BIG DATA SKILLSWorkshop

Marquette University AMU 227

Marquette MS in Computing and NVISIA

AGENDA

Welcome

Why we are doing this

Data Talent Need

MIT Sloan Report

Programs

Resources

WHY WE ARE DOING THIS

Competitive environment

Sustainable pipeline of technology talent

Brain drain

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/

DATA TALENT

Aptitude and Attitude

Current and Near-term Skills

DATA TALENT NEEDS

Current and Near-term openings

Projected in 3-5 years

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

TECHNOLOGY IS NO LONGER THE PRIMARY ROADBLOCK

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BIGGEST SKILL GAP IS NOT ANALYTIC CAPABILITIES

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TALENT IS IMPORTANT

but it is difficult to attract and retain.

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SKILL SOURCES

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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)

REPORT

Ideas with actionable items

Framework – Ties the ideas together

List participants from your table