1njiku/sb_take_home_challenge

SB_Take_Home_Challenge

Data Analysis Interview Challenge

Part 1 ‑ Exploratory data analysis

The attached logins.json file contains (simulated) timestamps of user logins in a particular geographic location. Aggregate these login counts based on 15­minute time intervals, and visualize and describe the resulting time series of login counts in ways that best characterize the underlying patterns of the demand. Please report/illustrate important features of the demand, such as daily cycles. If there are data quality issues, please report them.

Part 2 ‑ Experiment and metrics design

The neighboring cities of Gotham and Metropolis have complementary circadian rhythms: on weekdays, Ultimate Gotham is most active at night, and Ultimate Metropolis is most active during the day. On weekends, there is reasonable activity in both cities. However, a toll bridge, with a two­way toll, between the two cities causes driver partners to tend to be exclusive to each city. The Ultimate managers of city operations for the two cities have proposed an experiment to encourage driver partners to be available in both cities, by reimbursing all toll costs.

1. What would you choose as the key measure of success of this experiment in encouraging driver partners to serve both cities, and why would you choose this metric?

2. Describe a practical experiment you would design to compare the effectiveness of the proposed change in relation to the key measure of success. Please provide details on: a. how you will implement the experiment b. what statistical test(s) you will conduct to verify the significance of the observation c. how you would interpret the results and provide recommendations to the city operations team along with any caveats

Part 3 ‑ Predictive modeling

Ultimate is interested in predicting rider retention. To help explore this question, we have provided a sample dataset of a cohort of users who signed up for an Ultimate account in January 2014. The data was pulled several months later; we consider a user retained if they were “active” (i.e. took a trip) in the preceding 30 days. We would like you to use this data set to help understand what factors are the best predictors for retention, and offer suggestions to operationalize those insights to help Ultimate.

1. Perform any cleaning, exploratory analysis, and/or visualizations to use the provided
   data for this analysis (a few sentences/plots describing your approach will suffice). What 
   fraction of the observed users were retained?
2. Build a predictive model to help Ultimate determine whether or not a user will be active
   in their 6th month on the system. Discuss why you chose your approach, what
   alternatives you considered, and any concerns you have. How valid is your model? 
   Include any key indicators of model performance.
3. Briefly discuss how Ultimate might leverage the insights gained from the model to
   improve its long-term rider retention (again, a few sentences will suffice).