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DATA SCIENCE
1. Fundamentals
2. Statistics
3. Programming
4. Machine learning
5. Text Mining
6. Visualization
7. Big Data
8. Data Ingestions
9. Data Munging
10. Toolbox
Source: Swami Chandrasekaran Becoming a Data Scientist – Curriculum via Metromap: http://nirvacana.com/thoughts/becoming-a-data-scientist/
MIT SLOAN REPORT
The Analytics Talent Dividend
2014 Global executive study in collaboration with SAS
5 year study, 10,000 executives and 100+ countries
Authors: Sam Ransbotham, David Kiron, Pamela Kirk Prentice (SAS)
Analytics is pervasive and growing
WHAT WE RECOMMEND
“Current employees already know the business”
Infusing new data workers can alienate traditional data workersCreate relationships between data-workers and end-users”
No one has all of the skillsTeams of complimentary skills
Train mangers to become more analytical
Train analytics professionals on the business
STRAW MAN FOR BUILDING AND RETAINING A DATA WORKFORCE
Look internally and identify employees with analytical skills and interests
Educate them Tools you use in your organization – Vendor training
Foundational knowledge – Academic courses
Establish internal apprenticeships and buddy system
Build your core
EDUCATIONAL PROGRAMS
At Marquette (Math, Statistics and Computer Science) Computational Sciences
MS in Computing: Big Data and Data Analytics
1. Fundamentals
2. Statistics
3. Programming
4. Machine learning
5. Text Mining
6. Visualization
7. Big Data
8. Data Ingestions
9. Data Munging
10. Toolbox
Inter-disciplinary programs (e.g., Health, Supply Chain, etc.)
UWM
RESOURCES – USER GROUPS AND MEET UPS
Milwaukee Area Big Data Users Group
Milwaukee BI SIG
ASA – Wisconsin Chapter (June 5 meeting)