Case Analysis Process

Fundamentals of the Average Case Analysis of Particular Algorithms

Rainer Kemp, "Fundamentals of the Average Case Analysis of Particular Algorithms"
1984 | pages: 239 | ISBN: 0471903221 | DJVU | 1,9 mb

Practical Case Analysis (repost)  

Posted by Veslefrikk at Sept. 15, 2014
Practical Case Analysis (repost)

Linda L. Edwards, "Practical Case Analysis"
English | 1996-02-20 | ISBN: 0314064346 | 394 pages | PDF | 101 mb

Environmental Impact Analysis: Process and Methods  

Posted by ksveta6 at Sept. 1, 2014
Environmental Impact Analysis: Process and Methods

Environmental Impact Analysis: Process and Methods by James T. Maughan
2013 | ISBN: 146656783X | English | 400 pages | PDF | 3 MB

Average Case Analysis of Algorithms on Sequences  

Posted by enmoys at Aug. 21, 2013
Average Case Analysis of Algorithms on Sequences

Average Case Analysis of Algorithms on Sequences By Wojciech Szpankowski
2001 | 576 Pages | ISBN: 047124063X | PDF | 18 MB

Practical Case Analysis (Repost)  

Posted by elodar at Sept. 27, 2012
Practical Case Analysis (Repost)

Linda L. Edwards, "Practical Case Analysis"
English | 1996-02-20 | ISBN: 0314064346 | 394 pages | PDF | 101 mb

Practical Case Analysis  

Posted by tot167 at June 12, 2011
Practical Case Analysis

Linda L. Edwards, "Practical Case Analysis"
Del mar Cen gage Lear ning | 1996 | ISBN: 0314064346 | 400 pages | PDF | 100 MB
Software Testing and Analysis: Process, Principles and Techniques

Mauro Pezze, Michal Young, "Software Testing and Analysis: Process, Principles and Techniques"
Chapman | 2002 | ISBN: 0000 | 336 pages | CHM | 9 MB

Variations in Economic Analysis: Essays in Honor of Eli Schwartz  eBooks & eLearning

Posted by tanas.olesya at Nov. 16, 2016
Variations in Economic Analysis: Essays in Honor of Eli Schwartz

Variations in Economic Analysis: Essays in Honor of Eli Schwartz by J. Richard Aronson
English | 9 Nov. 2009 | ISBN: 1441911812 | 160 Pages | PDF | 1 MB

The twelve original essays cover a range of topics, including tax reform, corporate finance, fiscal policy, banking, economic growth, and globalization, representing a variety of methodologies, including economic theory, econometrics, and case analysis.

Coursera - Process Mining: Data Science in Action [repost]  eBooks & eLearning

Posted by ParRus at Feb. 18, 2016
Coursera - Process Mining: Data Science in Action [repost]

Coursera - Process Mining: Data Science in Action
WEBRip | English | MP4 | 960 x 540 | AVC ~151 kbps | 29.970 fps
AAC | 128 Kbps | 44.1 KHz | 2 channels | 13:23:25 | 1.68 GB
Genre: eLearning Video / Computer Science, Engineering and Technology

Process mining is the missing link between model-based process analysis and data-oriented analysis techniques. Through concrete data sets and easy to use software the course provides data science knowledge that can be applied directly to analyze and improve processes in a variety of domains. Data science is the profession of the future, because organizations that are unable to use (big) data in a smart way will not survive. It is not sufficient to focus on data storage and data analysis. The data scientist also needs to relate data to process analysis. Process mining bridges the gap between traditional model-based process analysis (e.g., simulation and other business process management techniques) and data-centric analysis techniques such as machine learning and data mining.

Coursera - Process Mining (2015)  

Posted by house23 at Jan. 29, 2015
Coursera - Process Mining (2015)

Coursera - Process Mining (2015)
MP4 | AVC 161kbps | English | 960x540 | 29.97fps | 14 hours | AAC stereo 128kbps | 1.68 GB
Genre: Video Training

Process mining is the missing link between model-based process analysis and data-oriented analysis techniques. Through concrete data sets and easy to use software the course provides data science knowledge that can be applied directly to analyze and improve processes in a variety of domains. Data science is the profession of the future, because organizations that are unable to use (big) data in a smart way will not survive. It is not sufficient to focus on data storage and data analysis. The data scientist also needs to relate data to process analysis. Process mining bridges the gap between traditional model-based process analysis (e.g., simulation and other business process management techniques) and data-centric analysis techniques such as machine learning and data mining.