Miller J.D. Statistics for Data Science: Leverage the power of statistics for Data Analysis, Classification, Regression, Machine Learning, and Neural Networks

Packt Publishing, 2017. — 286 p.No need to take a degree in statistics, read this book and get a strong statistics base for data science and real-world programs;
Implement statistics in data science tasks such as data cleaning, mining, and analysis
Learn all about probability, statistics, numerical computations, and more with the help of R programsWho This Book Is ForThis book is intended for those developers who are willing to enter the field of data science and are looking for concise information of statistics with the help of insightful programs and simple explanation. Some basic hands on R will be useful.What You Will LearnAnalyze the transition from a data developer to a data scientist mindset
Get acquainted with the R programs and the logic used for statistical computations
Understand mathematical concepts such as variance, standard deviation, probability, matrix calculations, and more
Learn to implement statistics in data science tasks such as data cleaning, mining, and analysis
Learn the statistical techniques required to perform tasks such as linear regression, regularization, model assessment, boosting, SVMs, and working with neural networks
Get comfortable with performing various statistical computations for data science programmatically
In Detail
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.What this book covers
What you need for this bookWho this book is forConventions
Reader feedback
Customer support
Transitioning from Data Developer to Data Scientist
Data developer thinking
Objectives of a data developer
Advantages of thinking like a data scientist
Transitioning to a data scientistDeclaring the Objectives
Key objectives of data scienceA Developers Approach to Data Cleaning
Understanding basic data cleaning
R and common data issues
Transformations
Deductive correction
Deterministic imputationData Mining and the Database Developer
Data mining
Mining versus querying
Dimensional reduction
Frequent patterning
Sequence miningStatistical Analysis for the Database Developer
Data analysis
Statistical analysis
Summarization
Establishing the nature of data
Successful statistical analysis
R and statistical analysisDatabase Progression to Database Regression
Introducing statistical regression
Identifying opportunities for statistical regression
Project profitability
R and statistical regression
A working exampleRegularization for Database Improvement
Statistical regularizationDatabase Development and Assessment
Assessment and statistical assessment
Development versus assessment
Data assessment and data quality assurance
R and statistical assessmentDatabases and Neural Networks
Ask any data scientistBoosting your Database
Definition and purpose
Back to boosting
Using R to illustrate boosting methodsDatabase Classification using Support Vector Machines
Database classification
Definition and purpose of an SVM
Using R and an SVM to classify data in a databaseDatabase Structures and Machine Learning
Data structures and data models
Machine learning
Using R to apply machine learning techniques to a database