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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

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Product type Paperback
Published in Nov 2017
Publisher Packt
ISBN-13 9781788290678
Length 286 pages
Edition 1st Edition
Languages
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Author (1):
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James D. Miller James D. Miller
Author Profile Icon James D. Miller
James D. Miller
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Table of Contents (13) Chapters Close

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

Data developer thinking

Having spent plenty of years wearing the hat of a data developer, it makes sense to start out here with a few quick comments about data developers.

In some circles, a database developer is the equivalent of a data developer. But whether data or database, both would usually be labeled as an information technology (IT) professional. Both spend their time working on or with data and database technologies.

We may see a split between those databases (data) developers that focus more on support and routine maintenance (such as administrators) and those who focus more on improving, expanding, and otherwise developing access to data (such as developers).

Your typical data developer will primarily be involved with creating and maintaining access to data rather than consuming that data. He or she will have input in or may make decisions on, choosing programming languages for accessing or manipulating data. We will make sure that new data projects adhere to rules on how databases store and handle data, and we will create interfaces between data sources.

In addition, some data developers are involved with reviewing and tuning queries written by others and, therefore, must be proficient in the latest tuning techniques, various query languages such as Structured Query Language (SQL), as well as how the data being accessed is stored and structured.

In summary, at least strictly from a data developer's perspective, the focus is all about access to valuable data resources rather than the consumption of those valuable data resources.

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