Project Recap: BCG GAMMA

The BCG GAMMA Case Project was our biggest project as a club to date. 30 participants (six teams of five) gathered at BCG’s Seattle office on October 30th for the case kickoff. The case focused on a real-life challenge BCG consulted on in the past and teams were tasked with building random forest models to determine factors affecting customer churn. Over the three weeks following the kickoff, teams worked to develop models using R and Python primarily. On November 21st, the teams presented their recommendations to BCG consultants.

The case was a textbook example in fulfilling our club’s mission to bridge the gap between the traditionally technical and nontechnical disciplines. We accomplished this by balancing each team with both business/econ students and info/CS/data science students, and by pairing graduate and undergraduate students. The outcome? Everyone learned something new about their fellow huskies!

We couldn’t have been more impressed by our peers at the University of Washington, who presented some amazing case projects to BCG GAMMA last week! Thank you to Allen Chen, Spencer Barnes, and Annie Lai for judging the presentations, it was a blast to participate! A major shout out to Nam Pho for the initial connection and for mentoring club leadership throughout the process! A wonderful learning experience for all involved.

Recap 11/13/2019: Predictive Analytics Introduction Workshop

For this week’s workshop, we covered the basics of predictive analytics by introducing the concepts of explanatory analysis, linear regression, decision tree and random forest. The students during the workshop were able to learn how to explore a dataset in R and build a linear model to predict the future. Please see below for a summary of the workshop and link to the slides!

Covered in Workshop:

  • Went over basic R syntax & dplyr package
  • Real-world examples of predictive analytics
  • Importance & methods of cleaning data
  • Introduction of linear regression
  • Evaluation & interpretation of linear regression
  • Introduction of classification
    • Decision Trees
    • Random Forest
  • Books and classes recommendation to study data science

Workshop Slide