Executive Education Big Data Business Program

Big Data and Managerial Decision-making

Upcoming Dates

Dates
February 14, 2022 - February 18, 2022
Time Requirement
This 5-day program will take place over one week, February 14, 15, 16, 17, and 18th, with 2 hours a day of live, synchronous class sessions.

Registration for this course will close -- February 7, 2022. There is a 10% discount for three or more registrants from the same organization.
Program Cost
$2250
Location
Marshall Live Online
Dates
July 25, 2022 - July 29, 2022
Time Requirement
This 5-day program will take place over one week, July 25, 26, 27, 28, and 29th, with 2 hours a day of live, synchronous class sessions.

Registration for this course will close -- July 18, 2022. There is a 10% discount for three or more registrants from the same organization.
Program Cost
$2250
Location
Marshall Live Online

Program

Develop strategies to use data analytics for more effective decision-making in this course taught by a top behavioral finance researcher — and lead with confidence in a data-driven world.

Data is everywhere, and understanding what it means is vital for 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, ideal for 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 and make fact-driven decisions based on analysis. Big Data and Managerial Decision-making was 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 or 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

download brochure

EARN A DIGITAL BADGE
An important aspect of our programs is the ability to share your accomplishment with important stakeholders. Upon completing Big Data and Managerial Decision-making, you will earn a digital badge recognizing your new proficiency. Share and showcase your achievements by posting your digital badge on online resumes and social networks such as LinkedIn.

Executive Education Big Data Marketing Business

 

WHO SHOULD ATTEND?  This course is designed for executives who are interested in implementing big data initiatives, area leaders whose operational areas would benefit from increased use of data analytics (e.g., marketing), managers who want to understand/deploy/scale analytics and AI in their organization, and anyone who wants to understand the benefits and limits of big data/AI and data-driven decision-making.

 

Teaching Methods

Live, Online:  This program will take place over five days, with 2 hours a day of live, synchronous class sessions that include small group breakout discussions, as well as data projects. To maximize this collaborative learning experience for everyone, participants are expected to attend every session and contribute actively during the class and while in small groups. This format allows for instant question-and-answer sessions for immediate clarification.

- Hide
Faculty
Tom Chang

Tom Chang, Ph.D., M.S., MBA, Associate Professor of Finance and Business Economics, USC Marshall School of Business

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, he 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.

Professor 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, The New York Times, PBS Newshour, The Wall Street Journal, The Washington Post, “The Daily Show” and “The Today Show” — and once made it into a “Tonight Show” monologue. In addition, he 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

 

- Hide
Course Disclaimer

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.

- Hide