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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

Statistical analysis

Some in the study of statistics sometimes describe statistical analysis as part of statistical projects that involves the collection and scrutiny of a data source in an effort to identify trends within the data.

With data analysis, the goal is to validate that the data is appropriate for a need, and with statistical analysis, the goal is to make sense of, and draw some inferences from, the data.

There is a wide range of possible statistical analysis techniques or approaches that can be considered.

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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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