ll learn how to work with decision trees and use machine learning to make predictions. By the end of the book, you’ll have the skills and the knowledge that’s needed to write code in Python and extract information from data.
Table of Contents (13 chapters)
Free Chapter
1
Cover
9
Index
10
About the Authors
11
Advertisement Page
12
Connect with Dummies
13
End User License Agreement

Index

Numerics

  • 2-D arrays, 140
  • 3-D arrays, 140
  • 64-bit operating system, 42–43

A

  • ABC language, 22
  • absolute errors, linear regression, 352
  • Adaboost application, 424–425
  • adjacency matrices, 165–166
  • agglomerative clustering
    • hierarchical cluster solution, 307–308
    • linkage methods, 306
    • metrics, 306
    • overview, 305–306
    • two-phase clustering solution, 308–310
  • aggregation, shaping data through, 146–147
  • AI applications, 13
  • AI For Dummies (Mueller and Massaron), 13
  • algorithms
    • choosing, 33
    • classifiers, 158
    • hyperparameters
      • GBM model, 427–428
      • grid search, 364–368
      • overview, 363–364
      • randomized search, 368–369
    • k-means
      • big data, 304–305
      • centroids, 298–299
      • ground truth, 301–304
      • image data, 299–301
      • overview, 297
    • K-Nearest Neighbors
      • k-parameter, 344–345
      • overview, 342–343
      • predicting, 343–344
    • linear regression
      • limitations of, 333–334
      • with multiple...