...
Full Bio
Follow on
Apart from Dot NET Vs PHP Vs Java. Why Dot NET is the best?
today
Everyone wants a Java Developer. Why?
today
Do you really need to know Programming Language for becoming a Professional Hacker?
yesterday
K2-Artificial Intelligence Technology has been introduced by Tech Mahindra
yesterday
Every Programmer should strive for reading these 5 books
526107 views
Why you should not become a Programmer or not learn Programming Language?
171594 views
See the Salaries if you are willing to get a Job in Programming Languages without a degree?
135279 views
Highest Paid Programming Languages With Highest Market Demand
121974 views
Python Programming Language can easily be acquired easily. How?
106422 views
Data Scientist should admire these famous authors for some Famous Data Science books
Some books which will help data scientist to cope up with professional learning.
Using data science techniques, you can transform raw data into actionable insights for domains ranging from urban planning to precision medicine. Programming Skills for Data Science brings together all the foundational skills you need to get started, even if you have no programming or data science experience.
Leading instructors Michael Freeman and Joel Ross guide you through installing and configuring the tools you need to solve professional-level data science problems, including the widely used R language and Git version-control system. They explain how to wrangle your data into a form where it can be easily used, analyzed, and visualized so others can see the patterns you've uncovered. Step by step, you'll master powerful R programming techniques and troubleshooting skills for probing data in new ways, and at larger scales.
A concise introduction to the emerging field of data science, explaining its evolution, relation to machine learning, current uses, data infrastructure issues, and ethical challenges.
The goal of data science is to improve decision making through the analysis of data. Today data science determines the ads we see online, the books and movies that are recommended to us online, which emails are filtered into our spam folders, and even how much we pay for health insurance. This volume in the MIT Press Essential Knowledge series offers a concise introduction to the emerging field of data science, explaining its evolution, current uses, data infrastructure issues, and ethical challenges.
This book offers a brief history of the field, introduces fundamental data concepts, and describes the stages in a data science project. It considers data infrastructure and the challenges posed by integrating data from multiple sources, introduces the basics of machine learning, and discusses how to link machine learning expertise with real-world problems. The book also reviews ethical and legal issues, developments in data regulation, and computational approaches to preserving privacy. Finally, it considers the future impact of data science and offers principles for success in data science projects.
An Introduction to Data Science by Jeffrey S. Saltz and Jeffrey M. Stanton is an easy-to-read, gentle introduction for people with a wide range of backgrounds into the world of data science. Needing no prior coding experience or a deep understanding of statistics, this book uses the R programming language and RStudio platform to make data science welcoming and accessible for all learners. After introducing the basics of data science, the book builds on each previous concept to explain R programming from the ground up. Readers will learn essential skills in data science through demonstrations of how to use data to construct models, predict outcomes, and visualize data.
Data science for Business Paperback- 2013
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 to 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.
It seems like ''big data'' is in the news every day, as we read the latest examples of how powerful algorithms are teasing out the hidden connections between seemingly unrelated things. Whether it is used by the NSA to fight terrorism or by online retailers to predict customers buying patterns, big data is a revolution occurring around us, in the process of forever changing economics, science, culture, and the very way we think. But it also poses new threats, from the end of privacy as we know it to the prospect of being penalized for things we haven't even done yet, based on big data's ability to predict our future behavior. What we have already seen is just the tip of the iceberg.
Big Data is the first major book about this earthshaking subject, with two leading experts explaining what big data is, how it will change our lives, and what we can do to protect ourselves from its hazards.
Source: HOB