Kellogg | Mr. Danish Raised, US Based
GMAT 710, GPA 10.6 out of 12
Darden | Ms. Unicorn Healthcare Tech
GMAT 730, GPA 3.5
Stanford GSB | Mr. MBB to PM
GRE 338, GPA 4.0
Harvard | Mr. Sales To Consulting
GMAT 760, GPA 3.49
Chicago Booth | Mr. Semiconductor Guy
GMAT 730, GPA 3.3
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Wharton | Mr. Sr. Systems Engineer
GRE 1280, GPA 3.3
Tuck | Mr. Consulting To Tech
GMAT 750, GPA 3.2
Stanford GSB | Mr. Rocket Scientist Lawyer
GMAT 730, GPA 3.65 Cumulative
Stanford GSB | Mr. Navy Officer
GMAT 770, GPA 4.0
Darden | Mr. Stock Up
GMAT 700, GPA 3.3
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GMAT 760, GPA 3.9
Cambridge Judge Business School | Mr. Social Scientist
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Darden | Mr. Federal Consultant
GMAT 780, GPA 3.26
INSEAD | Mr. Consulting Fin
GMAT 730, GPA 4.0
Duke Fuqua | Mr. Enlisted Undergrad
GRE 315, GPA 3.75
INSEAD | Ms. Hope & Goodwill
GMAT 740, GPA 3.5
Harvard | Mr. Milk Before Cereals
GMAT 710, GPA 3.3 (16/20 Portuguese scale)
Chicago Booth | Mr. Guy From Taiwan
GRE 326, GPA 3.3
Darden | Mr. Leading Petty Officer
GRE (MCAT) 501, GPA 4.0
Columbia | Mr. NYC Native
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Tepper | Mr. Leadership Developement
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Harvard | Ms. Athlete Entrepreneur
GMAT 750, GPA 3.3
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GRE 326, GPA 3.58
Harvard | Ms. Ambitious Hippie
GRE 329, GPA 3.9
Stanford GSB | Mr. Unrealistic Ambitions
GMAT 710, GPA 2.0
Stanford GSB | Mr. Equal Opportunity
GMAT 760, GPA 4.0

Essential MOOC Courses In Business For June

Introduction to Computational Finance and Financial Econometrics

 

School: University of Washington

Platform: Coursera

Registration Link:  Introduction to Computational Finance and Financial Econometrics

Start Date: June 1, 2015 (10 Weeks)

Workload: Not Specified

Instructor: Eric Zivot

Credentials: A Yale Ph.D., Zivot has taught economics, statistics, finance, and applied mathematics courses at the University of Washington for 18 years. A consultant in the finance and software industries, Zivot conducts research on financial risk measurement and the econometric analysis of high frequency financial data. He has also received the university’s Henry T. Buechel Award for Outstanding Teaching.

Graded: Not Specified

Description: Using the R statistical programming language, students will learn how to “analyze financial data, estimate statistical models, and construct optimized portfolios.” In addition, students will create probability models to project asset returns and measure portfolio risk. Along with video lectures, students will complete assignments on Datacamp.com, which provides instant feedback on student computations.

Review: “Most of the class is spent in a detailed review of basic statistics, with an eye to applying it to financial data series. I really needed that, plus we were taught how to do all computations in R, with useful examples. Great treatment of confidence and the bootstrap methods. Final weeks were about basics of portfolio theory (efficient frontier, etc.) again enabling us to do all computations. I also appreciated the teacher mentioning that the theory’s value decreases when the market is unstable (as correlation increases) and showing how wildly the theoretic results can vary depending on when the data is collected. Overall I now feel confident with basic statistics (also beyond financial applications) and have continued to use R for statistics and data analysis since this class.” For additional reviews, click here.

Additional Note: Dr. Zivot recommends several texts before participating in the course, which are listed on the course link above.