Statistics for Data Science

Get your statistics basics right before diving into the world of data science
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Statistics for Data Science

James D. Miller

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Get your statistics basics right before diving into the world of data science
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Book Details

ISBN 139781788290678
Paperback286 pages

Book Description

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

Chapter 1: 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
Chapter 2: Declaring the Objectives
Key objectives of data science
Summary
Chapter 3: A Developer's Approach to Data Cleaning
Understanding basic data cleaning
R and common data issues
Transformations
Deductive correction
Deterministic imputation
Summary
Chapter 4: Data Mining and the Database Developer
Data mining
Mining versus querying
Dimensional reduction
Frequent patterning
Sequence mining
Summary
Chapter 5: Statistical Analysis for the Database Developer
Data analysis
Statistical analysis
Summarization
Establishing the nature of data
Successful statistical analysis
R and statistical analysis
Summary
Chapter 6: Database Progression to Database Regression
Introducing statistical regression
Identifying opportunities for statistical regression
Project profitability
R and statistical regression
A working example
Summary
Chapter 7: Regularization for Database Improvement
Statistical regularization
Summary
Chapter 8: Database Development and Assessment
Assessment and statistical assessment
Development versus assessment
Data assessment and data quality assurance
R and statistical assessment
Summary
Chapter 9: Databases and Neural Networks
Ask any data scientist
Summary
Chapter 10: Boosting your Database
Definition and purpose
Back to boosting
Using R to illustrate boosting methods
Summary
Chapter 11: 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
Chapter 12: Database Structures and Machine Learning
Data structures and data models
Machine learning
Using R to apply machine learning techniques to a database
Summary

What You Will Learn

  • Analyze 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

Authors

Table of Contents

Chapter 1: 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
Chapter 2: Declaring the Objectives
Key objectives of data science
Summary
Chapter 3: A Developer's Approach to Data Cleaning
Understanding basic data cleaning
R and common data issues
Transformations
Deductive correction
Deterministic imputation
Summary
Chapter 4: Data Mining and the Database Developer
Data mining
Mining versus querying
Dimensional reduction
Frequent patterning
Sequence mining
Summary
Chapter 5: Statistical Analysis for the Database Developer
Data analysis
Statistical analysis
Summarization
Establishing the nature of data
Successful statistical analysis
R and statistical analysis
Summary
Chapter 6: Database Progression to Database Regression
Introducing statistical regression
Identifying opportunities for statistical regression
Project profitability
R and statistical regression
A working example
Summary
Chapter 7: Regularization for Database Improvement
Statistical regularization
Summary
Chapter 8: Database Development and Assessment
Assessment and statistical assessment
Development versus assessment
Data assessment and data quality assurance
R and statistical assessment
Summary
Chapter 9: Databases and Neural Networks
Ask any data scientist
Summary
Chapter 10: Boosting your Database
Definition and purpose
Back to boosting
Using R to illustrate boosting methods
Summary
Chapter 11: 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
Chapter 12: Database Structures and Machine Learning
Data structures and data models
Machine learning
Using R to apply machine learning techniques to a database
Summary

Book Details

ISBN 139781788290678
Paperback286 pages
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