Cambridge Judge | Mr. Nuclear Manager
GMAT 700, GPA 2.4
London Business School | Ms. Aussie Consultant
GMAT 730, GPA 3.5
Darden | Mr. Deloitte Dreamer
GMAT 700, GPA 3.13
Stanford GSB | Mr. Young Entrepreneur
GMAT 730, GPA 3.4
Stanford GSB | Ms. Retail Innovator
GMAT 750, GPA 3.84
Harvard | Mr. Double Whammy
GMAT 730, GPA 3.3
Kellogg | Mr. Geography Techie
GMAT 740, GPA 3.9
INSEAD | Mr. Media Startup
GMAT 710, GPA 3.65
Kellogg | Ms. Kellogg Bound Ideator
GMAT 710, GPA 2.4
Cornell Johnson | Mr. Emporio Armani
GMAT 780, GPA 3.03
Foster School of Business | Mr. Tesla Gigafactory
GMAT 720, GPA 3.0
Wharton | Ms. Female Engineer
GRE 323, GPA 3.5
Darden | Ms. Teaching-To-Tech
GRE 326, GPA 3.47
Stanford GSB | Mr. Financial Controller
GRE Yet to Take, Target is ~330, GPA 2.5
Kellogg | Mr. 770 Dreamer
GMAT 770, GPA 8.77/10
Ross | Ms. Middle Aged MBA-er
GRE 323, GPA 3.6
London Business School | Mr. Impact Financier
GMAT 750, GPA 7.35/10
Chicago Booth | Mr. PM to FinTech
GMAT 740, GPA 6/10
Ross | Mr. Operational Finance
GMAT 710, taking again, GPA 3
Kellogg | Mr. Texan Adventurer
GMAT 740, GPA 3.5
Harvard | Mr. Data & Strategy
GMAT 710 (estimate), GPA 3.4
Tuck | Ms. Green Biz
GRE 326, GPA 3.15
MIT Sloan | Mr. Unicorn Strategy
GMAT 740 (estimated), GPA 3.7
Duke Fuqua | Mr. National Security Advisor
GMAT 670, GPA 3.3
Duke Fuqua | Mr. Tech Evangelist
GMAT 690, GPA 3.2
Duke Fuqua | Mr. 911 System
GMAT 690, GPA 3.02
Duke Fuqua | Mr. Musician To Consultant
GMAT 710, GPA 1.6

Essential MOOCs In Business For March

The Analytics Edge

School: MIT

Platform: EdX

Registration Link:  The Analytics Edge

Start Date: March 3, 2015 (12 Weeks)

Workload: 10-15 Hours Per Week

Instructors: Imitris Bertsimas and Allison O’Hair

Credentials: Bertsimas is the Boeing Leader for Global Operations Professor of Management. A faculty member since 1988, Bertsimas also co-directs MIT’s Operations Research Center. He has authored three books and 150 scientific papers, along with editing several journals including Optimization for Management Science and Operations Research in Financial Engineering. His research interests include statistics analytics – and their application to industries ranging from aerospace to finance. He holds a Ph.D. from MIT and has supervised 65 doctoral candidates during his tenure.

O’Hair teaches several data and analytics courses in MIT’s Sloan School of Management. Like Bertsimas, she holds a Ph.D. in operations research from MIT, with her interests including the application of analytics to the healthcare industry. She and Bertsimas are also collaborating on a book and several journal articles.

Graded: Students will receive a certificate of achievement for meeting all requirements and following the honor code.

Description: Analytics is more than a passing fad. These days, the ability to translate, segment, connect, and apply data can help organizations gain competitive intelligence and find unexpected solutions. In this course, students will start their week learning how an organization applied analytics to their business model. For example, students will study how the Oakland A’s used analytics to perform a cost-benefit analysis that enabled them to win their division championship with the league’s lowest payroll. And the class will explore similar tools and techniques used by eHarmony, IBM Watson, Twitter, and Netflix. Through these examples – and using real world data sets – students will learn analytics methods like “linear regression, logistic regression, trees, text analytics, clustering, visualization, and optimization.” The course will be taught at a graduate level using lecture videos to convey concepts and data homework assignments. At the end, students will also take a final exam to assess their overall mastery of the content.

Review: “I took the course to freshen my R skills and get a fuller overview on recent analytics practice. The first 9 weeks of the 11 week course strongly fulfilled those goals. The course is extremely practical and focused on real pitfalls you will encounter in analysis. It covers a good breadth of techniques and develops a strong sense of how to proceed with analysis once the data is cleaned.

If I had to critique the course, it had two main shortcomings:

1. The real challenge of any analytics problem is usually in the initial structuring of the problem, and then getting the data to answer the questions raised. I would have liked to see more discussion here. You would probably need a different course to more fully address the issues.

2. The last two weeks are focused on linear and integer optimization using spreadsheet models. Really not relevant for me. This felt like an add-on for completeness. I felt I had an adequate grasp of the issues, so I dropped the course. A better way to spend the final two weeks would have been to focus on writing functions and doing other programming tasks in R.

Still, I think this is a great course. I just stuck with it for the parts I was interested in. It beat just drumming around mailing lists and reading obscure web posts to get caught back up on R.

I think online, opportunistic learners are different from people taking a college course to get an overview of a field. This still has too much the flavor of a college course.”

For additional reviews, click here. 

Additional Note: Students will need to download R, a free statistical software package, to build models using the data sets provided.