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

Summarization


Let's return to our bicycle parts manufacturing organization example. Suppose we have a new file of transactions and this time we have more data and our efforts are going to be focused on performing a statistical analysis with the intention of identifying specifics that may be contributing to the sales performance reported as part of the preceding activities.

Step one a summarization of the data. The previous section already presented some groupings: products and periods. Using those components, we were able to be telling the story of the organization's sales performance.

What other groupings or categories might be within the data?

For example, if we theorize that sales performance is dependent upon a period of time, the first thing to do is probably to group the data into time periods. Standard time periods are, of course, month, quarter, and year (and we already did that in a prior section), but statistically speaking, the more data the better, so a better time grouping might...