Posted by **FenixN** at Dec. 4, 2016

HDRips | MP4/AVC, ~372 kb/s | 1920х1080 / 1280x720 | Duration: 20:19:39 | English: AAC, 128 kb/s (2 ch) | 9.84 GB

The R programming language has arguably become the single most important tool for computational statistics, visualization, and data science. With this Learning Path, master the basics that you'll need as a data scientist. You'll work your data like never before.

Posted by **hill0** at Jan. 2, 2017

English | 20 Dec. 2016 | ISBN: 940241018X | 484 Pages | PDF | 13.93 MB

This book offers an introduction to the physics of nonlinear phenomena through two complementary approaches: bifurcation theory and catastrophe theory. Readers will be gradually introduced to the language and formalisms of nonlinear sciences, which constitute the framework to describe complex systems. The difficulty with complex systems is that their evolution cannot be fully predicted because of the interdependence and interactions between their different components.

Posted by **Underaglassmoon** at Nov. 18, 2016

Springer | Artificial Intelligence | December 5, 2016 | ISBN-10: 3319467611 | 199 pages | pdf | 4.56 mb

Authors: James, Simon

Includes hands-on tutorials on how to program the functions covered in R without needing extensive programming courses

Does not assume a mathematics background

Posted by **Underaglassmoon** at Nov. 11, 2016

Springer | Computer Science | December 6, 2016 | ISBN-10: 3319467611 | 199 pages | pdf | 4.56 mb

Authors: James, Simon

Includes hands-on tutorials on how to program the functions covered in R without needing extensive programming courses

Does not assume a mathematics background

Posted by **ParRus** at Nov. 10, 2016

WEBRip | English | MP4 | 1280 x 720 | AVC ~936 kbps | 30 fps

AAC | 61.9 Kbps | 44.1 KHz | 2 channels | 05:52:02 | 2.39 GB

Use the R Programming Language to execute data science projects and become a data scientist. Implement business solutions, using machine learning and predictive analytics. The R language provides a way to tackle day-to-day data science tasks, and this course will teach you how to apply the R programming language and useful statistical techniques to everyday business situations. With this course, you'll be able to use the visualizations, statistical models, and data manipulation tools that modern data scientists rely upon daily to recognize trends and suggest courses of action.

Posted by **lengen** at Oct. 31, 2016

English | July 3, 2006 | ISBN: 0132216558 | 1043 Pages | PDF | 5 MB

Written by a very well-known authority in forensic science, this book introduces the non-scientific reader to the field of forensic science. Through applications to criminal investigations, clear explanations of the techniques, and the abilities and limitations of modern crime labs, Criminalistics covers the comprehensive realm of forensics.

Posted by **U.N.Owen** at July 5, 2016

Size: 468 MB | Duration: 3h 6m | Video: AVC (.mp4) 1280x720 15&30fps | Audio: AAC 48KHz 2ch

Introduction to Data Science provides a comprehensive overview of modern data science: the practice of obtaining, exploring, modeling, and interpreting data.

Posted by **roxul** at May 31, 2016

English | ISBN: 3527411186, 3527412468 | 2013 | 200 pages | PDF | 6 MB

Posted by **enmoys** at May 27, 2016

2013 | 816 Pages | ISBN: 0226925315 | PDF | 9 MB

Posted by **house23** at Feb. 16, 2016

MP4 | AVC 88kbps | English | 960x540 | 30fps | 16h 03mins | AAC stereo 113kbps | 3.88 GB

Commerce and research is being transformed by data-driven discovery and prediction. Skills required for data analytics at massive levels – scalable data management on and off the cloud, parallel algorithms, statistical modeling, and proficiency with a complex ecosystem of tools and platforms – span a variety of disciplines and are not easy to obtain through conventional curricula. Tour the basic techniques of data science, including both SQL and NoSQL solutions for massive data management (e.g., MapReduce and contemporaries), algorithms for data mining (e.g., clustering and association rule mining), and basic statistical modeling (e.g., linear and non-linear regression).