This document summarizes information about SolidQ, a company dedicated to data management on the Microsoft platform. SolidQ was established in 2007 in Italy and has over 1000 customers and 200 consultants worldwide. The document also provides details about Davide Mauri, a Microsoft SQL Server MVP and president of an Italian SQL Server user group who works as a mentor at SolidQ, providing consulting, mentoring, and training. Finally, the document discusses concepts related to data science, machine learning, and big data.

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 are the new gatekeepers
• They decided
• What we find
• What we see
• What we buy

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.