La BuzzWord dell’ultimo anno è “Data Science”. Ma cosa significa realmente? Cosa fa un “Data Scientist”? Che strumenti sono messi a disposizione da Microsoft? E che altri strumenti ci sono oltre a Microsoft?

1. Previously known as
Think Big. Move Fast.

2. Template designed by
brought to you by

3. SolidQ
• Born in 2002 in USA and Spain
• Established in 2007 in Italy
• More than 1000 customers and more than 200 consultants worldwide
• Dedicated to Data Management on the Microsoft Platform
• Books Authors, Conference Speakers, SQL Server MVPs and Regional Directors
• www.solidq.com

4. Davide Mauri
• 18 Years of experience on the SQL Server Platform
• Specialized in Data Solution Architecture, Database Design, Performance
Tuning, Business Intelligence
• Microsoft SQL Server MVP
• President of UGISS (Italian SQL Server UG)
• Mentor @ SolidQ
• Video, Book & Article Author
• Regular Speaker @ SQL Server events
• Projects, Consulting, Mentoring & Training

5. Data Science
Reinassance 2.0

6. “Companies are collecting
mountains of information about
you, to predict how
likely you are to buy a product,
and using that knowledge to
craft a marketing message
precisely calibrated to get you to
do so”

7. Data Science
• Extraction of knowledge from data
• So, what’s new?
• Nothing. Except that it’s now economic and fast.
• It’s now applicable to everything. And we have a lot of data produced everyday
that can be used to extract knowledge

8. Data Science
DecisionsKnowledgeInformationData

9. Data Science
• A Sum Of
• Statistics
• Mathematics
• Machine Learning
• Data Mining
• Computer Programming
• Data Engineering
• Visualization
• Data Warehousing
• High Performance Computing
• To support (Informed) Decision Making
• Data-Driven Decisions

10. Data Scientist
• IBM
• A data scientist represents an evolution from the business or data analyst role.
• The formal training is similar, with a solid foundation typically in computer science and
applications, modeling, statistics, analytics and math.
• What sets the data scientist apart is strong business acumen, coupled with the ability to
communicate findings to both business and IT leaders in a way that can influence how
an organization approaches a business challenge.
• It's almost like a Renaissance individual who really wants to learn and bring change to
an organization.

11. Algorithms
• Algorithms are the new gatekeepers
• http://www.slideshare.net/socialisten/algorithms-are-the-new-gatekeepers
• There is simply too much data for a human to analyze!
• They decide
• What we find
• What we see
• What we buy
• Data is the foundation upon which algorithm works
• Better Data lease Better Results
• Data-Driven Decisions will be a MUST in the next years!
• Data Scientists will help companies to leverage their most valuable asset: Data

12. Modern Data Environment
Master
Data
EDW
Data Mart
Big Data
Unstructured
Data
BI Environment
Analytics Environment
Structured
Data

13. Big Data
The 3 V
No, the 4 V!!!
No, no, the 5 V!!!!!

14. http://www.ibmbigdatahub.com/infographic/four-vs-big-data

15. Big Data
• Volume, Velocity, Variety, Veracity….V<your-v-here>
• Data sets with sizes beyond the ability of commonly used software tools
to capture, curate, manage, and process the data within a tolerable elapsed
time
• Grid Computing, Parallel Computing needed
• keep processing time reasonable
• provide scalability

16. Big Data Data
• Paradigm: “Store Now, Figure Out Later”
• Data is the new resource. Never throw it away!
• Unstructured Data
• Text Files
• Images
• Sounds
• Structured/Semi Structured Data
• Sensors
• Transactions
• Logs

17. Data Storage
• RDBMS
• SQL Server
• Hadoop
• HDInsight
• Hortonworks Data Platform
• Distributed File (Eco)System
• CSV
• JSON
• *.*

18. Data Storage
• Hadoop Ecosystem
http://hortonworks.com/hadoop-modern-data-architecture/

19. Data Science & Big Data
• Data Science != Big Data
• Data Science Not Only on Big Data
• Data Science can be applied to Big Data
• Data Science starts from Small Data
• 1) find the algorithm that extract knowledge
• 2) measure algorithm results and in terms of probability

20. Machine Learning
• Machine learning, a branch of artificial intelligence, concerns the construction
and study of systems that can learn from data. (Wikipedia)
• For example, a machine learning system could be trained on email messages to learn to
distinguish between spam and non-spam messages. After learning, it can then be used
to classify new email messages into spam and non-spam folders.
• Flavors
• Supervised
• Unsupervised

21. Data Analysis
• Common Data Scientists Tools
• R
• Weka
• Octave
• Scikit-Learn
• Common Data Scientists Languages
• Python
• Scala
• F#

23. Resources
• https://www.coursera.org/
• Data Scientist Specialization
• https://www.khanacademy.org/
• Math
• http://www.osservatori.net/business_intelligence
• Italian Big Data Market Analysis Resources
• http://www.solidq.com/consulting/
• Data Science Services
• Big Data / Business Intelligence / Data Warehousing

24. Previously known as
Think Big. Move Fast.