This document summarizes Microsoft technologies that can be used for data science, including SQL Server, Excel, Azure Machine Learning, and F#. It provides an overview of these tools and how they can be used for tasks like data mining, semantic search, and machine learning. Examples of using these technologies for text analysis and benchmarking document processing times are also presented. Links to documentation, forums, and conferences related to applying Microsoft technologies to data science are included.

1. Microsoft Technologies
for Data Science
Mark Tabladillo, Ph.D.
Senior Data Scientist
LogicBlox/Predictix

2. Networking
Interactive

4. 





5. http://www.bizjournals.com/atlanta/subscriber-only/2014/07/11/top-employers-in-
metro-atlanta.html

13. http://www.kdnuggets.com/polls/2015/analytics-
data-mining-data-science-software-used.html
http://products.office.com/en-us/excel
http://www.microsoft.com/en-
us/server-cloud/products/sql-server/
http://pytools.codeplex.com/
http://azure.microsoft.com/en-
us/services/hdinsight/
http://www.revolutionanalytics.com/

15. Technology Choices
SQL SERVER ANALYSIS SERVICES Enterprise
Business Intelligence
EXCEL ADD-IN FOR SSAS Office 2013 or Higher x64
SEMANTIC SEARCH Enterprise
Business Intelligence
Standard
Web
Express with Advanced Services
MICROSOFT AZURE ML Free (Size Limited)
Paid (Web Service): Experiment + Query
F# Open Source

17. http://download.microsoft.com/download/F/C/2/FC21C981-
4351-4434-A78A-
3384CA7515BF/SQL_Server_2016_Deeper_Insights_Across_D
ata_White_Paper.pdf

18. SS
SQL
AS
NoSQL

20. Data mining add-in for business
analysts
• Ease of use
• Rich data mining
• Scalable

24. Rowset
Output
with Scores
Varchar
NVarchar
Office
PDF

25. Documents
Full-Text
Keyword
Index
“FTI”
iFilters
Semantic Document
Similarity Index “DSI”
Semantic
Database
Semantic
Key Phrase
Index –
Tag Index
“TI”

26. Simplified Chinese
British English
Portuguese
Chinese (Hong Kong SAR, PRC)
Spanish
Chinese (Singapore)
Chinese (Macau SAR)

27. Time in Seconds vs. Number of Documents
(2011 – K. Mukerjee, T. Porter, S. Gherman – Microsoft)
http://users.cis.fiu.edu/~lzhen001/activities/KDD2011Program/docs/p213.pdf

30.  http://download.microsoft.com/download/3/B/9/3B9
FBA69-8AAD-4707-830F-
6C70A545C389/Introducing_Azure_Machine_Learnin
g.pdf

31.  http://datacamp.com

32. 
 https://github.com/jakevdp/sklearn_pycon2015

33. http://azure.microsoft.com/en-us/services/machine-learning/

37. Mutable Immutable
Classic Open
Source
Java Scala
.NET
Now Open Source
C#, C++,
VB.NET
F#

39. Conference: http://www.kdd.org/

44. http://blogs.technet.com/b/machinelearning/
http://social.msdn.microsoft.com/forums/azure/en-
US/home?forum=MachineLearning
http://sqlserverdatamining.com
http://marktab.net
http://curah.microsoft.com/342704/azure-machine-learning-
videos-february-2015

45.  http://datascience.sqlpass.org