James Mott, Ph.D, is a senior education consultant with extensive experience in teaching statistical analysis, modeling, data mining and predictive analytics. He has over 30 years of experience using SPSS products in his own research including IBM SPSS Statistics, IBM SPSS Modeler, and IBM SPSS Amos. He has also been actively teaching these products to IBM/SPSS customers for over 30 years. In addition, he is an experienced historian with expertise in the research and teaching of 20th Century United States political history and quantitative methods. His specialties are data mining, quantitative methods, statistical analysis, teaching, and consulting.
Statistics for Data Science
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
Free Chapter
Transitioning from Data Developer to Data Scientist
Declaring the Objectives
A Developer's Approach to Data Cleaning
Data Mining and the Database Developer
Statistical Analysis for the Database Developer
Database Progression to Database Regression
Regularization for Database Improvement
Database Development and Assessment
Databases and Neural Networks
Boosting your Database
Database Classification using Support Vector Machines
Database Structures and Machine Learning
Customer Reviews