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Principles of Data Science

Principles of Data Science

By : Sinan Ozdemir
3.9 (8)
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Principles of Data Science

Principles of Data Science

3.9 (8)
By: Sinan Ozdemir

Overview of this book

Need to turn your skills at programming into effective data science skills? Principles of Data Science is created to help you join the dots between mathematics, programming, and business analysis. With this book, you’ll feel confident about asking—and answering—complex and sophisticated questions of your data to move from abstract and raw statistics to actionable ideas. With a unique approach that bridges the gap between mathematics and computer science, this books takes you through the entire data science pipeline. Beginning with cleaning and preparing data, and effective data mining strategies and techniques, you’ll move on to build a comprehensive picture of how every piece of the data science puzzle fits together. Learn the fundamentals of computational mathematics and statistics, as well as some pseudocode being used today by data scientists and analysts. You’ll get to grips with machine learning, discover the statistical models that help you take control and navigate even the densest datasets, and find out how to create powerful visualizations that communicate what your data means.
Table of Contents (15 chapters)
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14
Index

Identifying effective and ineffective visualizations


The main goal of data visualization is to have the reader quickly digest the data, including possible trends, relationships, and more. Ideally, a reader will not have to spend more than 5-6 seconds digesting a single visualization. For this reason, we must make visuals very seriously and ensure that we are making a visual as effective as possible. Let's look at four basic types of graphs: scatter plots, line graphs, bar charts, histograms, and box plots.

Scatter plots

A scatter plot is probably one of the simplest graphs to create. It is made by creating two quantitative axes and using data points to represent observations. The main goal of a scatter plot is to highlight relationships between two variables and, if possible, reveal a correlation.

For example, we can look at two variables: average hours of TV watched in a day and a 0-100 scale of work performance (0 being very poor performance and 100 being excellent performance). The goal...

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