Hack.Diversity Data Interview: Product Inclusion Analysis Challenge

Overview

This technical interview challenge will evaluate your skills in data cleaning, exploratory data analysis (EDA), feature engineering, and predictive modeling within a product inclusion context. Your task is to analyze user engagement with inclusive product features, focusing on accessibility trends and demographic impacts. The final submission should be in the form of a Jupyter Notebook or RMarkdown file, demonstrating your analytical skills and ability to extract insights.

Objectives

This challenge assesses the following skills:

• Data Cleaning: Address missing values and handle outliers in key columns.
• Exploratory Data Analysis: Analyze user engagement and feature adoption.
• Feature Engineering: Develop new features to enhance your analysis.
• Predictive Modeling: Build a basic model to understand user behavior.
• Visualization Tools: Utilize tools such as Tableau Public for advanced visualizations, where appropriate.
• Version Control: Use GitHub to demonstrate a well-organized, meaningful commit history for each stage of your work.

Preparation and Setup

1. Fork the Repository: Clone the provided repository. If no starter repo is provided, create a new one and commit your work frequently.
2. Make Meaningful Commits: Ensure each significant task (e.g., data cleaning, feature engineering, etc.) has its own commit to track progress. Clear, descriptive commit messages will showcase your work methodology effectively.

Instructions

1. Retrieve and Inspect the Dataset

• Dataset Access: You have a CSV file containing simulated user interaction data with inclusive product features.
• Columns: The dataset includes columns such as:
  • user_id: Unique identifier for each user.
  • feature_used: Type of feature accessed (e.g., "Colorblind Mode," "Multilingual," "Visual Aid").
  • session_duration: Length of the user’s session (in minutes).
  • engagement_score: Engagement level on a scale from 1 to 5.
  • accessibility_settings: Count of customizations enabled (0-5).
  • age_group: Categorizes users by age group.
  • gender: Gender (e.g., "Male," "Female," "Non-Binary").
  • additional_attributes: Additional user demographics (e.g., "LGBTQ+" or "Person of Color").

2. Data Cleaning & Preparation

• Missing Values: Identify any missing values and decide how to handle them, especially in session_duration and engagement_score.
• Outliers: Detect and manage outliers in session_duration and engagement_score.
• High Accessibility User Classification: Create a new column called high_accessibility_user, classifying users with three or more accessibility settings as a binary label (1/0 or True/False). Explain your choice and its implications for model interpretability.
• Intersectionality Analysis: Add a new column that combines age_group, gender, and additional_attributes to facilitate intersectional analysis. For example, label combinations as “Young Adult - Female - Person of Color.”

3. Exploratory Data Analysis (EDA)

• Engagement Score Analysis: Visualize engagement scores across features and demographics (e.g., age group, gender, additional attributes). Highlight which demographics show the highest engagement for each feature.
• Feature Usage by Demographic: Examine patterns in feature_used across demographic attributes. Use your new intersectional column to uncover trends that may not appear when demographics are analyzed individually.
• Recommendation: Use Tableau Public for advanced visualizations such as interactive dashboards, which can provide deeper insights into feature usage and demographic trends. Export key visualizations for inclusion in your final report.

4. Feature Engineering & Predictive Modeling

• New Features: Create features such as:
  • Average session_duration per feature_used.
  • Average engagement_score by accessibility_settings and demographics (e.g., age group, gender).
• Predictive Model: Build a simple predictive model (e.g., logistic regression or decision tree) to determine if a user is a high_accessibility_user. Use variables such as age_group, gender, additional_attributes, engagement_score, and feature_used.

5. Insights & Recommendations

• Key Findings Summary: Summarize insights for each analysis step. Identify which demographics show the most engagement with each accessibility feature.
• Actionable Recommendations: Provide data-driven recommendations on how the company can enhance the adoption of inclusive features, particularly among demographics with lower engagement levels.

Submission Requirements

• File Format: Submit a Jupyter Notebook or RMarkdown file containing code, visualizations, and a 3-4 sentence summary for each analysis section.
• GitHub Submission: Push your work to GitHub, ensuring a meaningful commit history. Each major section of the analysis should have its own commit, clearly labeled (e.g., “Completed data cleaning” or “Finished EDA”).
• Google Drive Form Submission: It is required to submit your final Jupyter Notebook or RMarkdown file through this link.

Helpful Tips

• Exploratory Data Analysis: Use libraries like Matplotlib, Seaborn, ggplot2, or Tableau Public for visualizations.
• Predictive Model: Keep it simple with logistic regression or a decision tree.
• Version Control: Use clear commit messages for every significant update.

Evaluation Criteria

Your submission will be evaluated on the following:

• Data Cleaning & Preparation: Appropriateness and clarity of data handling.
• Insights: Meaningful analysis of trends in inclusive feature adoption.
• Modeling & Accuracy: Clarity in model choice and interpretation of results.
• Presentation: Clear explanations with supporting visualizations.

Good luck, and showcase your best analysis skills!