Managerial Decision-Making with Big Data
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

LIVE Virtual

Program Information

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, suitable 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.

EARN A DIGITAL BADGE AND CERTIFICATE:
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.

Key Takeaways / Curriculum:

  • 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

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.

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Time

9:00 a.m. to 11:00 a.m.

TIME REQUIREMENT

10 live hours total, 5 live sessions of 2 hours each. There are at least 20 hours of reading and exercises.

Dates

First DatesJuly 25 - July 29 2022

Second DatesTo Be Announced

Program Cost

$2,250

COURSE DISCLAIMEREvery reasonable effort will be made to ensure this course runs as described on this webpage. 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 requires a minimum number of registrants to take place. You will be notified by email if the course does not meet this minimum.

TEACHING
METHODS

Live, Online: This program will take place over five 2-hour sessions of live, synchronous class sessions each day that include small group breakout sessions and discussions. 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 spontaneous questions and answers for immediate clarification, real-time coaching and real-world practice.

Program Faculty

Tom Chang

Associate Professor of Finance and Business Economics

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. He received a BS in Physics (1998) and a PhD in economics (2009), both from the Massachusetts Institute of Technology. His current research focuses on better understanding individual decision-making and its implications for firm behavior.

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