An Introduction to Data Science

Learning Path: Introduction to Data Science with R  eBooks & eLearning

Posted by FenixN at Dec. 4, 2016
Learning Path: Introduction to Data Science with R

Learning Path: Introduction to Data Science with R
HDRips | MP4/AVC, ~372 kb/s | 1920х1080 / 1280x720 | Duration: 20:19:39 | English: AAC, 128 kb/s (2 ch) | 9.84 GB
Genre: Development / Programming

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.

Complex Dynamics and Morphogenesis: An Introduction to Nonlinear Science  eBooks & eLearning

Posted by hill0 at Jan. 2, 2017
Complex Dynamics and Morphogenesis: An Introduction to Nonlinear Science

Complex Dynamics and Morphogenesis: An Introduction to Nonlinear Science by Chaouqi Misbah
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.

An Introduction to Data Analysis using Aggregation Functions in R  eBooks & eLearning

Posted by Underaglassmoon at Nov. 18, 2016
An Introduction to Data Analysis using Aggregation Functions in R

An Introduction to Data Analysis using Aggregation Functions in R
Springer | Artificial Intelligence | December 5, 2016 | ISBN-10: 3319467611 | 199 pages | pdf | 4.56 mb

Authors: James, Simon
Provides an overview of aggregation functions
Includes hands-on tutorials on how to program the functions covered in R without needing extensive programming courses
Does not assume a mathematics background

An Introduction to Data Analysis using Aggregation Functions in R  eBooks & eLearning

Posted by Underaglassmoon at Nov. 11, 2016
An Introduction to Data Analysis using Aggregation Functions in R

An Introduction to Data Analysis using Aggregation Functions in R
Springer | Computer Science | December 6, 2016 | ISBN-10: 3319467611 | 199 pages | pdf | 4.56 mb

Authors: James, Simon
Provides an overview of aggregation functions
Includes hands-on tutorials on how to program the functions covered in R without needing extensive programming courses
Does not assume a mathematics background

Introduction To Data Science [repost]  eBooks & eLearning

Posted by ParRus at Nov. 10, 2016
Introduction To Data Science [repost]

Introduction To Data Science
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
Genre: eLearning Video / Business, Data Analysis

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.

Criminalistics: An Introduction to Forensic Science  eBooks & eLearning

Posted by lengen at Oct. 31, 2016
Criminalistics: An Introduction to Forensic Science

Criminalistics: An Introduction to Forensic Science (College Edition) (9th Edition) by Richard Saferstein
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.

Lynda - Introduction to Data Science  eBooks & eLearning

Posted by U.N.Owen at July 5, 2016
Lynda - Introduction to Data Science

Lynda - Introduction to Data Science
Size: 468 MB | Duration: 3h 6m | Video: AVC (.mp4) 1280x720 15&30fps | Audio: AAC 48KHz 2ch
Genre: eLearning | Level: Beginner | Language: English

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

An Introduction to Cluster Science  eBooks & eLearning

Posted by roxul at May 31, 2016
An Introduction to Cluster Science

Phuong Mai Dinh and Paul-Gerhard Reinhard, "An Introduction to Cluster Science"
English | ISBN: 3527411186, 3527412468 | 2013 | 200 pages | PDF | 6 MB

Zookeeping: An Introduction to the Science and Technology (Repost)  eBooks & eLearning

Posted by enmoys at May 27, 2016
Zookeeping: An Introduction to the Science and Technology (Repost)

Zookeeping: An Introduction to the Science and Technology By Mark Irwin
2013 | 816 Pages | ISBN: 0226925315 | PDF | 9 MB

Coursera - Introduction to Data Science  eBooks & eLearning

Posted by house23 at Feb. 16, 2016
Coursera - Introduction to Data Science

Coursera - Introduction to Data Science
MP4 | AVC 88kbps | English | 960x540 | 30fps | 16h 03mins | AAC stereo 113kbps | 3.88 GB
Genre: Video Training

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).