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Statistics for Data Science

You're reading from  Statistics for Data Science

Product type Book
Published in Nov 2017
Publisher Packt
ISBN-13 9781788290678
Pages 286 pages
Edition 1st Edition
Languages
Concepts

Table of Contents (19) Chapters

Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
Transitioning from Data Developer to Data Scientist Declaring the Objectives A Developer's Approach to Data Cleaning Data Mining and the Database Developer Statistical Analysis for the Database Developer Database Progression to Database Regression Regularization for Database Improvement Database Development and Assessment Databases and Neural Networks Boosting your Database Database Classification using Support Vector Machines Database Structures and Machine Learning

Key objectives of data science


As mentioned in Chapter 1, Transitioning from Data Developer to Data Scientist, the idea of how data science is defined is a matter of opinion.

I personally like the explanation that data science is a progression or, even better, an evolution of thought or steps, as shown in the following figure:

This data science evolution (depicted in the preceding figure) consists of a series of steps or phases that a data scientist tracks, comprising the following:

  • Collecting data
  • Processing data
  • Exploring and visualizing data
  • Analyzing (data) and/or applying machine learning (to data)
  • Deciding (or planning) based on acquired insight

Although a progression or evolution implies a sequential journey, in practice, this is an extremely fluid process; each of the phases may inspire the data scientist to reverse and repeat one or more of the phases until they are satisfied. In other words, all or some phases of the process may be repeated until the data scientist determines that the...

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