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Statistics for Data Science

By : James D. Miller

3.6 (5)

Statistics for Data Science

3.6 (5)

By: James D. Miller

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.

Preface

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

Transitioning from Data Developer to Data Scientist

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 scientist

Summary

Declaring the Objectives

Declaring the Objectives

Key objectives of data science

Summary

A Developer's Approach to Data Cleaning

A Developer's Approach to Data Cleaning

Understanding basic data cleaning

R and common data issues

Transformations

Deductive correction

Deterministic imputation

Summary

Data Mining and the Database Developer

Data Mining and the Database Developer

Data mining

Mining versus querying

Dimensional reduction

Frequent patterning

Sequence mining

Summary

Statistical Analysis for the Database Developer

Statistical Analysis for the Database Developer

Data analysis

Statistical analysis

Summarization

Establishing the nature of data

Successful statistical analysis

R and statistical analysis

Summary

Database Progression to Database Regression

Database Progression to Database Regression

Introducing statistical regression

Identifying opportunities for statistical regression

Project profitability

R and statistical regression

A working example

Summary

Regularization for Database Improvement

Regularization for Database Improvement

Statistical regularization

Summary

Database Development and Assessment

Database Development and Assessment

Assessment and statistical assessment

Development versus assessment

Data assessment and data quality assurance

R and statistical assessment

Summary

Databases and Neural Networks

Databases and Neural Networks

Ask any data scientist

Summary

Boosting your Database

Boosting your Database

Definition and purpose

Back to boosting

Using R to illustrate boosting methods

Summary

Database Classification using Support Vector Machines

Database Classification using Support Vector Machines

Database classification

Definition and purpose of an SVM

Using R and an SVM to classify data in a database

Summary

Database Structures and Machine Learning

Database Structures and Machine Learning

Data structures and data models

Machine learning

Using R to apply machine learning techniques to a database

Summary

Transitioning from Data Developer to Data Scientist

In this chapter (and throughout all of the chapters of this book), we will chart your course for starting and continuing the journey from thinking like a data developer to thinking like a data scientist.

Using developer terminologies and analogies, we will discuss a developer's objectives, what a typical developer mindset might be like, how it differs from a data scientist's mindset, why there are important differences (as well as similarities) between the two and suggest how to transition yourself into thinking like a data scientist. Finally, we will suggest certain advantages of understanding statistics and data science, taking a data perspective, as well as simply thinking like a data scientist.

In this chapter, we've broken things into the following topics:

The objectives of the data developer role
How a data developer thinks
The differences between a data developer and a data scientist
Advantages of thinking like a data scientist
The steps for transitioning into a data scientist mindset

So, let's get started!

CONTINUE READING

83

Tech Concepts

36

Programming languages

73

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