Data Science for Business - Foster Provost & Tom Fawcett

What You Need to Know About Data Mining and Data-Analytic Thinking

Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect.
This guide also helps you understand the many data-mining techniques in use today.
Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. 
You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. 
You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making.

• Understand how data science fits in your organization—and how you can use it for competitive advantage
• Treat data as a business asset that requires careful investment if you’re to gain real value
• Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way
• Learn general concepts for actually extracting knowledge from data
• Apply data science principles when interviewing data science job candidates

[Book Description Source: www.amazon.com ]


Ratings
Goodreads Rating - 4.17 out of 5 ( 932 Ratings ,67 Reviews   - As on Dec 14 2017)
My Rating:   4 out of 5
My Comments:
This book is an excellent Executive Guide on Data Science. Written in a fairly non-technical manner focusing more on business perspective rather than deep technicalities. Serves as a very good introduction to Data Science..

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Introduction to Machine Learning with Python by Andreas C. Muller & Sarah Guido

Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. 
 If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. 
With all the data available today, machine learning applications are limited only by your imagination.
You’ll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. 
Authors Andreas Müller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book.
With this book, you’ll learn:

• Fundamental concepts and applications of machine learning
• Advantages and shortcomings of widely used machine learning algorithms
• How to represent data processed by machine learning, including which data aspects to focus on
• Advanced methods for model evaluation and parameter tuning
• The concept of pipelines for chaining models and encapsulating your workflow
• Methods for working with text data, including text-specific processing techniques
• Suggestions for improving your machine learning and data science skills

 [Book Description Source: www.amazon.com ]


Ratings
Goodreads Rating - 4.55 out of 5 ( 11 Ratings; 0 Reviews  - As on October 30 2017)
My Rating:   4 out of 5
My Comments:
A very good introduction to machine learning concepts. 
Systematic organization of the topics,  and  ample examples provided makes it a worthwhile read. 
Several important algorithms have been discussed along with their pros and cons with minimum use of advanced mathematics.  
Much much better book to start with Machine Learning as compared to the "Machine Learning for Dummies" book which was pedagogically horrible.
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Data Science For Dummies - Lillian Pierson

Discover how data science can help you gain in-depth insight into your business – the easy way!

Jobs in data science abound, but few people have the data science skills needed to fill these increasingly important roles in organizations. 
Data Science For Dummies is the perfect starting point for IT professionals and students interested in making sense of their organization’s massive data sets and applying their findings to real-world business scenarios. 
From uncovering rich data sources to managing large amounts of data within hardware and software limitations, ensuring consistency in reporting, merging various data sources, and beyond, you’ll develop the know-how you need to effectively interpret data and tell a story that can be understood by anyone in your organization.

• Provides a background in data science fundamentals before moving on to working with relational databases and unstructured data and preparing your data for analysis
• Details different data visualization techniques that can be used to showcase and summarize your data
• Explains both supervised and unsupervised machine learning, including regression, model validation, and clustering techniques
• Includes coverage of big data processing tools like MapReduce and Hadoop

It’s a big, big data world out there – let Data Science For Dummies help you harness its power and gain a competitive edge for your organization.

[Book Description Source: www.amazon.com ]
Ratings
Goodreads Rating - 2.22 out of 5 (9 Ratings; 1 Review)
My Rating:  3 out of 5. Provides a good overview of lots of topics, but definitely not written in a manner so that dummies can understand. It is more like a refresher for experts. 

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