Faculty, Accelerated MS in Data Science | Northwestern University School of Professional Studies - Northwestern School of Professional Studies

Miller, Thomas W. 2020. “Measurement, Meaning, and Prediction in Sports,” In C. Ley and Y. Dominicy (eds.), Science Meets Sports: When Statistics are More than Numbers, Cambridge, U.K.: Cambridge Scholars Publishing.

Miller, Thomas W. 2016. Sports Analytics and Data Science: Winning the Game with Methods and Models, Old Tappan, N.J.: Pearson Education/FT Press.

Miller, Thomas W. 2015. Marketing Data Science: Modeling Techniques in Predictive Analytics with R and Python, Upper Saddle River, N.J.: Pearson Education/FT Press.

Miller, Thomas W. 2015. Web and Network Data Science: Modeling Tech- niques in Predictive Analytics, Upper Saddle River, N.J.: Pearson Education/FT Press.

Miller, Thomas W. 2015. Modeling Techniques in Predictive Analytics with Python and R: A Guide to Data Science, Upper Saddle River, N.J.: Pearson Education/FT Press.

Miller, Thomas W. 2015. Modeling Techniques in Predictive Analytics: Busi- ness Problems and Solutions with R (Revised and Expanded Edition), Upper Saddle River, N.J.: Pearson Education/FT Press.

Miller, Thomas W. 2014. Modeling Techniques in Predictive Analytics: Busi- ness Problems and Solutions with R, Upper Saddle River, N.J.: Pearson Education/FT Press.

Tully, Shawn. How a data scientist beat the polls by nailing the Georgia senate race. Fortune, January 9, 2021. https://fortune.com/2021/01/09/data-science-polling-us-senate-race/

David K. Hardin Award, American Marketing Association, Best article in

MSDS 453 Natural Language Processing

MSDS 459 Knowledge Engineering

MSDS 460 Decision Analytics

MSDS 411 Unsupervised Learning Methods

Data Analytics, Ethics in AI (and data), AI for Good, Organizational Leadership, Diversity and Inclusion, Social and Industrial/Organizational Psychology, Sustainability, Entrepreneurship, and Social Entrepreneurship

PhD in Organizational Leadership, Concordia University Chicago
MS in Information and Knowledge Strategy, Columbia University in the City of New York
EMBA, Quantic School of Business and Technology, Washington DC
BS, MS Computer Science, Bahria University, Pakistan Harvard CORe. Business Analytics, Economics, and Accounting
Certified SAFe 5 Agilist
AWS Certified Cloud Practitioner
Microsoft Certified Azure Fundamentals
Certified Celonis Sales Professional
Teradata Certified Master (six exams)
Certified Engagement Manager (Capgemini internal)

Northwestern University (Fall 2018–Present), School of Professional Studies, Lecturer, Master of Data Science
University of Chicago (Fall 2020–Present), School of Continuing Liberal and Professional Studies. Lecturer, Master of Science in Analytics
Columbia University (Spring – Summer, 2017), School of Professional Studies. Lecturer, Master of Applied Analytics
Dataiku. Strategic Customer Success Manager. 2021 – Present
Capgemini Financial Services Insights and Data. Portfolio Manager. 2020 - 2021
Information Resources Incorporation (IRi). Director Technology Data Engineering. 2017 – 2020
Capgemini Financials. Manager Insights and Data. 2009 – 2017
Teradata Global Consulting Center. Professional Services Consultant (Data Engineering). 2005 - 2009

MSDS 420 Database Systems and Data Preparation 

MSDS 432 Foundations of Data Engineering

MSDS 480 Business Leadership and Communications

Applied linear algebra, Combinatorics, Data science, Image reconstruction in discrete tomography and computerized tomography

PhD in Mathematics, The Graduate School and University Center of the City University of NY

MS in Mathematics, City College of the City University of NY

Adjunct Lecture (2015 – Present)
School of Professional Studies, Data Science Program, Northwestern University, Evanston, IL

Online Mathematics Instructor (2012 – 2018)
Schools include American Public University System, Georgia Military College, Ottawa University, Southern New Hampshire University, and Thomas Edison State University.

Software Test Engineer (2007 – 2009)
Princeton Server Group/Telvue Corporation, Princeton, NJ

Assistant Professor Computer Science (2001 – 2006)
Francis Marion University, Florence, SC

Assistant Professor of Mathematics and Computer Science (1998 – 2001)
Georgia Southern University, Statesboro, GA

“A New Approach for Understanding the Journal Impact Factor”, with F. Arroyo and F. Ling, submitted for publication to PLOS ONE, April 2021
“Error Analysis of Reweighted l_1 Greedy Algorithm for Noisy Reconstruction”, with J. Zhu, X. Li and F. Arroyo, Journal of Computational and Applied Mathematics, 286, pp. 93-101, 2015
“The Convergence of the Block Cyclic Projection with an Overrelaxation Parameter for Compressed Sensing Based Tomography”, with F. Arroyo, X. Li and J. Zhu, Journal of Computational and Applied Mathematics, 280, pp. 59-67, 2015
“The Convergence of Block Cyclic Projection with Underrelaxation Parameters for Compressed Sensing Based Tomography”, with F. Arroyo, X. Li and J. Zhu, Journal of X-Ray Science and Technology, 22:2, pp. 197-211, 2014
“Numerical Studies of the Generalized l_1 Greedy Algorithm for Sparse Signals”, with F. Arroyo, X. Li and J. Zhu, Advances in Computed Tomography, Vol. 2, No. 4, 2013

MSDS 400 Math for Data Scientists

MSDS 420 Database Systems and Data Preparation

MSDS 430 Python for Data Science

MSDS 458 Artificial Intelligence and Deep Learning

Interested in the design and development of parallel and real-time data-intensive applications and multithreaded platforms.

PhD in Computer Science, Illinois institute of Technology

Northwestern University (Chicago, IL USA) - Adjunct Faculty (2002 – Present)
Illinois Institute of Technology (Chicago, IL USA) - Adjunct Faculty (1997 – Present)
DePaul University (Chicago, IL USA) - Adjunct Faculty ( 2001 – Present)
Alcatel-Lucent (Chicago, IL USA) – Engineering ( 1997– 2008)

[1] Shubhanan Bakre, Atef Bader and Tzilla Elrad. Aspect Interaction Chart - A UML Approach for Modularizing Aspect Interaction Conflicts. Accepted to Requirements Engineering track at the 28th ACM Symposium on Applied Computing 2013.
[2] Atef Bader, Shangping Ren, and Husein Armouti, "Delta Changes for OO Software Adaptation: Pervasive or Invasive?," Proceedings of the 38th Hawaii International Conference on System Sciences - Adaptive and Evolvable Software Systems: Techniques, Tools and Applications, 2005
[3] Mark Mahoney, Atef Bader, Tzilla Elrad, Omar Aldawud, “Using Aspects to Abstract and Modularize Statecharts,” The 5th Aspect-Oriented Modeling Workshop in Conjunction with UML 2004 Lisbon, Portugal, October 2004
[4] Atef Bader, Husein Armouti, Shangping Ren, Tzilla Elrad “Components Weaving for Software Product Features,” Managing Variabilities Consistently in Design and Code as part of OOPSLA’04, Vancouver, Canada, October 2004
[5] Paul Adamczyk, Atef Bader, and Tzilla Elrad “Quantitative Pattern system,” EUROPLOP 2002.
[6] Tzilla Elrad, Omar Aldawud , and Atef Bader, “Aspect Oriented Modeling - Bridging the Gap Between Design and Implementation,” Proceedings of the First ACM SIGPLAN/SIGSOFT International Conference on Generative Programming and Component Engineering (GPCE 2002), Pittsburgh, PA, Oct. 2002.
[7] Omar Aldawud, Tzilla Elrad, and Atef Bader “A UML Profile for AOP,” Workshop on Aspect Oriented Programming, OOPSLA 2001.
[8] Boris Vaysburg, Luay H. Tahat, Atef Bader, Bogdan Korel, “Automating Test Case Generation from SDL Specifications,” In the 18th International Conference and Exposition on Testing Computer Software, Washington, D.C., June 2001.
[9] Aldawud,Elrad, and Bader “Automatic code generation using an Aspect Oriented Framework,” In Proc. of the ECOOP'2001 Workshop on Automating Object-Oriented Software Development Methods (AOOM'01). Hungary, June 2001
[10] Luay H. Tahat, Boris Vaysburg, Bogdan Korel, Atef Bader, “Requirement-Based Automated Black-Box Test Generation,” 25th International Computer Software and Applications Conference (COMPSAC2001). Chicago, IL, USA, Oct 2001
[11] Aldawoud O., A. Bader, C. Constantinides, and T. Elrad, “Modeling Intra object Aspectual Behavior,” ICSE Workshop on Describing Software Architecture with UML. 23rd International Conference of Software Engineering, May 2001
[12] Omar Aldawud, Professor Tzilla Elrad, and Atef Bader, "Aspect-Oriented Modeling to Automate the Implementation and Validation of Concurrent Software Systems", Specification, Implementation and Validation of Object-oriented Embedded Systems (SIVOES'2001) Workshop, ECOOP 2001.
[13] Akkawi F., Bader A., Elrad T., “The Multi-Layered Approach To Building Intelligent Systems,” International Symposium on Information Systems and Engineering., Monte Carlo Resort, Las Vegas, Nevada, USA, June 2001.
[14] Akkawi F., Bader A., Elrad T., “Data Mining Techniques For Building Intelligent Agent-Based Systems,” 2001 International Conference on Artificial Intelligence, Monte Carlo Resort, Las Vegas, Nevada, USA, June 2001.
[15] Akkawi F., Bader A., Elrad T., “Building Agent-based Intelligent Concurrent Systems,” International Conference on Artificial Intelligence and Soft Computing, Cancun, Mexico, May 2001.
[16] Akkawi F., Bader A., Elrad T., “Framework for Adaptability using Intelligent Agent,” International Conference on Intelligent Agents, Las Vegas, USA, July 2001
[18] Akkawi F., Bader A., Elrad T., “Aspect-Oriented Technology for Business Applications: A case study in stock Trading,” Workshop on Knowledge Extraction for Enterprise Services , 12th International Workshop on Database and Expert Systems Applications (DEXA 2001), Munich, Germany, September 2001.
[19] O. Aldawoud, A. Bader, E. Tzilla , “Automatic Code Generation using an Aspect Oriented Framework,” ECOOP’2001, Automating Object-Oriented Software Development Methods Workshop, June 2001, Budapest, Hungary.
[20] Atef Bader, Constantinos Constantinides, Tzilla Elrad, Paniti Netinant, “Building Reusable Concurrent Software Systems, ”Conference on Distributed Objects in Computational Science, Held in Conjunction with the PDPTA Series of Conferences. Las Vegas, Nevada, June 2000.
[21] Paniti Netinant, Constantinos Constantinides, Atef Bader, Tzilla Elrad. “Supporting the Design of Adaptable Operating Systems Using Aspect-Oriented Frameworks,” In International Conference on Parallel and Distributed Techniques and Applications (PDPTA'2000) special session on Aspect-Oriented Programming, Las Vegas, Nevada. June 2000.
[22] Constantinos A. Constantinides, Atef Bader, Tzilla Elrad, Mohamed Fayad, Paniti Netinant, “Designing an Aspect-Oriented Framework in an Object-Oriented Environment,” ACM Computing Survey, 32(1), March 2000.
[23] Constantinos A. Constantinides, Atef Bader, Tzilla Elrad,. “A Two-Dimensional Composition Framework to Support Software Adaptability and Reuse, “ In Proceedings of the International Conference on Software Reuse (ICSR6), Vienna, Austria. June 2000.
[24] Bader, A., and Elrad, T., “Groupware: Framework for interacting Distributed Objects,” Proceedings of the International Conference on Parallel and Distributed Computing and Systems , Boston, MA, Nov. 1999.

Guest Editor for special issue of Communications of the ACM on Aspect-oriented programming. (Oct. 2001)

MSDS 420 Database Systems and Data Preparation

MSDS 430 Python for Data Science

MSDS 432 Foundations of Data Engineering

MSDS 440 Real-­Time Interactive Processing and Analytics

MSDS 442 Real-­Time Stream Processing and Analytics

MSDS 403 Data Science in Practice

MSDS 422 Practical Machine Learning

MSDS 434 Analytics Application Engineering

MSDS 436 Analytics Systems Engineering

MSDS 462 Computer Vision

MSDS 464 Intelligent Systems and Robotics

Computational Statistics, Generalized Linear Models, Consumer and Behavioral Modeling

PhD in Statistics (Computational Statistics and Financial Econometrics), Rice University
MS in Applied Mathematics, University of Dayton
BSBA in Economics and Finance with a minor in Mathematics, Xavier University

Principal Data Scientist, Citizens Bank (2016 – Present)
Northwestern University, Faculty in MS in Predictive Analytics Program, MS in Data Science Program (Sept 2011 – Present)
Principal Statistician, JPMorgan Chase (2008-2016)
Tulane University, Department of Mathematics
Rice University, Department of Statistics

[1] “The Birnbaum-Saunders Autoregressive Conditional Duration Model”, Chad R. Bhatti, Mathematics and Computers in Simulation Vol 80, Issue 10, March 2010 pp. 2062-2078.

[2] “On the Interday Homogeneity in the Intraday Rate of Trading”, Chad R. Bhatti, Mathematics and Computers in Simulation Vol 79, Issue 7, March 2009 pp. 2250-2257.

[3] “Intraday Trade and Quote Dynamics: A Cox Regression Analysis”, Chad R. Bhatti, Mathematics and Computers in Simulation Vol 79, Issue 7, March 2009 pp. 2240-2249.

[4] “Conditional Probability and HIV Testing: A Real World Example”, Chad R. Bhatti and Jennifer L. Wightman, The American Statistician Vol 62, No 3, August 2008 pp. 238-241.

MSDS 410 Supervised Learning Methods

MSDS 454 Applied Probability and Simulation Modeling

MSDS 460 Decision Analytics

My teaching approach is traditional for the types of courses that I teach in the MSDS program – applied statistical modeling (including generalized linear models, statistical learning, and machine learning) with a strong statistical computing component (computing with data in a statistical computing language such as R). In these courses we take a standard three prong approach to integrating statistical concepts, statistical computing, and the modeling process. Models need to be relevant, correctly built, and implementable into production systems. How do we do that? That’s what we learn in the Analytics & Modeling Specialization.

How do we do that? – Let’s take a moment to break that down.

How do we learn concepts? - It’s hard to apply a concept correctly if we do not know what it is. Concepts are hard for beginning students to understand so I like to use video lectures to explain important concepts to students, just like I would in a brick-and-mortar classroom.

How are we applying these concepts? - Using a statistical computing language like R. Most of our students have a limited understanding of R so we develop our understanding of R through tutorial videos with sample code.

How do we learn how to build models? – By building models under supervision. My assignments are designed to constructively teach students to build models under my supervision. These are not mindless activities or busy work. You will engage in the modeling process inside my classroom under my supervision with the help from my teaching assistant. To be clear – what we are learning cannot be learned from a blog, MOOC, etc. You are learning how to build models from a professional modeler, not a code snippet here and a code snippet there. Code snippets are not modeling.

Over the history of the program I have developed a lot of modelers. I enjoy developing modelers. Typically I get the ‘greenhorn’ students in an introductory course where we focus on developing their modeling capabilities through the development of modeling fundamentals. Then, I continue to extend that development through the Analytics and Modeling specialization by having the students back in more advanced courses, and finally the capstone course. I like to have the students two to three times before they leave the program. It gives them a chance to develop, and me a chance to get to know them.

Northwestern University
Kellogg School of Management Evanston, IL
Master of Management (Information Systems and Quantitative Methods)
Northwestern University
Robert R. McCormick School of Engineering Evanston, IL
Bachelor of Science (Computer Science)
(completed engineering degree requirements while enrolled in Kellogg School of Management )

Baumgartner Associates LLC Naperville, IL
Management consulting firm providing IT strategy, business process management,
transformation management, and program management services.

Dynamical systems, social network analysis, social media data analysis, digital humanities, and computational social science

Ph.D., Johns Hopkins University, Department of Mechanics. Dissertation title: "Stability of Rimming Flows.”
Diploma, National Technical University of Athens, School of Chemical Engineerin

Northwestern University
2018–:
Adjunct Instructor for the Master of Science in Data Science Program

2018–:
Affiliated faculty at the Science of Networks in Communities (SONIC)

New York University Abu Dhabi (NYUAD)
2019–2020:
Visiting Professor, NYUAD Science Division, Program of Mathematics

University of Patras
1998–2017:
Professor, Department of Mathematics

Democritus University of Thrace at Xanthi
1982–1998:
Associate Professor, Department of Electrical & Computer Engineering

Visiting Positions
1990–91:
Visiting Professor, Department of Mathematics, University of California at Irvine
1981–82:
Post-Doctoral Fellow, Laboratory of Numerical Analysis, University Paris VI (Pierre et Marie Curie), France
1980–81:
Post-Doctoral Fellow, Department of Mathematics, University of Florence, Italy

Hasheminezhad, R., Boudourides, M., & Brandes, U. (2020). Scale-free networks need not be fragile.
Submitted to ASONAM 2020 on Apr 20, 2020, https://github.com/mboudour/var/blob/
master/HasheminezhadBoudourides%26Brandes_Scale-FreeNetworksNeedNotBeFragile.
pdf

Tsakonas, G., Lenis, S., & Boudourides, M. (2020). How open is “open”? A bibliometric study on the literature of Open Science and Open Access. Invited to be publishes as a feature paper in Publications
https://github.com/mboudour/var/blob/master/TsakonasLenis%26Boudourides_HowOpenIs%
22Open%22%3F_ABibliometricStudyOnTheliteratureOfOpenScience%26OpenAccess.
pdf.

Balaska, V., Bampis, L., Boudourides, M., & Gasteratos, A. (2020). Unsupervised Semantic Clustering and Localization for Mobile Robotics Tasks.
Robotics and Autonomous Systems, 131, September 2020, 103567. https://doi.org/10.1016/j.robot.2020.103567.

Siapera, E., Boudourides, M.A., Lenis, S., & Suiter, J. (2018). Refugees and Networked Publics: Affect and
Capture. Social Media + Society , 4 (1): 1–21, https://doi.org/10.1177/2056305118764437.

Papachristopoulos, L., Tsakonas, G., Boudourides, M., Sfakakis, M., Kleidis, N., Papatheodorou, C., &
Lenis, S. (2017). Discovering the Structure and Impact of the Digital Library Evaluation Domain.
International Journal of Digital Libraries, https://doi.org/10.1007/s00799-017-0222-x.

Boudourides, M.A., & Lenis, S.T. (2016). Dominating sets and ego-centered decompositions in social
networks. The European Physical Journal Special Topics 225:6, 1293-1310 https://doi.org/10.1140/epjst%2Fe2016-02673-0

Andretta, M, Botetzagias, I, Boudourides, M.A., Kioufegi, O., & Yang. M. (2009). Novel Characteristics of the GJMs: A (latent) network approach. In Donatella della Porta (ed.), Another Europe: Conceptions and Practices of Democracy in the European Social Forums (pp. 149–72). London: Routledge.

Boudourides, M.A. (2009). On Minimal Relations. Nessie 2 (Vespusia, Juillet 2009).

Boudourides, M.A. (2009). The Relational Ontology of Social Network Theories. Nessie 1 (FireWorks, Juin 2009).

Boudourides, M.A., & Botetzagias, I. (2007). Networks of protest in Greece 2002-3. In Derrick Purdue
(ed.), Civil Societies and Social Movements: Potentials and Problems(pp. 109–23). London: Routledge.

Boudourides, M.A., Kioufenzi, O., & Leandros, N. (2006). Political campaigning in new media: The Greek
national elections of March 2004. In N. Leandros (ed.),The Impact of Internet on the Mass Media in Europe (pp. 167–79). Suffolk, UK: Abramis, Arima Publishing.

Robert K. Merton Visiting Research Fellowship from the Institute for Analytical Sociology (IAS) at Linköping University in Sweden.

MSDS 400 Math for Data Scientists

MSDS 430 Python for Data Science

MSDS 452 Web and Network Data Science

MSDS 402 Introduction to Data Science

MSDS 455 Data Visualization

MSDS 485 Data Governance, Ethics, and Law

Ph.D in Management from School of Business, Rutgers University
MBA from the IIM Ahmeadabad, India.

President – Suman Inc.
Consultant at Newbold Advisors
Senior Consultant at Accenture
Director at Capital One
Category Analytics Manager at Kraft Foods
Member of Technical Staff at AT&T Bell Labs

- Chaturvedi, A. D. and Carroll, J. D. (2006). CLUSCALE (“CLUstering and Multidimensional SCAL[E]ing”). A Three-Way Hybrid Model Incorporating Overlapping Clustering and Multidimensional Scaling Structure. Journal of Classification, 23, 2, 269-300.
- Carroll, J. D., Arabie, P. A., Chaturvedi, A. D., and Hubert, L. A. (2004). Multidimensional Scaling and Clustering in Marketing: Paul Green’s Role,” in Market Research and Modeling: Progress and Prospects: A Tribute to Paul Green, Eds., Yoram Wind and Paul Green, Kluwer Academic Publishers: Netherlands, 71-102.
- Chaturvedi, A. D., Green, P. E., and Carroll, J. D. (2001). K-Modes Clustering. Journal of Classification, 18, 35-56.
- Chaturvedi, A.D, and Carroll, J. D. (1998). A Perceptual Mapping Procedure for Analysis of Proximity Data to Determine Common and Unique Product-Market Structures. European Journal of Operational Research, 111, 268–284.
- Carroll, J. D., and Chaturvedi, A. (1998). K-Midranges Clustering. In A. Rizzi, M. Vichi & H. H. Bock (Eds.), Advances in Data Science and Classification (pp. 3–14). Berlin: Springer.
- Chaturvedi, A.D, and Carroll, J. D. (1997). An L1-Norm Procedure for Fitting Overlapping Clustering Models to Proximity Data. In Y. Dodge (Ed.), L1-Statistical procedures and related topics (31, 443-456). Hayward, CA: IMS [Institute of Mathematical Statistics] Lecture Notes - Monograph Series [LNM].
- Chaturvedi, A.D, Carroll, J. D., Green, P. E., and Rotondo, J. A. (1997). A Feature-Based Approach to Market Segmentation via Overlapping K-Centroids Clustering. Journal of Marketing Research, XXXIV, 370–377.
- Chaturvedi, A. D., and Green, P. (1995). Software Review: SPSS for Windows CHAID 6.0, Chicago: SPSS, Inc., 1992. Journal of Marketing Research, Volume 32, pages 245-254.
- Carroll, J. D., and Chaturvedi, A. (1995). A General Approach to Clustering and Multidimensional Scaling of Two-way, Three-way, or Higher-Way Data. In R. D. Luce, M. D'Zmura, D. D. Hoffman, G. Iverson & A. K. Romney (Eds.), Geometric Representations of Perceptual Phenomena (pp. 295–318). Mahwah, NJ: Erlbaum.
- Chaturvedi, A.D, and Carroll, J. D. (1994). An Alternating Combinatorial Optimization Approach to Fitting the INDCLUS and Generalized INDCLUS Models. Journal of Classification, 11, 155-170.
- De Soete, G., Carroll, J. D., and Chaturvedi, A. (1993). A Modified CANDECOMP Method for Fitting the Extended INDSCAL Model. Journal of Classification, 10, 75–92.
BOOK
- Carroll, J. D., and Green, P. E., with contributions from Chaturvedi, A.D. (1997). Mathematical Tools for Applied Multivariate Analysis, Academic Press: San Diego. (ISBN 0121609553 and 0121609545)

• Citations of publication: https://scholar.google.com/citations?user=vO5BAEoAAAAJ&hl=en
• Multiple US Patents [Listed on Linkedin profile]

MSDS 411-DL : Unsupervised Learning Methods
MSDS 422-DL : Practical Machine Learning
MSDS 450-DL : Marketing Analytics

Marketing analytics, cloud architecture, brand loyalty, NLP, healthcare analytics, data governance and ethics, social media analytics, digital transformation strategy, ecommerce

PhD, Consumer Behavior from Purdue University, West Lafayette, Indiana

MA, Economics from UIC, Chicago, Illinois

CEO and co-founder, Core Analytics®, LLC 2003-Current

MSDS faculty and course designer, Northwestern University 2014-Current

VP, Data Science, Horizon Media (Night Market) 2020

Head of Data Science Lab, Cap Gemini 2018-2019

Director, Business Analytics Certificate University of Chicago - Graham School, 2017                                 

Global Black Belt, Advanced Analytics, Microsoft 2016-2017

Technical Sales Engineer, Data Science, Dell 2015

Market Manager Cloud Analytics, Enterprise Architect, IBM 2012-2014 

Database Marketing Analytics Manager, Wellpoint/Anthem 2010-2011

‘Data Mining Twitter for Cancer, Diabetes and Asthma Insights” 2016

BrandMeter®

MSDS 403 Data Science in Practice

MSDS 480 Business Leadership and Communications

MSDS 485 Data Governance, Ethics, and Law

Data visualization, Pricing, Revenue management, Project management

MS, Predictive Analytics, Northwestern University

MBA, Rollins College

BA, Economics, University of Colorado at Boulder

Project Management Professional (PMP)

Lean Six Sigma Green Belt from Lockheed Martin

Restaurant Revenue Management from eCornell

Senior Pricing Product Manager at Chewy, Inc.

Manager, Revenue Management and Data Science at Universal Parks & Resorts

Senior Pricing & Revenue Management Analyst at Disney Parks, Experiences & Products

Financial Analyst at Lockheed Martin Inc.

Senior Banker at US Bank

MSDS 455 Data Visualization

MSDS 474 Accounting and Finance for Technology Managers

MSDS 476 Business Process Analytics

Hello.  My name is Kathryn Daugherty.  I’ve been teaching in the MSDS program since twenty seventeen and primarily teach Data Visualization and Accounting and Finance for Analytics Managers.  I’m also a graduate of this program, so I’m familiar with many of the courses and have experienced balancing professional work with course work.  Thus, I feel I’ve been in your shoes, so-to-speak.

My professional experience started off in banking, transitioned to finance, and now I focus on analytics specific to pricing and revenue management.  I stay up-to-date by following online sources and sites, while also reading any books about data visualization or pricing that I can find.  I also learn from colleagues and my students.

My teaching methods include discussions, presentations, individual assignments and group projects.  Assigned reading and presentations cover foundational lessons that are supplemented with discussions.  I host optional, and recorded, sync sessions throughout the term to highlight key themes and, based on my experience, I also cover areas that are sometimes more challenging.  I’m available for any questions or consultations, as is my Teaching Assistant, and am also open to career development conversations as well.  I like it infuse related current topics  into the courses where appropriate, typically in the form of announcements. 

In Accounting and Finance, foundational terms and statements are covered and then students build out a business case for an analytics start-up of their design.  There is no coding in this data science course; we instead focus on spreadsheets and presentations to communicate financial results.  As this course combines financial accounting, managerial accounting and also finance, three classes often required in an MBA program, there are three texts to cover the essential components of each.

In Data Visualization, principles of visualization best practices, which are tool agnostic, are the primary focus.  Those practices are then employed to create several visualizations, including the creation of a visualization-driven narrative called ‘The Show.’  Python and/or R are utilized and we make Tableau available as well.  This is a very interactive course, but also requires a fair amount of reading – especially the primary textbook.

I enjoy teaching as I like sharing my experiences with others while also learning from my students.  I get excited about these topics and like to share what I’ve learned.  What inspires me most is seeing work throughout the term assembled into final projects that demonstrate a quarter’s worth of learning.  It’s amazing to see how much can be covered in ten weeks.

PhD Applied Mathematics, Northwestern University, 2000

MS Applied Mathematics, Northwestern University, 1995

Technical Training Manager, BCG GAMMA 2018-Present

Analytics Content Manager, Boston Consulting Group 2014 – 2018

Program Advisor and Adjunct Faculty, Certificate in Business Analytics, Northwestern University, 2013 – 2019

MSDS 402 Introduction to Data Science

MSDS 403 Data Science in Practice

Hello, my name is John Derwent and I have been a part of the MS in data science program since 2015.

My course offerings are generally bookends for the program:

• Introductions to the way that data science can be used to improve organizations, be they businesses, not for profits, public sector, NGO or otherwise
• And a capstone focused on analytics leadership expressed through the implementation of data science solutions within the context of a business merger case study.

My academic background is in theoretical and applied mathematics.  Combined with a drive to help others be better, this has taken me through a series of learning and training roles in the tech, software, and consulting industries.  My latest role is building learning solutions for a group dedicated to creating AI and data science solutions to business problems.

Our program is driven by numbers and technology, but I believe that is only a part of what is needed to make data science a successful shaper of the world. Inherent in building solutions is an understanding of the mission, business, organization, and people involved.  The classes that I lead are somewhat outliers in the program in that they focus on that understanding and how data science can be applied in those contexts rather than only the numbers, methods, and technology of data science. This, too, inspires my teaching philosophy: let’s build understanding together.

In class, I present myself more as guide, mentor, and discussion partner pushing my students to gain their own understanding, not as a lecturer imparting knowledge and wisdom.  For me asking questions and having to clearly explain answers are the best way to learn.  This means that you will find me asking those questions, poking your answers, and pushing you to explain more clearly and concisely.  We don’t spend time looking for too many exact answers in my classes.  Instead, together we build structures and practices to gain insights and understanding that can be applied to a multitude of contexts.

Graph Databases, Data Privacy, Data Ethics, Enterprise Information Systems

Doctorate in Computer Science, Colorado Technical University
Emphasis in Enterprise Information Systems

MS in Data Science, Northwestern University

MBA, University of Phoenix

Northwestern University, Evanston, Illinois (August 2018 – Present)
School of Professional Studies
Adjunct Professor
Data Science Program

Professional Association of Diving Instructors (November 1988 – June 2017)
Chief Data Officer (2015 – 2017)
Chief Information Officer (2000 – 2015)
Director, Information Technology (1994-2000)
Senior Programmer/Analyst (1988 – 1994)

Munson Wholesale Sporting Goods (July 1984 – November 1988)
Programmer

MSDS 420 Database Systems and Data Preparation
MSDS 430 Python for Data Science

PhD in Pure Mathematics, Kent State University
BS in Mathematics, Marshall University

Northwestern University School of Professional Studies (2014 – Present), Course Lead for MSDS 400
Coopersmith (2019) Contract Employee for Course Development
Ashland University (2016 – 2020) Contract Employee for Course Development
Southern New Hampshire University (2012 – 2016) Mathematics Adjunct
University of Northwest Ohio (2012 – 2016) Mathematics Adjunct
American Public University System (2010 – 2015) Mathematic Adjunct

MSDS 400 Math for Data Scientists

MSDS 430 Python for Data Science

Ethics in data science, investment strategies, sports and gambling analytics

PhD in Applied Mathematics, Northwestern University

MS in Applied Mathematics, Northwestern University

BA in Physics and Philosophy, Lafayette College

Northwestern University, Evanston, Illinois (June 2011–Present) School of Professional Studies, Assistant Professor of Instruction, Data Science Program

University of Liverpool, Liverpool, England (2002-2010), Teacher of the University

Columbia College, Chicago, Illinois (July 2000-June 2002), Assistant Professor, Department of Science and Mathematics

Booz Allen & Hamilton, Inc. Chicago, Illinois (October 1998 – March 2000)

Senior Consultant, Operations Management Group

A. DiLellio and P. M. Goldfeder, “Is There a Free Lunch in Commission Free ETFs?”, Graziadio Business Review 19, No. 2, 2016.

Goldfeder, Philip M. “Math of the Internet. “Mathematics (Macmillan Science Library). Ed. Barry Mas, Jr. New York: Macmillan, 2002.

Goldfeder, Philip M.; Lynch, Rose Kathleen, “Accuracy and Data Collection, “Mathematics (Macmillan Science Library). Ed. Barry Mas, Jr. New York: Macmillan, 2002.

Goldfeder, P.M.; Volpert, V.A. "A Model of Frontal Polymerization Using Complex Initiation." Mathematical Problems in Engineering 5, 139-160 (1999).

MSDS 400 Math for Data Scientists

MSDS 485 Data Governance, Ethics, and Law

Entrepreneurship, Startups, Personal Data and Security, Behavioural Economics, AI & ML

MS – Economics and International Trade – Louisiana State University
MS – Information Systems and Decisions Science – Louisiana State University
MMS – Birla Institute of Technology and Science

Iceland Venture Studio, Founder and Managing Director, Jan 2019 – Present

Reykjavik University, Course Instructor, Aug 2015 - Present

Startup Iceland, Founder, Jan 2012 - Present

Glitnir Bank, Executive Director and Chief Representative in India, Feb 2006 – Oct 2008

Capgemini, Manager, June 1999 – Jan 2006

Louisiana State University, Graduate Research and Teaching Assistant, Aug 1996 – June 1999

A Smartphone Application for Personalized Approach to Diabetic Eye Disease, Journal of Eye Study and Treatment · Apr 1, 2019

Startup Communities, Wiley Global Finance · Oct 1, 2012

Can Power Law help us avoid the high impact tail events? Rannsóknir Í Félagsvísindum X - University of Iceland Yearly Publication of Scholarly work October 2009

BS in Physics from the United States Military Academy (West Point)

MS in Engineering Management from University of Missouri, Science and Technology

MS in Operations Research from Air Force Institute of Technology

PhD in Operations Research from Air Force Institute of Technology

Northwestern University, Evanston, Illinois (June 2021–Present) School of Professional Studies, Adjunct Professor, Data Science Program

US Army (May 2008–Present)

• Joint Artificial Intelligence Center (August 2021 – Present) AI Engineer and Data Scientist
• Headquarters, Department of the Army (December 2019 – August 2021) Budget/Program Analyst

Kline, Alexander, Darryl Ahner, and Raymond Hill, 2019. “The Weapon-Target Assignment Problem,” in Computers & Operations Research, 105, 226-236

Kline, Alexander, Darryl Ahner, and Brian Lunday, 2019. “Real-Time Heuristic Algorithms for the Static Weapon Target Assignment Problem,” in Journal of Heuristics, 25 (3), 377-397

Kline, Alexander, Darryl Ahner, and Brian Lunday, 2020. “A Heuristic and Metaheuristic Approach to the Static Weapon Target Assignment Problem,” in Journal of Global Optimization, 1-22

Caballero, William, Alexander Kline, and Brian Lunday, 2018. “Challenges and Solutions with Exponentiation Constraints using Decision Variables via the BARON Commercial Solver,” in 2018 IISE Annual Conference Proceedings, 1331-1336

Kline, Alexander, William Caballero, and Darryl Ahner, 2018. “False Optimality of Integer Programs with Exponential Decision Variables,” in 2018 IISE Annual Conference Proceedings, 1873-1877

Kline, Alexander and Darryl Ahner, 2018. “Estimating Parameters of Partially Observable Queues,” in 2018 IISE Annual Conference Proceedings

MSDS 400 Math for Data Scientists

MSDS 460 Decision Analytics

MSDS 453 Natural Language Processing

MSDS 458 Artificial Intelligence and Deep Learning

MSDS 498 Capstone Project

The robustness of commonly used statistical tests and predictive models, the use of automated variable selection procedures in regression, modern re-sampling and non-parametric statistical methods, the teaching and learning of statistics, and the construction of knowledge through modeling quantitative data.

Ph.D., Educational Psychology, University of Wisconsin, Madison, WI (1995)

M.S., Statistics and Operations Research, Michigan State University, East Lansing, MI (1985)

B.A., Mathematics, Saint Olaf College, Northfield, MN (1983)

Assistant Professor of Instruction (faculty), School of Professional Studies, Northwestern University, Evanston, IL (2011–present).   

Associate Professor of Statistics, Department of Mathematics University of Wisconsin-Whitewater, Whitewater (2007- 2019). 

Senior Statistician and Senior Consultant, Chamberlain Research Consultants, Madison, Wisconsin (2003–2007).

Director, Nebraska Evaluation and Research Center, University of Nebraska-Lincoln (2002–2003).

Assistant Professor of Research Methods, Department of Educational Psychology

University of Nebraska, Lincoln (1998–2003)

Assistant Professor of Statistics, Department of Mathematics/Division of Statistics

University of Idaho, Moscow (1995–1998).

Associate Operations Research Specialist, The RAND Corporation, Santa Monica, California (1986–1990).

Mickelson, W.T. (2013).  A Monte Carlo Simulation of the Robust Rank Order-Test Under Various Population Symmetry Conditions.  Journal of Modern Applied Statistical Methods, 12(1), 1-13.

Welch, S.A., & Mickelson, W.T. (2013).  A Listening Competence Comparison of Working Professionals.  International Journal of Listening, 27(1), 85-99.

Mickelson, W.T., & Welch, S.A. (2012).  Factor Analytic Validation of the Ford, Wolvin and Chung Listening Competence Scale.  International Journal of Listening, 26(1), 29-39.

Hankes, J., Skoning, S., Mason, L., Fast, G., Beam, J., Mickelson, W., and Merrill, C. (2011).   Closing the mathematics achievement gap of Native American students identified as learning disabled, Voices of Native American Indian Educators:  Integrating History, Culture and Language to Improve Learning Outcomes for Native American Indian Students. Editor Sheila T. Gregory. University Press of America, Lanham, MD.

Mickelson, W., & Heaton, R. (2004).  Primary teachers’ statistical reasoning about data.  In, Ben-Zvi, D., & Garfield, J. (Eds.), The Challenge of Developing Statistical Literacy, Reasoning and Thinking.  Kluwer Academic Publishers, Netherlands. 

Heaton, R., & Mickelson, W.T. (2002).   The Learning and Teaching of Statistical Investigation in Teaching and Teacher Education.   Journal of Mathematics Teacher Education, 5, 35-59. 

Heaton, R. & Mickelson,W.T., (2002).  Reasoning about data and distribution through the statistical investigations of a third grade classroom.  Statistics Education Research Journal, 1(1), 31-33.

Swearer, S., Song, S., Cary, P., Eagle, J., & Mickelson, W.  (2001). Psychosocial correlates in bullying and victimization:  The relationship between depression, anxiety, and bully/victim status.  In, Geffner, Loring & Young (Eds.), Bulling Behavior:  Current Issues, Research, and Interventions.  Binghamton, NY, Hayworth Maltreatment and Trauma Press.

Sudilovsky, A., Cutler, N.R., Sramek, J.J., Wardle, T., Veroff, A.E., Mickelson, W.T., Markowitz, J., & Repetti, S. (1993).  A pilot clinical trial of the angiotensin-converting enzyme inhibitor ceranapril in Alzheimer disease.  Alzheimer Disease and Assorted Disorders, 7(2), 105-111.

 Veroff, A.E., Cutler, N.R., Sramek, J.J., Prior, P.L., Mickelson, W.T., & Hartman, J.K., (1991).  A new assessment tool for neuropsychopharmacologic research:  The computerized neuropsychological test battery.  Journal of Geriatric Psychiatry and Neurology, 4(4), 211-217.

Teaching Scholars Fellow, University of Wisconsin – Whitewater, 2010

Distinguished Teaching Award, Teachers College, University of Nebraska-Lincoln, 2002

MSDS 401 Applied Statistics with R

MSDS 410 Supervised Learning Methods

MSDS 411 Unsupervised Learning Methods

MSDS 460 Decision Analytics

MSHA 409 Statistical Analysis with R

MSHA 410 Regression and Multivariate Analysis

MSHA 411 Advanced Data Modeling for Health Analytics

Provide expert witness research and consulting. Results are considered proprietary and in some cases work product. Access is limited.

PhD in Statistics from Oregon State University
MS in Mathematics from Utah State University
BS in Math Science from Eastern Oregon University

President & CEO
Melvin Ott & Associates 6/05-Present

• Provide statistical research, predictive modeling and expert testimony to a broad range of clients.

• Teach Data Science courses in the MSDS Program at NU. Core course covers general linear models, regression analysis, logistic regression, exploratory data analysis, cluster analysis, principle components analysis and factor analysis. Python & R programming currently the languages of instruction.

Theses advising for Master’s students:

• “Mining the NFL”, Matthew R Snee, May 2014
• “Analyzing and Aiding Sports Analytics in Television Journalism”, Robert “Edward” Egros, March 2015
• “The Role of the Shutdown Inning in Major League Baseball”, Charles Gray, May 2014
• “Predicting the Future Success of Minor League Catchers Using Classification Algorithms and Quantitative and Qualitative Data” William Young

Adjunct Professor
Gonzaga School of Business, Whitworth College and Eastern Washington University, Spokane, WA 1/03 – 6/12

• Taught Market Research and the Analysis of Demographic Data and Statistical Design in MBA Program and undergraduate.

Statistician Consultant
Company Confidential 6/05 – 12/05

• Researched and wrote two reports regarding lending practices for a large national bank. First report was an aggregate national view while the second report focused on several states. Reports revealed statistically significant differences by race, gender and ethnicity for high rate loans and loan denials.

Director/Consultant
Ingenix, Inc., Salt Lake City, UT 12/03 - 6/05

• Directed and performed several research studies for new product development. Studies utilized modeling with SAS programs running over the web to access Oracle based claims data in the large Ingenix data repository. The study demonstrated the lognormal distribution to assess sample size limits for estimating extreme percentiles of physician charges.
• Designed & implemented SAS interactive testing programs to run over the web that enabled multiple users to conduct the tests.
• Developed inpatient hospital payment models for the design of State Workers Comp payment, in Georgia, Vermont, New Jersey, and Nebraska. Delivered the models and testified in open forums.
• Trained other staff members on SAS Programming & Recovery Services random sampling for claims auditing.

Manager then Director for Data Administration
Sacred Heart Medical Center, Spokane, WA 2/96 - 4/03

• Provided the ongoing Kaplan-Meier survival analysis for the kidney transplant program.
  Projected future demand for hospital services based on statistical, demographic and utilization models.
• Future demand report resulted in a $150M expansion of the medical center.
• Developed clinical quality improvement analysis for a broad range of projects and special studies using statistical tools such as logistic regression modeling, Fishers exact test and others.
• Successfully designed and maintained Hospital Performance Measurement System with standardized reporting and automated statistical significance for a JCAHO Performance Measurement System.
• System was then marketed to other hospitals in the area through Inland Northwest Health Services.

Manager, then Director for Managed Care & Reimbursement
Regence- Blue Shield of Idaho, Lewiston, ID 6/92 - 2/96

• Directed startup operation of a new managed care product called HealthSense.
  Designed & implemented cost containment program for physician and hospital payment.
• Resulted in a net savings of $10M/yr.
• Designed and implemented reimbursement programs for traditional and managed care programs.
  Designed and implemented new reimbursement programs for physicians, and hospitals.
• Payment systems standardized the methodology using RBRVS and DRG’s.
• Hired and trained the analysts to maintain these new programs using SAS for analytics.

Assistant Professor Operations Management - School of Business
Gonzaga University, Spokane, WA 9/87 - 6/92

• Taught MBA and undergraduate courses in statistical decision-making, predictive modeling, sampling, statistical analysis, and linear programming.
• Provided consulting to clients in utility industry, industry associations, city government, and a private law firm.

• Appointed by the governor for the State of Washington, Regional Coordinator for the super-conducting supercollider bid.
• Selected by Member Vote as the Publications Officer for ASA Health Policy Statistics Section 1998-1999.
• Selected to serve on Executive Committee Eastern Oregon University Foundation.
• Appointed by the governor for the State of Washington, Regional Coordinator for the super-conducting supercollider bid.
• Selected by Member Vote as the Publications Officer for ASA Health Policy Statistics Section 1998-1999.
• Selected to serve on Executive Committee Eastern Oregon University Foundation.

MSDS 401 Applied Statistics with R

MSDS 430 Python for Data Science

MSDS 410 Supervised Learning Methods

MSDS 498 Capstone Course

Work history is a blend of teaching with industry.

Industry history demonstrates the successful application of training in Statistics/Mathematics along with programming skills to advance as an analyst to director positions.

Teaching history has been an opportunity to share knowledge of what works in industry and in consulting.

It is my belief that the best instruction at all levels is provided by having the actual experience of working in a diverse set of experiences.

Work history is a blend of teaching with industry.
Industry history demonstrates the successful application of training in Statistics/Mathematics along with programming skills to advance as an analyst to director positions.
Teaching history has been an opportunity to share knowledge of what works in industry and in consulting.
It is my belief that the best instruction at all levels is provided by having the actual experience of working in a diverse set of experiences.

• Spatial/temporal distribution of Matian near-surface volatiles (water and ice)
• Probabilistic surface saturation by impactors on solar system rocky bodies
• Correlation structures of random effects in hierarchical generalized linear models
• Modeling sunspot areas and counts with spatial/temporal, non-Gaussian models

• PhD, Applied Statistics and Research Methods, University of Northern Colorado
• MS, Applied Mathematics and Statistics, Wichita State University
• MS, Physics, Wichita State University

• Northwestern University, Chicago, Illinois (June 2014–Present) School of Professional Studies, Adjunct Lecturer, Data Science Program
• University of Northern Colorado, Greeley, Colorado (January 2019–Present) Department of Applied Statistics and Research Methods, Adjunct Lecturer, Graduate School
• University of Northern Colorado, Greeley, Colorado (January 2010–2014) Department of Applied Statistics and Research Methods, Research Consultant, Graduate School
• Southwest Research Institute, Boulder, Colorado (2015-present) Consulting Statistician, NASA Mars Data Analysis Program
• Sun Microsystems, Inc., Broomfield, Colorado (2000 – 2009) Senior Staff Statistician

• J.D. Riggs and T.L. Lalonde. (2017). Handbook for Applied Modeling: Non-Guassian and Correlated Data. Cambridge University Press, New York.
• S.J. Robbins, P.M. Schenk, J.D. Riggs, A.H. Parker, V.J. Bray, C.B. Beddingfield, R.A. Beyer, A.J. Verbiscer, R.Binzel, and K.D. Runyon, Depths of Pluto’s and Charon’s craters, and their simple-to- complex transition. Icarus. June 2020. doi:https://doi.org/10.1016/j.icarus.2020.113902
• S.J. Robbins, J.D. Riggs, B. Weaver, C. Chapman, K. Singer, E. Bierhaus (2018). Revised Recom- mended Methods for Analyzing Crater Statistics. Meteoritics & Planetary Science, 04/2018, Vol.53(4), pp.891-931.
• S.J. Robbins, W. A. Watters, J. E. Chappelow, V. J. Bray, I. J. Daubar, J. D. Riggs, R. A. Craddock, R. A. Beyer, M. Landis, L. R. Ostrach, B. P. Weaver, L. Tornabene. Measuring Impact Crater Depth Throughout the Solar System. Meteoritics & Planetary Science, Article ID: MAPS12956, Article DOI: 10.1111/maps.12956, Internal Article ID: 14496744.
• R. S. de Souza, A.L. Chies-Santos, J. Hilbe, E. Cameron, E. Ishida, A. M. M. Trindade, J.D. Riggs. (2015). The Overlooked Potential of Generalized Linear Models in Astronomy-III: Negative-Binomial and Globular Cluster Population. MNRAS (October 21, 2015) 453 (2): 1928-1940. doi: 10.1093/mn- ras/stv1825.
• 5. R.S. de Souza, E. Cameron, M. Killedar, J. Hilbe, R. Vilalta, U. Maio, V. Biffi, B. Ciardi, J.D. Riggs, for the COIN collaboration. (2015). The overlooked potential of Generalized Linear Models in astronomy-I: Binomial regression. Astronomy and Computing. 12: 21-32. http://dx.doi.org/10.1016/j.ascom.2015.04.002. 24 citations.
• H.P. Shuch and J.D. Riggs (2008). Correcting for interdependence of terms in the San Marino Scale. 59th International Astronautical Congress Preprints: IAC-08-A4.2.04, IAF, Glasgow, Scotland.

• US Patent, System and Method for Diagnostic Test Innovation, 2009, patent 7,555,677.
• US Patent, Apparatus for Comparing Performance of Electronic Devices, 2007, patent 7,200,527.
• US Patent, Method and Apparatus for Comparing Performance of Electronic Devices, 2007, patent 7,200,526.

MSDS 401 Applied Statistics with R 

MSDS 413 Time Series Analysis and Forecasting

Data Science Applications of Blockchains; Statistical Architectures of Machine Learning, Entrepreneurial opportunities in customer data, privacy, loyalty, and credit.

Ph. D in Statistics (University of Pittsburgh, Pittsburgh, USA)
M.A in Mathematics (University of Pittsburgh, Pittsburgh, USA)
M.Sc. in Statistics (Madras Christian College, University of Madras, Chennai, India )
B.Sc. in Statistics, Economics, and Mathematics (Loyola College, University of Madras, Chennai, India)
Certified in Data Engineering, Big Data, and Machine Learning on GCP

Forty plus years of Consulting in Statistics, Economics, and Mathematics Computations and Applications, and Teaching; Worked as Research Associate on Economics of Education initiative at University of Madras. Trained by stalwarts in Multivariate Statistics such as C. R. Rao, and K. Krishnaiah. Took special courses with William Shapley and Martin Shubik in Game Theory at ISI, Delhi, and University of Madras. Group leader for outsourced projects at Acxiom.

Patel J, Ladani A, Sambamoorthi N, LeMasters T, Dwibedi N and Sambamoorthi U. A Machine Learning Approach to Identify Predictors of Potentially Inappropriate Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) Use in Older Adults with Osteoarthritis - Accepted with Int. J. Environ. Res. Public Health 2021, 18, x

Thornton JD, Dwibedi N, Scott VG, Ponte CD, Ziedonis DM, Sambamoorthi N, Sambamoorthi U. Predictors of transitioning to incident chronic opioid therapy among working-aged adults. Journal of American Health & Drug Benefits. Feb2018, Vol. 11 Issue 1, p12-19.

Garg R, Shen C, Sambamoorthi N, Kelly KM, Sambamoorthi U. Type of Multimorbidity and Patient-Doctor Communication and Trust among Elderly Medicare Beneficiaries. International Journal of Family Medicine, Volume 2016 (2016), Article ID 8747891, 13 pages http://dx.doi.org/10.1155/2016/8747891.

Agarwal P, Bias TK, Madhavan S, Sambamoorthi N, Frisbee S, Sambamoorthi U. Factors Associated With Emergency Department Visits: A Multistate Analysis of Adult Fee-for-Service Medicaid Beneficiaries. Health Serv Res Manag Epidemiol. 2016 Jan-Dec;3. doi: 10.1177/2333392816648549. Epub 2016 May 27. PMID: 27512721

 Weiner RC, Shen C, Sambamoorthi N, Sambamoorthi U. Preventive Dental Care in Older Adults with Diabetes. J Am Dent Assoc. 2016 May 14. pii: S0002-8177(16)30309-9. doi: 10.1016/j.adaj.2016.03.022. [Epub ahead of print] PMID: 27189741

Garg R, Shen C, Sambamoorthi N, Sambamoorthi U. Type of Multimorbidity and Propensity to Seek Care among Elderly Medicare Beneficiaries. Journal of Health Disparities Research and Practice, Accepted.

Past Chairman – Awards Committee – Marketing Technology and Internet Council; DMA
Government of India National Merit Scholar during post-graduate studies – M.Sc Statistics

MSDS 402 Introduction to Data Science
MSDS 403 Data Science in Practice
MSDS 420 Database Systems and Data Preparation

‘Articulation, Articulation, Articulation’ and I have a long way to go!

1. We need to provide templates of problems and their solutions. The testing is basically coming from these templates. I work with curriculum development team with that in my mind. Our curriculum development methodology, bloom methodology, is a strong student focused philosophy, and it is helpful to focus on outcome based, templatized solutions.

2. Take pride in simplification, articulation, and be part of joy of learning, from student's point of view.

3. Data intelligence automation fascinates me, and I love to teach the next generation. I know how many difficulties I went through in learning on my own. My students do not have to go through such experiences.

4. Being in the world of symbols and theorems (Math and Stat), one of my favorite quotes is "Say it in English". Symbols and equations do not bring out the dynamics of their interplay easily, but
construction of words and sentences can. While formula is important for computations, converting
the formula as one of explaining the logical process of reasoning purely in words and just how it fits the problem on hand is fun.

Articulation is an important talent whatever the course is, as every course has its own set of terminologies, definitions, and phrases. How they relate to daily life is a talent worthy of emulation

Decision science, quantitative psychology and artificial intelligence,  econophysics, and behavioral finance

Chartered Financial Analyst (CFA ™ Charter Holder)
PhD Economics (awaiting defense), Jawaharlal Nehru University
MS Applied Economics, University of Texas at Dallas
MA Economics, Jadavpur University
BA Economics, Jadavpur University

Current Positions:
Co-Founder – CEO, dōnō consulting
Co-Founder – CEO, propmath.com
Head of Academics, vired.com

Past Positions:
Head of Analytics and New Products (Real Estate), Thomson Reuters
Vice President, Sorin Capital
Senior Consultant, The Blackstone Group
Analyst, Dow Jones Indexes

Publications:
1. History Dependence in an Experimental Labor Market, Journal of Quantitative Economics
2. Effect of Tobin Tax on Trading Decisions in an Experimental Minority Game, New Perspectives and Challenges in Econophysics and Sociophysics
3. Big Data: Prospects and Challenges, Vikalpa: The Journal for Decision Makers

Products: (all part of PropMath.com)
1. Automated Real Estate Valuation Model for Emerging Markets with sparse data
2. Automated Rental Model for Emerging Markets with sparse data
3. Commercial and Residential Price Indexes for Emerging Markets

• 10 Most Prominent Analytics Academicians in India-2017, Analytics India Magazine
• 20 Most Prominent Analytics & Data Science Academicians In India: 2018, Analytics India Magazine
• 40-under-40 Data Scientists in India-2019 (INSOFE-AIM)

MSDS 451 Financial Machine Learning

MSDS 474 Accounting and Finance for Technology Managers

Hi – I am Professor Dipyaman Sanyal. Everyone calls me Deep. And I am the creator of MSDS 474, which I have been teaching every alternate semester since it was launched in 2018. I have also once taught MSDS 451. As a former quant and investment analyst on the buyside working with hedge funds and PE funds, my work has always been about overlaps between finance and data science – add to that my training as an economist and my career as an entrepreneur – and you will get MSDS 474.

After graduate school I started my career at Dow Jones Indexes and then moved on to work in firms like The Blackstone Group and Sorin Capital – all in the New York area, before moving back to India for Thomson Reuters. In that time, I also completed my CFA exams and received my charter.

In 2012, I started dōnō consulting, which has worked with clients in 5 continents across multiple industries and domains – again, both in finance and in data science-ML-AI. In 2020 I also co-founded propmath.com – which is one of the first ai-ml driven proptech firms that is trying to provide transparency in emerging markets real estate. We have already created India’s first ML-driven automated valuation and rental models for both commercial and residential real estate.

While being a CEO of 2 firms keeps me busy, I also teach extensively. Other than my teaching at NU, since I started my firm, I have also headed 3 of the top-10 data science programs in India – one of them in partnership with Northwestern and another in partnership with UChicago. Now, I am Head of Academics for Vired, where I am responsible for launching multiple programs in collaboration with MIT in India, including programs on data science-AI-ML and Finance and Financial Technology.

While I have been a student a large part of my life (BA, MA, MS, CFA and PhD) much of it has been part-time, while I worked quite intense jobs, including while setting up my firm. So, I believe I understood the challenges of continuing education even before I started at Northwestern. My teaching tends to be extremely hands-on and brings my experiences to the classroom – whether I teach data science or finance or economics or entrepreneurship – or an overlap of these as in 474. The final projects in my class have inspired multiple students over the past years to use that as a part of their initial pitch document for a new start up or for a division within their firm – those feedbacks have been one of the most heartening things that I have heard from my students, because I ensure my courses end with a complete project plan and presentation on a data science start up idea from the student where I provide extensive feedback to the students so that they can take outside of class to the real life.

Master of Science in Data Science, Northwestern University School of Professional Studies, Evanston, IL

Bachelor of Science in Business, Accounting Major, University of Minnesota Carlson School of Management, Minneapolis, MN

Bachelor of Art, Elementary Education, Concordia University, River Forest, IL

Blockchain Technology (voting systems), Use of Data Analytics in the Fishing Industry (Iceland), Startups and AI-based Business Models, human-centered design, B Corps Certifications and designing business models as a force for good.

Various finance and operations roles at Accenture and EY, particularly roles in Accenture’s New Businesses area where internal businesses were launched and grown organically or through acquisitions. Focus on business model and operations process design.

Launch of personal LLC, Fearbox Solutions

Northwestern Segal Design Institute

MSDS 491 Product Prototyping, Spring 2024

Sports analytics, statistics and online education, applied statistics, and data visualization

Ph.D. Psychological Studies in Education, Statistics Minor (Currently ABD)
    University of Nebraska-Lincoln, Lincoln, NE
    Specialization: Quantitative, Qualitative, and Psychometric Methods
M.S. Statistics,  University of Nebraska-Lincoln, Lincoln, NE
M.S. Mathematics, Pittsburg State University, Pittsburg, KS
B.S. Mathematics with emphasis in Actuarial Science, Pittsburg State University, Pittsburg, KS

• Player Development Analyst (March 2018 – Present), Professional Baseball Organization
• Assistant Director of Sports Analytics (August 2016 – March 2018), University of Nebraska-Lincoln, Lincoln, NE
• Research Assistant (August 2016 – March 2018), Brain, Behavior, Emotion Lab University of Nebraska-Lincoln, Lincoln, NE
• Senior Actuarial Analyst (February 2009 – July 2009), Coventry Health Care, Kansas City, MO
• Actuarial Analyst (February 2007 – February 2009), Humana, Louisville, KY

MSDS 401 Applied Statistics with R
MSDS 456 Sports Performance Analytics
MSDS 457 Sports Management Analytics

I’ve been an instructor in Northwestern’s Master of Data Science Program since 2018. In that time, I’ve taught Applied Statistics with R, and the Sports Performance and Sports Management Analytics course sequence.

In these sports analytics courses, all work is centered around the expectation that you’re preparing for a job in the Sports Analytics field. This is done through a combination of assignments, discussions, and projects that are designed to address real world scenarios that you would encounter working for an organization. These assessments aim to allow you to apply the skills and knowledge that you’ve acquired throughout the Program in addition to any academic or professional experiences that you’ve had.

While technical competence is necessary, a key focus in these courses is ensuring that you’re able to communicate your findings to both technical and non-technical audiences alike. Therefore, even if you’re not set on a career in sports, any student who takes these courses will benefit from the work that you’re asked to complete.

Sports covered in these courses include the 3 major sports: Baseball, basketball and football, however, as a student you are allowed to adapt the work to fit the sport or sports that are most interesting to you. Topics covered in these courses include sections related to on field performance, in game strategy, roster construction including player categorization, as well as off field topics including sections on revenue sources such as ticketing, marketing, and fan engagement. In combination these courses allow you to combine both on and off field perspectives to assess the strengths of organizations both financially and competitively.

Throughout these courses I utilize my experience working with sports organizations at both the collegiate and professional level, to give you a sense of the day to day work that you would be expected to complete. In addition, I’ll provide guidance on how to navigate the job search, and help determine ways to make you a more desirable candidate. In conclusion, whether you’re laser focused on a specific sport, industry, or team, or still trying to figure out if sports analytics is just a hobby, these courses allow you to improve your technical and communication skills, all while pursuing the topics that are most interesting to you.

PhD in Statistics, Colorado State University

MS in Statistics, Colorado State University

MS in Mathematics, Thiagarajar College, India

Leadership Certificate, Wharton School of Business & Kellogg School of Business

Adjunct Faculty – Northwestern University (2013 – Present)

Chief Data Scientist – CGN Global Inc. (2011 – Present)

Principle Data Scientist – HBM nCode Federal LLC. (2017 – 2020)

Director of Analytics – Caterpillar Inc. (1989 – 2011)

Senior Data Scientist – Ecolab Inc. (1988 – 1989)

Assistant Professor – Northern Illinois University (1986 – 1987)

“Anomaly detection for multivariate time series data using Machine Learning methods” (presented at the Army Operations Research Symposium, October 2019)

"Probabilistic Design Methodology – A new approach for accurate predictive models" (presented at the 3rd Annual Probabilistic Methods Conference, June 2003).

"Reliability Prediction of Systems & Components – A new multidisciplinary approach" (presented 2nd Annual Probabilistic Methods Conference, June 2002).

"Business Applications using Data Mining Technology" (presented at the CIO conference sponsored by the Business School of University of Michigan, Oct 1999).

"Probabilistic Design Using Design of Experiments" (presented at the 7th Annual SAE - RMS Workshop in May, 1995).

"Probabilistic Modeling Using Response surface Methodology" (presented at the 45th Annual SAE - Earthmoving Industry Conference in 1994).

"Probabilistic Structural Analysis Methods" (presented at the 1993 Annual Meetings of the American statistical association).

"Confidence Intervals For Proportion of Total Variation in Unbalanced Two-Way Components of Variance Models using Unweighted Means" (published in Communications in Statistics: Theory and Methods, 1991, Vol. 20, No. 2, pp. 511 - 526).

"Establishing Reliability of Biomechanical Data using Univariate and Multivariate Approaches" (published in Research Quarterly for Exercise and Sport, 1990, Vol. 61, No. 2, pp.154 - 160).

"SPC for Batch processes" (presented at the 1989 Annual Meetings of the American Statistical Association).

"Confidence Intervals on Proportion of Total Variation due to error in two-way models" (presented at the 1988 Annual Meetings of the American Statistical Association).

National Grand Challenge Award

MSDS 401 Applied Statistics with R

MSDS 410 Supervised Learning Methods

MSDS 422 Practical Machine Learning

MSDS 450 Marketing Analytics

MSDS 453 Natural Language Processing

MSDS 458 Artificial Intelligence and Deep Learning

MSDS 401 Applied Statistics with R

MSDS 402 Introduction to Data Science

MSDS 460 Decision Analytics

PhD Engineering Systems (Machine Learning/AI), University of Toledo
MS Engineering Science (Statistics/Machine Learning), University of Toledo
MS Data Mining, Central Connecticut State University
MS Management, University of Akron
BS Electrical Engineering (Software Development), University of Toledo

DKW Analytics, LLC – Owner (2018-Present)
Northwestern University – Instructor (2013-Present)
Sprint Corporation – Director of Data Science (2016-2018)
SAS Institute – Advanced Analytic Consultant (2007-2016)
Progressive Insurance – Product Manager of Data Science (2005-2007)
Key Bank Corporation – VP of Strategic Analytics (1999-2005)

MSDS 498 Capstone

MSDS 422 Practical Machine Learning

MSDS 401 Applied Statistics with R