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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 (19) Chapters

Title Page

Credits

About the Author

About the Reviewer

www.PacktPub.com

Customer Feedback

Preface

1. Transitioning from Data Developer to Data Scientist FREE CHAPTER

2. Declaring the Objectives

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

Deterministic imputation

We have been discussing the topic of the data scientists deducing or determining how to address or correct a dirty data issue, such as missing, incorrect, incomplete, or inconsistent values within a data pool.

When data is missing (or incorrect, incomplete, or inconsistent) within a data pool, it can make handling and analysis difficult and can introduce bias to the results of the analysis performed on the data. This leads us to imputation.

In data statistics, imputation is when, through a data cleansing procedure, the data scientist replaces missing (or otherwise specified) data with other values.

Because missing data can create problems in analyzing data, imputation is seen as a way to avoid the dangers involved with simply discarding or removing altogether the cases with missing values. In fact, some statistical packages default to discarding any case that has a missing value, which may introduce bias or affect the representativeness of the results. Imputation preserves...

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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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