Data Scientist 101
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A Programmer's Guide to Data Mining | The Ancient Art of the Numerati

A Programmer's Guide to Data Mining | The Ancient Art of the Numerati | Data Scientist 101 | Scoop.it
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A Programmer's Guide to Data Mining | The Ancient Art of the Numerati
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A Practical Intro to Data Science

A Practical Intro to Data Science | Data Scientist 101 | Scoop.it

There are plenty of articles and discussions on the web about what data science is, what qualities define a data scientist, how to nurture them, and how you should position yourself to be a competitive applicant. There are far fewer resources out there about the steps to take in order to obtain the skills necessary to practice this elusive discipline. Here we will provide a collection of freely accessible materials and content to jumpstart your understanding of the theory and tools of Data Science.

 
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luiy's curator insight, April 9, 2013 4:42 PM

While the information contained in these resources is a great guide and reference, the best way to become a data scientist is to make, create, and share!

ENVIRONMENT

While the emerging field of data science is not tied to any specific tools, there are certain languages and frameworks that have become the bread and butter for those working in the field. We recommend Python as the programming language of choice for aspiring data scientists due to its general purpose applicability, a gentle (or firm) learning curve, and — perhaps the most compelling reason — the rich ecosystem of resourcesand libraries actively used by the scientific community.

DEVELOPMENT

When learning a new language in a new domain, it helps immensely to have an interactive environment to explore and to receive immediate feedback.IPython provides an interactive REPL which also allows you to integrate a wide variety of frameworks (including R) into your Python programs.

STATISTICS

It is often said that a data scientist is someone who is better at software engineering than a statistician and better at statistics than any software engineer. As such, statistical inference underpins much of the theory behind data analysis and a solid foundation of statistical methods and probability serves as a stepping stone into the world of data science.

jesse roy santos's curator insight, April 17, 2013 8:18 PM

data's is an important information.