Guide to intelligent data science: how to intelligently make use of real data

By:

Berthold, Michael R

Contributor(s):

Material type: Text

TextPublication details: Springer Switzerland 2020Description: xiii, 420 pISBN:

9783030455736

Subject(s):

DDC classification:

519.5 BER

Summary: About this book Making use of data is not anymore a niche project but central to almost every project. With access to massive compute resources and vast amounts of data, it seems at least in principle possible to solve any problem. However, successful data science projects result from the intelligent application of: human intuition in combination with computational power; sound background knowledge with computer-aided modelling; and critical reflection of the obtained insights and results. Substantially updating the previous edition, then entitled Guide to Intelligent Data Analysis, this core textbook continues to provide a hands-on instructional approach to many data science techniques, and explains how these are used to solve real world problems. The work balances the practical aspects of applying and using data science techniques with the theoretical and algorithmic underpinnings from mathematics and statistics. Major updates on techniques and subject coverage (including deep learning) are included.

List(s) this item appears in: IT & Decision Sciences | Operation & quantitative Techniques

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Holdings
Item type	Current library	Collection	Call number	Copy number	Status	Date due	Barcode
Book	Indian Institute of Management LRC General Stacks	IT & Decisions Sciences	519.5 BER (Browse shelf(Opens below))	1	Available		002496

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Previous	519.2 ROS Introduction to probability models	519.2076 SPI Probability and statistics	519.5 ACZ Complete business statistics	519.5 BER Guide to intelligent data science: how to intelligently make use of real data	519.5 WEI Statistics using R:	519.5 WOL Primer for data analytics and graduate study in statistics	519.50285 SAI Introduction to data analysis in R:	Next

About this book
Making use of data is not anymore a niche project but central to almost every project. With access to massive compute resources and vast amounts of data, it seems at least in principle possible to solve any problem. However, successful data science projects result from the intelligent application of: human intuition in combination with computational power; sound background knowledge with computer-aided modelling; and critical reflection of the obtained insights and results.

Substantially updating the previous edition, then entitled Guide to Intelligent Data Analysis, this core textbook continues to provide a hands-on instructional approach to many data science techniques, and explains how these are used to solve real world problems. The work balances the practical aspects of applying and using data science techniques with the theoretical and algorithmic underpinnings from mathematics and statistics. Major updates on techniques and subject coverage (including deep learning) are included.

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