Masanao Yajima,
Director of MSSP Consulting
Associate Professor of the Practice,
Department of Mathematics & Statistics,
Boston University

Statistics Practicum:

Placing 'Practice' at the Center ofData Science Education
Joint work with

Eric Kolaczyk andHaviland Wright.

https://www.datascienceprograms.org

Data Science (DS) Education Program Design▪

Traditional DS program

Practice

ComputingOr

Practice

MethodMethodTheoryTheoryComputingData Science (DS) Education Program Design▪

Traditional DS program

プログラミング

Computing

Practice

ComputingOr

Practice

Paradigm
Shiftパラダイムシフト

MethodMethodTheoryTheoryComputing実践

Practice
Theory理論

Method方法卵が先？それとも鶏？船越義珍

本部朝基

Form orPractice?
型か実践か？

http://www.fightingarts.com/reading/article.php?id=710

Our core belief:理論原則

実践

How we make it work in the university settingSince 2015

MSSP@BU
StatisticalConsulting
ServiceComputing

External
Partner
Projects

明日のデータサイエンティスト

今日の生徒

Practicum
Today’sStudents

TheoryMethodTomorrow’s
Data Scientists
image: Flaticon.com

MS in Statistical Practice (MSSP)
A Practice Centric Statistics Focused Data Science Program§ Cohort based (~45-60 students), diverse background§ Duration: 9 month - 1.5 year
§ 8 Courses (32 credit)§§§§MA675/676 Statistical Practicum I & II
MA678/679 Statistical Modeling / Machine Learning
MA677 Foundation of Statistics
3 Electives§ MA615 Data Science in R, etc§ Additional offerings:§ 2 Week Bootcamp§ Career Seminars§ Stats@Work Seminars§ Graduation requirement: Portfolio

ComputingMA615, etc

Practicum
MA675/676
TheoryMA677

Method
MA678/679

Practice Centric: Interwoven with Practical Experience
External Partner Project 1Projects

External Partner Project 2

Fall Consulting

Spring Consulting

Practicum (Statistical Process)Practicum (At scale)

Consulting
Practicum

MA675

MA676

Computation (DS with R)Computing
MethodSelf Learning (Python)

MA615

MA676 lab

Method (Statistical Modelling)

Method (Machine Learning)

MA678

MA679

Foundations (Statistics Theory)

TheoryMA677

Bootcamp PhD TF Training
AugSep

Oct

Nov

Fall Semester

DecJanFeb

Mar

Apr

Spring SemesterMay

SummerPractice Centric: Interwoven with Practical Experience
External Partner Project 1

Projects

External Partner Project 2

Fall Consulting

Spring Consulting

Practicum (Statistical Process)Practicum (At scale)

Consulting
Practicum

MA675

MA676

Computation (DS with R)
Computing
MethodSelf Learning (Python)

MA615

MA676 lab

Method (Statistical Modelling)

Method (Machine Learning)

MA678

MA679

Foundations (Statistics Theory)

TheoryMA677

Bootcamp PhD TF Training
AugSep

Oct

Nov

Fall Semester

DecJanFebMarAprSpring Semester

MaySummer

Practice Centric: Interwoven with Practical Experience
External Partner Project 1

Projects

External Partner Project 2

Fall Consulting

Spring Consulting

Practicum (Statistical Process)Practicum (At scale)

Consulting
Practicum

MA675

MA676

Computation (DS with R)
Computing
MethodSelf Learning (Python)

MA615

MA676 lab

Method (Statistical Modelling)

Method (Machine Learning)

MA678

MA679

Foundations (Statistics Theory)

TheoryMA677

Bootcamp PhD TF TrainingAugSep

OctNov

Fall Semester

DecJanFebMar

AprSpring Semester

MaySummerBootcamp▪

▪

▪

Two Weeks of intense program□ Basic R□ Probability and Statistics□ Linear Algebra
Multiple activities□ Paired introduction□ Team building exercises□ Social events
Purpose□ Pre-test and post-test to assess student performances.
□ Team exercises to identify communication and group work performance.
□ Set up the students so that they can hit the road running.

Practice Centric: Interwoven with Practical Experience
External Partner Project 1

Projects

External Partner Project 2

Fall ConsultingSpring Consulting

Practicum (Statistical Process)Practicum (At scale)

Consulting
Practicum

MA675

MA676

Computation (DS with R)
Computing
MethodSelf Learning (Python)

MA615

MA676 lab

Method (Statistical Modelling)

Method (Machine Learning)

MA678

MA679

Foundations (Statistics Theory)

TheoryMA677

Bootcamp PhD TF Training
AugSep

Oct

Nov

Fall Semester

Dec

JanFebMarAprSpring SemesterMay

SummerPractice Centric: Interwoven with Practical Experience
External Partner Project 1

Projects

External Partner Project 2

Fall Consulting

Spring Consulting

Practicum (Statistical Process)Practicum (At scale)

Consulting
Practicum

MA675

MA676Computation (DS with R)
Computing
MethodSelf Learning (Python)

MA615

MA676 lab

Method (Statistical Modelling)

Method (Machine Learning)

MA678

MA679

Foundations (Statistics Theory)

TheoryMA677

Bootcamp PhD TF Training
AugSep

Oct

Nov

Fall Semester

Dec

JanFebMar

Apr

Spring SemesterMay

SummerStatistical Practicum -- Where it all comes together.
§ Team taught (3 Faculties, 3 PhD TFs and 1 MS TA)§ Run as a ”regular class”§ In person class (1h15m * 2)
§ Discussion section (technical topics)§ Lab (50m) + On call consulting

Pedagogy§ MA675: Statistics Focused Data Science Life Cycle§ MA676: Statistician in Broader Data Science§ Three Pillars of the practicum§ Pedagogy (Weekly readings, discussion, quiz)§ Consulting Projects§ External Partner Project

StatisticalConsulting

Practicum

ExternalPartnerProjects

Pedagogy▪
▪
▪
▪

Weekly reading
Reading quiz
Discussion
Homework assignments□ Target Resume□ Writing□ Networking

Week

11

Assessing and reporting statistical results.

10(Mis)interpretation of Statistical Inferences

6

Statistical Modeling & Inference5

Data: Visualization

4

Data Provenance, Cleaning, manipulation

3

Communication: Speaking & Emails
Communication: Writing

2

Data: Types and Sources
Matching Questions with Analyses

1

The Project Lifecycle

1

What Is Statistics?

Pedagogy▪
▪
▪
▪

Weekly reading
Reading quiz
Discussion
Homework assignments□ Target Resume□ Writing□ Networking

Week

11

分析の発表方法

10

分析の理解

6

統計モデル、推論

5

データ：

4

データ： 出所の考慮、クリーニング 、操作3

コミュニケーション：会話、E-mailの書き方

視覚化

コミュニケーション：文書の書き方
2

データ：様々なデータと特徴
質問と分析を一致させる

1

プロジェクトライフサイクル

1

統計って何？

Consulting ≈ Residency Program

Faculty
Statisticians• Consulting service provided to BU research community.
• Every Thursday class is a Consulting day.

PhD StudentTFs• MS students + PhD TF execute the work.
• Roughly 15 students per TF meet during a lab• Students are expected to be “on call” for meetings.

MS StudentConsultants• Three levels of service
1.“One and Done”: at most one hour of consultation
2.Limited Duration (LD): 10~20 hours of student time
3.Extended Duration (ExD): length depends on the project.
Win & Win
MSSP
Student

Expertise
Experience

MSSP
Consulting

ResearchAnalysis

BU
Research
Community

MSSP Consulting: How it works
Typical Client Experience

MSSP Consultant Experience

OnlineRequestIntakeMeeting

TeamAssignment
Agreement
Form

Agreement

Weekly Update
Collaboration
Weekly Lab

Analysis andcode

Report andpresentation

Faculty Supervision
Filtering
Matching

Scoping
Educational
Feedback
DetailChecking
Final Check

Counseling Psychology

Impact (~100 projects / per year)

Exploration of large-scale
study on violence exposure
for LGBTQ population

Genetic Counseling Program

SED

Analysis of genetic test
choice for BMC patients

9%

MED

MED
19%

SED
9% COM

19%

Biology
Analyses ofbiogeochemistry
experiments and CO2
emission models

CAS
50%

CAS
Occupational Therapy

50%ENG

OtherSPH
STH

Other

Etc
4%

SSW
3%

Environmental Health
Assessment of particulatematters around Logan
Airport

4%

7%

ENG
4%

QST

4%`SSWSPH
STHEtc
4%MSSP

teaching young individuals
how to resolve environmental
barriers to participation

COM

7%

3%
MSSP

QST

Social Research4%
Study to improve themental and sexual healthof Asian American Women

External Partner Project▪
▪
▪

▪

R&D for the partners; guided internship experience.
One semester long , student team supervised by a Faculty.
Real experience;□ business context,□ direct (constructive) feedback, and□ real (messy) data
What we deliver□ Code/software,□ written report,□ detailed explanation of the idea.

Current and Competed MSSP Partner Projects

BOSTON PUBLIC SCHOOLSAgreement• Data Security• Limited Liabilityeer
AgnemtExternalPartner• Risk assessment• Project Scoping• Cost Handling
PersonProject

Student Educational
Project Agreement

Student

Controlled Access

Data

Unlimited Access

IP
Disclosure control

https://www.bu.edu/researchsupport/forms-policies/corporate-student-engagement-in-the-classroom-guidelines-for/Since 2015

MSSP@BU
StatisticalConsulting
ServiceComputing

ExternalPartner
Projects

明日のデータサイエンティスト

今日の生徒

Practicum
Today’sStudents

TheoryMethod

Tomorrow’s
Data Scientists
image: Flaticon.com

Example Student Experience
External Partner ProjectSep 23 - Dec 7

Public School Enrolment Projection

ALS/MS Medicare Claims Project75 days

Fall2018

Sep 25diveecfe
kof a R
Kic DatSep

Sep 28

ApEDiontatneresOct 25

Octr
nstee
tioamnteoendsatires
Mi sent
PalpreFin

Nov 20Nov

Dec 4Dec

Jan86 days

Spring
2019

f
kofci
KFeb 8Feb

LGBTQ Violence Exposure
Bacteria Growth
Oct 10 –Dec 5

Winterintersession

Parents and Genetic Counselling for ASD
Oct 22 - Feb 15

Mar 1diveec
Remtaaeri
DIntntioatsen
preMar 7MarMay 5

AprBone Hacking

Sep 24 - Dec 5ConsultingpalFin

Feb 8 - May 5

Feb 26 - May 8iontatneres

MayGraduation

May 22

Secret Sauce?
▪
▪
▪
▪
▪
▪
▪

Reality: real client, real project, real interest
Culture of Trust: Bootcamp, Cohort, Work together
Communication (faculty, TFs, students, clients)
Just In Time (JIT) teaching & training, Staying Agile.
Manage expectations and strive to provide value.
Instructors are put under pressure in both teaching and management.
Quantity
Class RoleConsulting
Human resource:Professors

3+Research Faculty

1+PhD TF

3+TA(1)++++Other things we did not go over▪
▪
▪

MA677 (Foundations of Statistical Practice)□ Look back theory. Why does it work? Not how it should be.
Practicum Labs and self guided programming trainings□ Kept open for just in time (JIT) learning
Deal with demand fluctuations for projects.

Challenges andFuture

MSSP in COVID19• Learn From Anywhere (LfA)• Weekly Testing• Socially Distanced Seating• Attendance Monitor App• 52 Students in at least 4 time zones• Total of 12 hour time zone differences• Heavy use of technology• Zoom, MS Teams, Flipgrid, etc• Fall 2020
• 5 Partner projects, 33 Consulting projects• Spring 2021
• 4 Partner Projects

Challenges
▪
▪
▪
▪
▪

Student Assessment□ Peer-Self-Co-Instructor (PSCo) Assessment
Diversity□ “Student's success” for diverse students
Recruiting and Working with Working Professionals□ University and industry schedule miss match
Staying agile but also staying principled□ Fast-changing landscape of Data Science
Scaling to a larger cohort with inclusivity

Before MSSPWe have data but notime or resource toinvest

We need statistical
help for papers
and grants

Company
Collage of Arts
and Sciences

Other Schools &Departments

Government
Department ofMathematics
and Statistics

NPO/NGO

Statisticians are
expensive andhard to house

There is no where to
get practical training

We don’t know whereto start
DS JobOpportunities

BA
Statisticians are not
DS candidates…

MAPhD

After MSSP
External Partner
ProjectCompany
Collage of Arts
and Sciences

Other Schools &Departments

Government

NPO/NGODS Job
Opportunities

Department ofMathematics
and Statistics

BAMSSP
Consulting
Experience

MSSPMAPhD

Special Thanks▪

Collaborators

Eric Kolaczyk▪

Haviland WrightMembers of MSSPLuis Carvalho

Sean Grogg

Please feel free to reach out if you
have any question.
Masanao Yajima
yajima@bu.edu
More Information athttps://www.bu.edu/mssp/

HDSR paper
https://hdsr.mitpress.mit.edu/pub/twyc748y/release/2

https://www.bu.edu/researchsupport/forms-policies/corporate-student-engagement-in-the-classroom-guidelines-for/Agreement TemplateAcknowledgement§ The pictures in the slides are from https://unsplash.com/§ Icons are from Slidesgo