Big Data and Managerial Decision-making
Registration for this course will close -- May 24, 2021. There is a 10% discount for three or more registrants from the same organization.
Data is everywhere and understanding what it means is an ever more important input into strategic decision making in organizations. This program is designed to give leaders the framework to judge what good data science looks like so that you can identify where data projects can add value and lead with confidence.
During this program, suitable to non-specialists, you will learn about both the capabilities and limitations of big data, AI and data analytics. You will be taught a framework from which to understand big data and analytics, and leave with a toolbox that you can use to lead effective business analytics initiatives.
B^3 is designed to give decision makers the framework to understand and implement data projects that generate actionable insights to help them make fact-driven decisions. The fact is that the hardest parts of implementing a big data analytics strategy or project do not involve data science of technology. Rather the real challenges are ones of leadership and management.
TOPICS AND KEY TAKEAWAYS
- Understand what Big Data/AI can and can’t do (abilities and limitations)
- Learn how to use experiments and predictive analysis to improve decision making.
- Learn to develop strategies to integrate data analytics for fact-based decision-making processes across an organization.
- Judge what “good” looks like in data science
- Identify where analytics provides value add and where it doesn’t
- Lead with confidence in a data driven world
|WHO SHOULD ATTEND?
Live, Online: This program will take place over 5 days, with 3 hours a day of live, synchronous class sessions that include small group breakout discussions, as well as data project. To maximize this collaborative learning experience for everyone, you are expected to attend every session, and contribute openly during the class and while in the small groups. This format allows for instant question and answer sessions for immediate clarification.
Dr. Tom Chang is an associate professor of Finance and Business Economics at the Marshall School of Business and a research fellow at both the Schaeffer Center for Health Policy and Economics and the Dornsife Center for Economic and Social Research. His current research focuses on better understanding individual decision-making and its implications for firm behavior. In between earning a B.S. in physics and a Ph.D. in economics from the Massachusetts Institute of Technology, Prof. Chang was a co-founder and managing partner of a software development firm whose clients included AMD, Boston Consulting Group, KPMG, MIT, Thomson Reuters, TWA and the City of New York.
Prof. Chang has published in a wide variety of academic and professional journals including Harvard Business Review, Journal of Finance, PLOS One, Review of Economic Studies, and Review of Financial Studies. In 2017, his article “Being Surprised by the Unsurprising: Earnings Seasonality and Stock Returns” was awarded the Hillcrest Behavioral Finance Award for the best paper published in behavioral finance that year.
His work has received significant media attention and has been covered by the Associated Press and featured in Bloomberg, CNN, The Economist, Financial Times, Los Angeles Times, NPR, New York Times, PBS Newshour, Wall Street Journal, Washington Post, The Daily Show, The Today Show and once made its way into the Tonight Show monologue. In addition, Prof. Chang is an in-demand speaker who has been invited to present his work at the Federal Reserve Bank, Harvard, Yahoo! and Nerd Nite LA. Click to read complete bio and CV
Every reasonable effort will be made to ensure this course runs as described on this web page. Please note that course dates and professors are subject to change. You will be notified by email in advance if there is a date or professor change. Additionally, this course also requires a minimum number of registrants in order to take place. You will be notified by email if the course does not meet this minimum.