Recap 2/26/2020: Accounting & Finance Analytics Special Topic

This week, we covered a wide range of finance and accounting topics including:

  • Foundational Accounting Principles
  • Vertical Statement/”Common-size” Analysis
  • Fraud Detection
  • Importing and Analyzing Stock Data in Rstudio
  • Single Stock & Portfolio Return Calculations in R
  • Technical Analysis vs. Fundamental

We could spend hours covering each one of these topics in depth, please let us know if you’d be interested in that by emailing us.

For those who couldn’t make it, below are the slides and files we went over. The R markdown file has descriptions for each code block so you can follow along.

Slides, R Markdown File, Financial Statement Analysis (DAL)

Content Based Filtering in Recommendation Systems Recap

Content based filtering is one of the most common recommending approaches. It provides recommendation based on items user currently likes or uses. For example, if a user likes the movie Frozen, content based filtering will find movies similar to Frozen according to movie characteristics such as movie category, producers, actors and movie length etc.

“The steps in recommending products or contents to the user in content based filtering are as follows:

  • Identify the factors which describe and differentiate the products and the factors which might influence whether a user would buy the product or not,
  • Represent all the products in terms of those factors, descriptors or attributes,
  • Create a tuple or number vector for each product that represents the strength of each factors for the product,
  • Start to look at the users and their histories to create a user profile based on their history. It will have the same number of factors and their strength would indicate how much influenced the user is towards that factor,
  • Recommend the user those products that are nearest to them in terms of those factors.”

For more, please refer to the original article below:

Article Link: https://medium.com/@rabinpoudyal1995/content-based-filtering-in-recommendation-systems-8397a52025f0

Recap: Guest Speakers from Symetra

This week we welcomed Chris, Denise, Jake, and Jennie from Symetra to come speak about their company and their data analytics division! We learned about some of the projects they work on, the challenges they face, and the best things about working at Symetra.

Symetra is looking for interns this summer! You can learn more and apply here:

Symetra Internships

Recap 1/15/2020: Basics of Data Science Workshop

For this week’s workshop, we went over the data science realm in general and talked about various tools leveraged by data scientists and analysts. Please see below for a summary of the workshop and link to the slides!

Covered in Workshop:

  • Debuted a sleek new look for our slides and logo!
  • Discussed our club’s purpose
  • Overview of various aggregated resources
  • The data science profession and their toolset
  • Overview of data visualization
  • Shared student projects (links on slides)
  • Discussed machine setup steps
  • Went over basic R syntax & Dplyr package

Workshop Slides

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 11/20/2019: Special Topic: Random Forest Using Python

For this week’s special topic, we demonstrated how to build a random forest model with real-world income data using Python and an example of how to predict a person’s income range with given information.

Covered in Special Topic:

  • Explanatory analysis in Python
  • Lambda function in Python
  • Python DataFrame
  • One-Hot Encoding vs Label Encoding
  • Training and Testing
  • Model Training
  • Model Prediction
  • Case Study of predicting one individual’s income range
  • Confusion Matrix
  • Differences of accuracy, precision, and recall

Special Topic Notebook

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