November 2023
Beginner
464 pages
11h 38m
English
Content preview from Python for Data Science For Dummies, 3rd Edition
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Chapter 13
Exploring Data Analysis
IN THIS CHAPTER
Understanding the Exploratory Data Analysis (EDA) philosophy
Describing numeric and categorical distributions
Estimating correlation and association
Testing mean differences in groups
Visualizing distributions, relationships, and groups
Data science relies on complex algorithms for building predictions and spotting important signals in data, and each algorithm presents different strong and weak points. In short, you select a range of algorithms, you have them run on the data, you optimize their parameters as much as you can, and finally you decide which one will best help you build your data product or generate insight into your problem. However, even if some of these tools seem like black or even magic boxes, no matter how powerful the machine learning algorithms you use are, you won’t obtain good results if your data has something wrong in it. It is all a matter of GIGO. GIGO stands for “Garbage In/Garbage Out.” It has been a well-known ...
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