How do we train Data Scientists and Data Engineers?

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How do we train Data Scientists

and Data Engineers?

Eric Rozier

Asst Prof of EECS at the University of Cincinnati

Faculty Mentor DSSG at the University of Chicago

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Training the Next Generation of

Data Scientists

• Focus on two main programs:

– Summer 3 month intensive program

• DSSG

– Normal year curriculum development to support in class hands-on experiences

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http://dssg.uchicago.edu @datascifellows

Eric & Wendy Schmidt

Data Science for Social Good

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Data Science for Social Good @datascifellows

What is DSSG?

40-50 Fellows in teams of 3-4 Experienced Mentors 12 weeks in Chicago Impactful problems with non-profit & govt

partners

Data Science for

Social Good Fellowship

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Goals of the Fellowship

• Train data scientists who care about and understand how to solve social problems

• Expose and train governments & non profits to use data to make better decisions

• Seed a community of people and organizations working together to make social impact

• Create open source data science tools that are targeted at the needs of high impact social problems

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48 Fellows

8 Mentors

14 Projects

12 Weeks

36 Fellows

6 Mentors

12 Projects

12 Weeks

2013

2014

By the Numbers

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Ideal Fellows

Making an Impact with Data

Computer Science &

Programming Statistics & _Machine Learning

Econometrics & Social Science Methods

Databases Experimental Design

Communication Problem Formulation

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~1000

Applicants

40

countries ~250 Universities

84 fellows

_{Computer Scientists}

Statisticians Economists Public Policy

(and other computational and quantitative fields)

2013-2014 Fellows

CMU U. of Chicago Northwestern Harvard MIT Stanford ITAM Cornell Yale Villanova Ohio State USC U Penn Notre Dame U of Minnesota U of Michigan Cambridge McGill UC Berkeley U of Colorado Swarthmore Oberlin UIUC Emory Duke Fordham Johns Hopkins IIT SAIC NYU Penn State Simon Fraser UC Santa Barbara

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• Partners: Non-Profits, Government Agencies, Corporations with a Social Mission

• Geographies: Local, State, National, and International

• Types of Problems: Impact Evaluation, Targeting, Risk Modeling,

• Types of Data: Structured data, geospatial data, time series, text data, network data

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Health Energy Education

Economic Development Corruption Federal Budgeting Predicting lead poisoning Home inspection data Reducing energy use via

disaggregatio n Smart-meter data Predicting high school dropout Education records Targeting and assessing urban revitalization Administra -tive data Detecting collusion Contract data Identifying earmarks Congress-ional bills

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• Improving high school graduation rates by identifying at-risk students early

• Increasing government transparency by identifying earmarks

• Developing new strategies to reduce maternal mortality

• Preventing Lead Poisoning by proactive home inspections and health check-ups

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Buildings: 197,157 Time: 76 years Money: $98 million Buildings: 42,695 Time: 16.4 years Money: $21.3 million Buildings: 378 Time: 2 months Money: $189,000

Prediction Saves Time & Money

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The Eric & Wendy SchmidtData Science for Social Good Summer Fellowship 2014

At Risk Children

Lead Levels During Childhood Even without detailed child-level features, there are strong, sanity-checked, prediction-capable patterns

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Target: Prediction From Birth

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Who we’re looking for ?

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• Expertise in one or more of the following ares

• Computer Science • Statistics

• Public Policy • Social Science

• Other Quantitative or Analytical Areas

• Some coding experience

• Passion for making a social impact

• Problem solving (critical thinking) experience • Enjoy working on a team

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Data Science for Social Good @datascifellows • Deep expertise in computer science, machine learning, statistics, or social sciences • Experience working on real problems in industry • Experience leading teams

and managing projects

Mentors

Ben Yuhas Principal, Yuhas Consulting Group Eric Rozier Assistant Professor of Electrical and Computer Engineering Kate Cagney

Sociology & Health Studies Director, Population

Research Center U’Chicago

Joe Walsh

Lead Forecaster for GE Healthcare & Policy

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• Organizations that…

1. Have an interesting social-impact problem to solve

2. Have data that can help solve it

3. Have a desire to put our work into action

• Especially interested in longer-term collaborations beyond 12 weeks of the fellowship

Project Partners

Governments / Government Orgs Foundations Research Institutions Non-Profits

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Get Involved!

• Application deadlines: • Fellows: Feb 1, 2015 • Mentors: Feb 1, 2015 • Partners: Jan 10, 2015

• Applications & more info: http://dssg.uchicago.edu • Or email: [email protected]

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Data Science in

the Curriculum with

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The Data Deluge

• Big Data education suffers from similar

challenges.

How do we help

students drink from

the fire hose?

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Big Data and the Curriculum

• Big Data is putting pressure on the curriculum

– Not just CS/ECE: Business, Finance, Social Science, Economics, Biology, Medicine, Public Policy

• NIH has held several meetings on Big Data

education.

– Wants to integrate

Big Data/Data Science into the regular curriculum.

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NIH Conclusions

• Teach from case studies

– Proper training should include hands on experience with real data.

– Use and study of cutting edge:

• Tools

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• Teach from case studies

– Proper training should include hands on experience with real data.

– Use and study of cutting edge:

• Tools

• Techniques

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NIH Conclusions

• Train Data Scientists to work as team

members.

– The team is one of the most important parts of real data science applications.

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New Ways of Thinking

• Get students used to the pace of change,

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New Ways of Learning

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Active Learning

• After 2 weeks we tend to remember:

– Passive learning

• 10% of what we read

• 20% of what we hear

• 30% of what we see

• 50% of what we hear and see

– Active learning

• 70% of what we say

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Bloom’s Taxonomy

Evaluation

Synthesis

Analysis

Application

Comprehension

Knowledge

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Three Pronged Approach

• Reading, presenting,

and discussing

current state of the

art.

• Hands on study with

real data.

• Original research in

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Creating a Classroom Around a

Digital Observatory

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Transitioning a DSSG like

Environment to the Year

• Identify a smaller number of partners to work

with larger groups on a longer time scale.

• Understand that our expectations need to be

tempered

– Summer – exclusive, competitive program with international recruitment

– Year – drawn from, admittedly excellent, student body at large, motivation may be lower.

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Frontiers of Data Science Class

• Several published papers resulting from the

class.

• Mixed undergrad and graduate,

interdisciplinary environment.

• Awarded Frontiers of

Engineering Education by

the NAE

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Growth of the Course

• First year

– 8 students

– Electrical Engineers, Computer Engineers,

Computer Scientists, Environmental Scientists, Economists

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Growth of the Course

• First year

– 8 students

– Electrical Engineers, Computer Engineers,

Computer Scientists, Environmental Scientists, Economists

• Second year

– 14 students

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Developing Scalable Infrastructures

• Understand the financial limitations of the

classroom

• Develop resources which can be leveraged for

research and curriculum, a practical

curriculum based on real experience will have

similar needs anyway!

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The Need for Practice in the

Academy

• We need to train ourselves in Data Science to

teach it.

– Many faculty haven’t had real industrial experience with Data Science.

– The field and practice is changing fast.

– Encourage the development of Data Science

workshops, boot camps, and summer programs for faculty as well as students.

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