Description
Book Synopsis: Statistical methods are a key part of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what’s important and what’s not.
Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.
With this book, you’ll learn:
- Why exploratory data analysis is a key preliminary step in data science
- How random sampling can reduce bias and yield a higher quality dataset, even with big data
- How the principles of experimental design yield definitive answers to questions
- How to use regression to estimate outcomes and detect anomalies
- Key classification techniques for predicting which categories a record belongs to
- Statistical machine learning methods that “learn” from data
- Unsupervised learning methods for extracting meaning from unlabeled data
Details
Are you a data scientist who wishes they had more formal statistics training? Look no further! Our Practical Statistics for Data Scientists book is the perfect resource for you. We understand that statistics is a crucial part of data science, but it can be difficult to find resources that cover the topic from a data science perspective. That's why our book focuses on applying statistical methods specifically to data science. Whether you're new to statistics or just need a refresher, our accessible and readable format will guide you through the essential concepts.
Don't waste time searching through books and courses that only touch on statistical methods without providing a deeper perspective. Our Practical Statistics for Data Scientists book is designed to bridge the gap for those already familiar with the R programming language and with some exposure to statistics. We dive deep into topics like exploratory data analysis, random sampling, and experimental design to not only teach you the techniques, but also explain why they are important in data science.
Regression, classification, and machine learning are all covered in our comprehensive Practical Statistics for Data Scientists book. Learn how to estimate outcomes, detect anomalies, predict categories, and extract meaning from unlabeled data. We provide real-life examples and practical advice on how to avoid misusing statistical methods, ensuring you have the skills to make the most of your data. Whether you’re a beginner or an experienced data scientist, our book is an essential tool for advancing your statistical knowledge and improving your data analysis abilities.
Ready to take your data science skills to the next level? Order your copy of Practical Statistics for Data Scientists today and unlock the power of statistical methods in your data analysis. Don't miss out on this opportunity to gain a deeper statistical perspective and become a more proficient data scientist. Order now!
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