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Statistics for Data Science

You're reading from Statistics for Data Science Leverage the power of statistics for Data Analysis, Classification, Regression, Machine Learning, and Neural Networks

Product type Paperback

Published in Nov 2017

Publisher Packt

ISBN-13 9781788290678

Length 286 pages

Edition 1st Edition

Languages

R

Tools

RStudio

Concepts

Data Analysis

Author (1):

James D. Miller

View More author details

Table of Contents (13) Chapters

Preface

1. Transitioning from Data Developer to Data Scientist

2. Declaring the Objectives FREE CHAPTER

3. A Developer's Approach to Data Cleaning

4. Data Mining and the Database Developer

5. Statistical Analysis for the Database Developer

6. Database Progression to Database Regression

7. Regularization for Database Improvement

8. Database Development and Assessment

9. Databases and Neural Networks

10. Boosting your Database

11. Database Classification using Support Vector Machines

12. Database Structures and Machine Learning

R and common data issues

Let's start this section with some background on R. R is a language and environment that is easy to learn, very flexible in nature, and very focused on statistical computing, making it a great choice for manipulating, cleaning, summarizing, producing probability statistics, and so on.

In addition, here are a few more reasons to use R for data cleaning:

It is used by a large number of data scientists so it's not going away anytime soon
R is platform independent, so what you create will run almost anywhere
R has awesome help resources--just Google it, you'll see!

Outliers

The simplest explanation for what outliers are might be is to say that outliers are those data points that just don't fit the rest of your data. Upon observance, any data that is either very high, very low, or just unusual (within the context of your project), is an outlier. As part of data cleansing, a data scientist would typically identify the outliers and then address the outliers using a generally...

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Authors (1)

James D. Miller

James D. Miller

James D. Miller is an IBM certified expert, Master Consultant, Application/System Architect with +35 years of applications & system design/development experience across multiple platforms, technologies and data formats, including Big Data. His experience includes IBM Planning Analytics, BI, Web architecture & design, systems analysis, GUI design & testing, Data modeling, design, and development of OLAP, Client/Server, Web & Mainframe applications and systems utilizing: Planning Analytics Workspace (PAW), IBM Watson Analytics, Cognos BI & TM1, Framework Manager, dynaSight/ArcPlan, ASP, DHTML, XML, MS Visual Basic, VBA, PERL, R, SPLUNK, MS SQL Server, ORACLE, etc. He has authored numerous books, including Implementing Splunk - Second Edition; Mastering Splunk; Hands-On Machine Learning with IBM Watson; IBM Watson Projects; Statistics for Data Science; Mastering Predictive Analytics with R - Second Edition and others. Project areas include those with Data Analytics, Planning Analytics, and FOPM projects, holding various roles from architect, developer, technical and project leader.

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