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

Development versus assessment


Although an assessment process does produce output, which ultimately is perhaps just a decision (that is, does the data, database, or statistical data model under observation meet the acceptable limits of performance, based on the objectives?), development implies building.

Development can also mean improving by expanding, enlarging, or refining. This means that (or at least it implies that) whatever one is developing may never be completely done. In fact, development and assessment do go hand in hand.

An industry-proven practice recommendation to develop anything is as follows:

  • Build (or develop)
  • Test
  • Assess
  • Repeat

When developing a relational data model, one might utilize a create SQL statement, something like the following code:

mysql> CREATE TABLE test (a INT NOT NULL AUTO_INCREMENT,
-> PRIMARY KEY (a), KEY(b))
-> ENGINE=MyISAM SELECT b,c FROM test2;

Dissecting the preceding code, we can see that the outcome is that a table object test is generated. Perhaps...