Reinforce your understanding of data science and data analysis from a statistical perspective to extract meaningful insights from your data using Python programming

Key Features

• Work your way through the entire data analysis pipeline with statistics concerns in mind to make reasonable decisions
• Understand how various data science algorithms function
• Build a solid foundation in statistics for data science and machine learning using Python-based examples

Book Description

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.

What you will learn

• 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

Who this book is for

This book is an entry-level guide for data science enthusiasts, data analysts, and anyone starting out in the field of data science and looking to learn the essential statistical concepts with the help of simple explanations and examples. If you're a developer or student with a non-mathematical background, you'll find this book useful. Working knowledge of the Python programming language is required.

Table of Contents

1. Fundamentals of Data Collection, Cleaning and Preprocessing
2. Essential Statistics for Data Assessment
3. Visualization with Statistical Graphs
4. Sampling and Inferential Statistics
5. Common Probability Distributions
6. Parametric Estimation
7. Statistical Hypothesis Testing
8. Statistics for Regression
9. Statistics for Classification
10. Statistics for Tree-based Methods
11. Statistics for Ensemble Method
12. A Collection of Best Practices
13. Exercises and Projects

About the Author

Rongpeng Li is a data science instructor and a senior data scientist at Galvanize, Inc. He has previously been a research programmer at Information Sciences Institute, working on knowledge graphs and artificial intelligence. He has also been the host and organizer of the Data Analysis Workshop Designed for Non-STEM Busy Professionals at LA.