Coursera’s Hottest Business MOOCs

Mastering Data Analysis in Excel

School: Duke University

Registration Link:  CLICK HERE

Start Date: December 14, 2015 (6 Weeks Long) – Open Until December 18

Workload:  Not Specified 

Grades: Students can choose to explore course videos, discussions, and ungraded assignments for free, but they won’t be able to submit graded assignments, earn a certificate, or complete a specialization without paying a $79 fee for each course.

Instructors: Daniel Egger and Jana Schaich Borg

Credentials: Since 2009, Egger has been an executive in residence at Duke University’s Master in Engineering Management program, where he teaches courses in data mining, entrepreneurship and venture capital, and computational methods in finance. He earned a JD from Yale Law and spent over 17 years in tech and software sector, including stints as the founder and CEO of a venture-backed tech firm and a managing partner in a venture capital firm.

Borg earned her Ph.D. in Neurbiology from Stanford University and is currently a postdoctoral associate at the Duke University Medical Center. Her research focuses on social behavior and social cognition, with an emphasis on how and why people make social decisions. She is also heavily involved in using big data to aid in disruptive innovation in biomedical science.

Description: Complex metrics and predictive models aren’t just the province of Ivy-trained MBAs and Wall Street bankers. They can be performed by anyone who can use an Excel spreadsheet. Forget learning calculus, coding, Matlab, or R. In this course, you’ll learn how to plot out data in Excel to predict outcomes and reduce uncertainty. The course opens with a tutorial on Excel basics to get all students on the same page. From there, students will be firmly grounded in the basics of predictive modeling: Binary classification and linear regression. At the same time they will be exposed to methodologies like the Bayesian Logical Data Analysis, where students will use real world data sets to solve complex forecasting issues. The course will be taught through videos, case studies, and readings, with students evaluated through weekly quizzes and a final project.

Review:  “The class focused on how certain statistical models are implemented in Excel – in theory. Although no math backgound was required, don’t even think about taking this class unless you are an Excel power user with integral and differential Calculus, statistics I and II, and machine learning under your belt otherwise you will be totally lost.” To read additional reviews, click HERE. 

Additional Note: This course is part of a four course specialization from Duke University called, “Excel to MySQL: Analytic Techniques for Business Specialization.” To learn more about these courses and register for them, CLICK HERE.