Book Image

Essential Statistics for Non-STEM Data Analysts

By : Rongpeng Li
Book Image

Essential Statistics for Non-STEM Data Analysts

By: Rongpeng 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)
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

Summary

In this chapter, we covered several important topics that usually emerge at the earliest stage of a data science project. We examined their applicable scenarios and conservatively checked some consequences, either numerically or visually. Many arguments made here will be more prominent when we cover other more sophisticated topics later.

In the next chapter, we will review probabilities and statistical concepts, including the mean, the median, quartiles, standard deviation, and skewness. I am sure you will then have a deeper understanding of concepts such as outliers.