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.
Find out how to grab and load data into an analysis environment
Perform descriptive analysis to extract meaningful summaries from data
Discover probability, parameter estimation, hypothesis tests, and experiment design best practices
Get to grips with resampling and bootstrapping in Python
Delve into statistical tests with variance analysis, time series analysis, and A/B test examples
Understand the statistics behind popular machine learning algorithms
Answer questions on statistics for data scientist interviews