Book Image

Statistics for Data Science

Book Image

Statistics for Data Science

Overview of this book

Data science is an ever-evolving field, which is growing in popularity at an exponential rate. Data science includes techniques and theories extracted from the fields of statistics; computer science, and, most importantly, machine learning, databases, data visualization, and so on. This book takes you through an entire journey of statistics, from knowing very little to becoming comfortable in using various statistical methods for data science tasks. It starts off with simple statistics and then move on to statistical methods that are used in data science algorithms. The R programs for statistical computation are clearly explained along with logic. You will come across various mathematical concepts, such as variance, standard deviation, probability, matrix calculations, and more. You will learn only what is required to implement statistics in data science tasks such as data cleaning, mining, and analysis. You will learn the statistical techniques required to perform tasks such as linear regression, regularization, model assessment, boosting, SVMs, and working with neural networks. By the end of the book, you will be comfortable with performing various statistical computations for data science programmatically.
Table of Contents (19 chapters)
Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Identifying opportunities for statistical regression


Typical statistical analysis efforts which often become official statistical projects, start out with determining an objective and then, ultimately, determining the right approach to meet that objective.

Popular data science opinion declares determining an objective as establishing the purpose of a statistical analysis effort, then splits the purpose into three areas:

  1. Summarizing data (also called building a data profile)
  2. Exposing and exploring relationships between variables in the data
  3. Testing the significance of differences (between variables or groups within the data)

Summarizing data

If your statistical objective is to summarize data, you generate descriptive statistics, such as mean, standard deviation, variances, and so on.

Exploring relationships

If your statistical objective is to look for and learn about relationships in your data, you first examine your data for a form or, in other words, ask the question: does your data revolve around...