【2017新书】The Data Science Design Manual

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• Title: The Data Science Design Manual
• Author: Steven S. Skiena
• Length: 445 pages
• Edition: 1st ed. 2017
• Language: English
• Publisher: Springer
• Publication Date: 2017-07-01
• ISBN-10: 3319554433
• ISBN-13: 9783319554433
  

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Book Description

This engaging and clearly written textbook/reference provides a must-have introduction to the rapidly emerging interdisciplinary field of data science. It focuses on the principles fundamental to becoming a good data scientist and the key skills needed to build systems for collecting, analyzing, and interpreting data.

The Data Science Design Manual is a source of practical insights that highlights what really matters in analyzing data, and provides an intuitive understanding of how these core concepts can be used. The book does not emphasize any particular programming language or suite of data-analysis tools, focusing instead on high-level discussion of important design principles.

This easy-to-read text ideally serves the needs of undergraduate and early graduate students embarking on an “Introduction to Data Science” course. It reveals how this discipline sits at the intersection of statistics, computerscience, and machine learning, with a distinct heft and character of its own. Practitioners in these and related fields will find this book perfect for self-study as well.

Additional learning tools:

• Contains “War Stories,” offering perspectives on how data science applies in the real world
• Includes “Homework Problems,” providing a wide range of exercises and projects for self-study
• Provides a complete set of lecture slides and online video lectures at www.data-manual.com
• Provides “Take-Home Lessons,” emphasizing the big-picture concepts to learn from each chapter
• Recommends exciting “Kaggle Challenges” from the online platform Kaggle
• Highlights “False Starts,” revealing the subtle reasons why certain approaches fail
• Offers examples taken from the data science television show “The Quant Shop” (www.quant-shop.com)
  

Table of Contents

Chapter 1 What Is Data Science?
Chapter 2 Mathematical Preliminaries
Chapter 3 Data Munging
Chapter 4 Scores And Rankings
Chapter 5 Statistical Analysis
Chapter 6 Visualizing Data
Chapter 7 Mathematical Models
Chapter 8 Linear Algebra
Chapter 9 Linear And Logistic Regression
Chapter 10 Distance And Network Methods
Chapter 11 Machine Learning
Chapter 12 Big Data: Achieving Scale
Chapter 13 Coda

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