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

Chapter 9. Databases and Neural Networks

In this chapter, we will look at and define Artificial Neural Network (ANN) and draw data from a data developer's knowledge of data, databases, and data models to help him or she understand the purpose and use of neural networks, and why neural networks are so significant to data science and statistics.

We have organized the information in this chapter into the following key areas:

  • Definition of a neural network
  • Relating a neural network model to a database model
  • Looking at R-based neural networks
  • Use cases