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Essential Statistics for Non-STEM Data Analysts

Essential Statistics for Non-STEM Data Analysts

By : Li
4.6 (10)
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Essential Statistics for Non-STEM Data Analysts

Essential Statistics for Non-STEM Data Analysts

4.6 (10)
By: Li

Overview of this book

Statistics remain the backbone of modern analysis tasks, helping you to interpret the results produced by data science pipelines. This book is a detailed guide covering the math and various statistical methods required for undertaking data science tasks. The book starts by showing you how to preprocess data and inspect distributions and correlations from a statistical perspective. You’ll then get to grips with the fundamentals of statistical analysis and apply its concepts to real-world datasets. As you advance, you’ll find out how statistical concepts emerge from different stages of data science pipelines, understand the summary of datasets in the language of statistics, and use it to build a solid foundation for robust data products such as explanatory models and predictive models. Once you’ve uncovered the working mechanism of data science algorithms, you’ll cover essential concepts for efficient data collection, cleaning, mining, visualization, and analysis. Finally, you’ll implement statistical methods in key machine learning tasks such as classification, regression, tree-based methods, and ensemble learning. By the end of this Essential Statistics for Non-STEM Data Analysts book, you’ll have learned how to build and present a self-contained, statistics-backed data product to meet your business goals.
Table of Contents (19 chapters)
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1
Section 1: Getting Started with Statistics for Data Science
5
Section 2: Essentials of Statistical Analysis
10
Section 3: Statistics for Machine Learning
15
Section 4: Appendix

Chapter 3: Visualization with Statistical Graphs

A picture is worth a thousand words. Humans rely on visual input for more than 90% of all information obtained. A statistical graph can demonstrate trends, explain reasons, or predict futures much better than words if done right.

Python data ecosystems come with a lot of great tools for visualization. The three most important ones are Matplotlib, seaborn, and plotly. The first two are mainly for static plotting, while plotly is capable of interactive plotting and is gaining in popularity gradually.

In this chapter, you will focus on static plotting, which is the backbone of data visualization. We have already extensively used some plots in previous chapters to illustrate concepts.

In this chapter, we will approach them in a systematic way. The topics that will be covered in this chapter are as follows:

  • Picking the right plotting types for different tasks
  • Improving and customizing visualization with advanced aesthetic...
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