Essential Data Science for Business: Prescriptive Analytics and Optimization

May 12, 2021 1-4 pm

The tutorials in this NISS series involve the Top 10 analytics approaches of the key topics that are used in business today!  Students and faculty, these are perhaps the top ten most important and practical topics that may not be covered in your program of study.  (Review the Overview Presentation about all 10 Sessions).


Prescriptive Analytics and Optimization

Overview

This tutorial will review the three types of analytics (descriptive, predictive, and prescriptive) that are commonly employed in real-world applications across industries, and illustrate the importance of prescriptive analytics and an optimization mindset for decision making. Examples from multiple application areas such as marketing analytics, operations management, workforce management, finance, and/or healthcare will be referenced in the context of introducing key optimization concepts and tools such as problem formulation and determination of optimal solutions, types of optimization problems (e.g., linear and integer programming problems), data-driven optimization and optimization under uncertainty. We will also introduce a practical framework that links predictive analytics, causal inference, and prescriptive analytics for optimizing decisions using appropriate techniques in different situations.

Instructors

Victor Lo (Fidelity Investments), and 

Dessislava Pachamanova (Babson College)


Goals

NISS is interested in sharing knowledge.  To this end, these webinars have been geared to provide practical information that you can use tomorrow. Examples, projects and code sharing are a part of these sessions wherever possible.

Series Prerequisites

Participants require a working knowledge of probability distributions, statistical inference, statistical modeling and time series analysis as a prerequisite. Students who do not have this foundation or have not reviewed this material within the past couple of years will struggle with the concepts and methods that build on this foundation.

Registration

Select a registration/payment option above the 'Register for this Event' button ($35 for this Data Science Essentials tutorial session, $250 for all 10 Essential Data Science for Business tutorial sessions.  Can attend this live session? Post Session Access to tutorial materials and recording can be obtained for $35 after the event is over.). NISS Affiliates, (https://www.niss.org/affiliates-list), please send an email to officeadmin@niss.org.).  Notifications: You will recieve an email that comes immediately to let you know you paid.  Links to the event will come via email the day before and one hour prior to the actual session.


Agenda

About the Instructors

Victor S.Y. Lo is a seasoned Big Data, Marketing, Risk, and Finance leader with over 26 years of extensive consulting and corporate experience employing data-driven solutions in a wide variety of business areas, including Customer Relationship Management, Market Research, Advertising Strategy, Risk Management, Financial Econometrics, Insurance Analytics, Product Development, Healthcare Analytics, Operations Management, Transportation, and Human Resources. He is actively engaged with causal inference and is a pioneer of Uplift/True-lift modeling, a key subfield of data science.

Victor has managed teams of quantitative analysts in multiple organizations. He is currently Senior Vice President of Data Science & AI, Workplace Investing at Fidelity Investments. Previously he managed advanced analytics/data science teams in Personal Investing, Corporate Treasury, Managerial Finance, and Healthcare and Total Well-being at Fidelity Investments. Prior to Fidelity, he was VP and Manager of Modeling and Analysis at FleetBoston Financial (now Bank of America), and Senior Associate at Mercer Management Consulting (now Oliver Wyman).

For academic services, Victor is an elected board member of the National Institute of Statistical Sciences (NISS), where he provides guidance to the board and general education to the statistics community. He has been a visiting research fellow and corporate executive-in-residence at Bentley University. Additionally, he has been serving on the steering committee of the Boston Chapter of the Institute for Operations Research and the Management Sciences (INFORMS) and on the editorial board for two academic journals. Victor earned a master’s degree in Operational Research and a PhD in Statistics and was a Postdoctoral Fellow in Management Science. He has co-authored a graduate-level econometrics book and published numerous articles in Data Mining, Marketing, Statistics, and Management Science literature, and is co-authoring a graduate-level data science textbook titled “Cause-and-Effect Business Analytics.”

Dessislava Pachamanova

Dr. Pachamanova is Professor and Zwerling Family Endowed Research Scholar at Babson College and Research Affiliate at the Massachusetts Institute of Technology. Her research and consulting span multiple fields, including robust optimization, risk management, simulation, predictive analytics and machine learning, text analytics, and operations. She has authored and coauthored dozens of articles in operations research, finance, accounting, engineering, marketing and management journals. Dr. Pachamanova teaches courses in analytics and computational finance in the undergraduate and the MBA programs, and cares deeply about creating a valuable learning experience for students. She co-designed Babson's undergraduate and MBA business analytics concentration curricula, co-chaired the design task force for a Masters program in Business Analytics, and co-developed the Business Analytics and Machine Learning MBA concentration. She has also served as faculty director for two open enrollment business analytics programs at Babson Executive Education, Business Analytics for Managers (face-to-face format) and the Babson-Pearson Business Analytics for Managers in India program (online/blended format), and has designed numerous modules on business analytics for custom executive education programs. Dr. Pachamanova serves on the INFORMS Professional Development Committee, the INFORMS OR & Analytics Student Team Competition Committee, the INFORMS Pro Bono Analytics Committee, and is Area Editor (Cases) for INFORMS Transactions on Education. She holds an A.B. from Princeton University and a Ph.D. from the Sloan School of Management at the Massachusetts Institute of Technology.

Event Type

Host

National Institute of Statistical Sciences

Cost

$35 for this session; $250 for all 10 Data Science Sessions

Location

Online Tutorial
Instructors: Victor Lo (Fidelity Investments) and Dessislava Pachamanova (Babson College)