Senior Data Analyst Assessment

Welcome to the Rise Senior Data Analyst take-home test! This assessment is designed to evaluate your analytical, statistical, and problem-solving skills.

You will find two files in this folder:

• user_acquisition.csv: Contains data on user sign-ups and their first actions.
• investment_data.csv: Contains a log of user investment events.

Instructions:

Your primary task is to conduct a funnel analysis on the user journey from sign-up to their first investment. The goal is to identify points of friction and provide actionable, data-driven recommendations to the product team.

1. Data Preparation (SQL): Write a series of SQL queries to prepare the data for analysis. Your queries should:

   • Join the two datasets on the appropriate key.
   • Create a clean, unified view of the user journey.
   • Calculate key metrics for each step of the funnel (e.g., number of users at each stage, conversion rate from one stage to the next).

2. Analysis and Modeling (Python): Using Python with libraries like pandas, scikit-learn, and matplotlib, perform the following:

   • Funnel Analysis: Calculate the conversion rates between each stage of the funnel. Use statistical tests (e.g., chi-square test) to determine if there's a significant difference in conversion rates across different user segments (e.g., by signup source, country).
   • Churn Prediction: Build a simple predictive model (e.g., a logistic regression or decision tree) to identify users who are likely to drop off before making their first investment. Explain your feature selection process and how you would evaluate the model's performance.

3. Visualization: Create a compelling visualization (e.g., a funnel chart, a series of bar charts) that clearly shows the drop-off points in the user journey. The visualization should be suitable for presentation to a non-technical audience.

4. Recommendations (Executive Summary): Write a brief executive summary that presents your findings and provides three actionable, data-driven recommendations for the product team. Your recommendations should be specific and backed by the data you analyzed. For example, if you find that a particular signup source has a significantly lower conversion rate, your recommendation should address that specific issue.

Submission:

Please submit a single document (PDF or Jupyter Notebook) containing the below to data@risevest.com:

• Your SQL queries.
• Your Python code and the resulting visualizations.
• Your written executive summary.