[Please Note: The Overview Session has already occurred. Go to the News Story for this event to read about what happened and to access a recording and speaker slides.]
Dates and times for the top ten topic sessions listed below to be announced soon!
NISS announces a brand new series of tutorials on some of the most interesting topics in data science that are in use in business today!
This first session in this series is an overview of these Top 10 analytics approaches of the key topics that are used in the business!
Students and faculty, these are perhaps the top ten most important and practical topics that may not be covered in your program of study.
This first overview session will be presented by Victor Lo of Fidelity Investments. Here are the titles of the sessions as they will be presented during the summer and into the fall of 2020 that Victor will introduce.
Dates and times for these sessions listed below to be announced soon!
- Analytical Consulting, Communication and Soft Skills
- Computer Science, Programming, and Tools
- Descriptive Analytics, Exploratory Data Analysis, and Data Visualization
- Predictive Analytics and Machine Learning
- Deep Learning
- Causal Inference and Uplift Modeling
- Prescriptive Analytics and Optimization
- Unstructured Data Analysis
- Social Sciences and Data Science Ethics
- Domain Knowledge and Case Studies
This first overview session is free and open to the public. There will be a modest fee for the subsequent tutorials
NISS is interested in sharing knowledge. To this end, these webinars have been geared to provide practical information that you can use right away. Examples, projects and code sharing will be a part of these sessions wherever possible.
Organization of the Essential Data Science for Business Series
The NISS Data Science Training Series starts with a free overview webinar, “Overview of Data Science: 10 Most Important Topics You Should Know About and Be Able to Use!” Every two months or so a more detailed training module on each of topics discussed in the overview webinar is presented. All of these sessions are 2-3 hours duration.
This first Overview session is free and open for the public. Please join to find out what the remaining sessions will entail!
Subsequent topic based tutorials involve a fee. For NISS Affiliates the subsequent individual tutorial modules are free, for non-affiliates a fee of $35 is charged.
It will be helpful for the participants to have a working knowledge of probability distributions, statistical inference, statistical modeling and time series analysis. Those 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.
About the Overview Speaker
Victor S.Y. Lo is a seasoned Big Data, Marketing, Risk, and Finance leader with over 25 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 currently leads the AI and Data Science Center of Excellence, 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 also 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 finishing a graduate-level textbook titled “Cause-and-Effect Business Analytics.”