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)
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Chapter 14

Reducing Dimensionality


Bullet Discovering the magic of singular value decomposition

Bullet Understanding the difference between factors and components

Bullet Automatically retrieving and matching images and text

Bullet Building a movie recommender system

Big data is defined as a collection of datasets so huge that the data becomes difficult to process using traditional techniques. The manipulation of big data differentiates statistical problems, which are based on small samples, from data science problems. You typically use traditional statistical techniques on small problems and data science techniques on big problems.

Data may be viewed as big because it consists of many examples, and this is the first kind of big that spontaneously comes to mind. Analyzing a database of millions of customers and interacting with them all simultaneously is really challenging, but that isn’t the only possible perspective of big data. Another view of big data is data dimensionality, which...