Data Analysis Interview Questions

Ravindra R. - "First, I want to understand why Airbnb is considering expanding into the restaurant space. Is the goal more user engagement, or unlock new monetization paths, or make the travel journey more memorable by connecting people with food? Next, I want to define what success would look like for this expansion. That could mean high guest engagement or a boost in revenue. Third, I will look at things like the volume of Airbnb travelers to the region, how big the dining market is, who the local competitor"See full answer

Ankit kumar S. - "A 25% drop-off rate at sign-up for a new app, especially one from Meta, indicates a significant challenge in onboarding and user retention. To analyze this, consider external, internal, and product-specific factors. Identify Potential Causes: External Factors:Competition: Are other platforms offering a more attractive or simpler signup process? Timing: Is there a recent major event or trend that might be influencing user behavior? User Experience: Is the signup process clunk"See full answer

Logan S. - "Clarify the Goal (What Does Success Look Like?) Asked: Why is DoorDash launching bikes for Dashers? Is it to: Expand Dasher availability in dense urban areas? Lower delivery times or costs? Increase access to work for those without cars? Each possible goal shapes the metrics differently. Broke Metrics into Categories: Operational Metrics Measure how well bikes are functioning as a delivery method. Average delivery time (bike vs car/walk) Order completion rate for bike Dashers On-t"See full answer

Ankit kumar S. - " logo Contact Interview Preparation Application Process Career Advancement Onboarding and Orientation Common Interview Questions Dashboard Creation Interview Questions and Answers Dashboard Creation Interview Questions and Answers What is a dashboard? Answer: A dashboard is a visual representation of key performance indicators (KPIs) and other important data, designed to provide a high-level overview of a specific area or business process. It typically uses charts, graphs, and other da"See full answer

Steve Y. - "Define: How is daily post view calculated Isolate Issues: Data issue Time period Geo IOS vs Android vs Web Correlated Metrics in the funnel DAU Time spent/ scrolls Engagement - likes, comments External factors Competitor actions Big events Internal factors Product launch Feature change"See full answer

Ankit kumar S. - "To analyze a year-over-year (YoY) slowdown in transactions, you should start by understanding the context, segmenting data, identifying potential drivers, and then formulating actionable recommendations. This involves a structured approach that includes a thorough examination of the data, both internally and externally. Understand the Context: Define "transactions": What specific transactions are being analyzed (e.g., website purchases, in-store purchases, total transactions)? Timeframe: Sp"See full answer

Rajdeep J. - "Let's take Google Fitbit as the product and analyze how to identify its competitors. 1. Identify Direct Competitors (Similar Wearables & Smartwatches) Fitbit is a fitness tracker and smartwatch brand, so direct competitors include: Apple Watch Series (Apple) Samsung Galaxy Watch (Samsung) Garmin Wearables (Garmin) Amazfit & Zepp (Huami) Xiaomi Mi Band (Xiaomi) These brands offer smartwatches or fitness bands with similar health-tracking features. 2. Use Launch Events for Insi"See full answer

Mark - "Questions: Should we consider US or Global? Is there any existing market insights/research around driverless cars adoption? Can we assume that driverless car technology is production / public ready by 2025? Is this focus on commercial or consumer use? Assumption: Let's look at US to start for our estimation since Driverless car regulation and adoption trend will probably differ per country Let's also assume we want to estimate market size for driverless cars in 2025 for consume"See full answer

Gabe M. - "My stab at this (feedback welcome): Total distance for last car 500 meters: Assuming mostly standard cars, average car length + gap = 5 meters. Acceleration time total 505 seconds: first car reacts slowest, accelerate in 10 seconds. Every other car needs 5 seconds (can see ahead). Takes 90 seconds to cover 500m: to cover 500m that means 90 seconds (20kph = 10km in 30min = 1km in 3min = 500m in 1.5m) A bit under 10 minutes = 90s + 505s. Probably a bit less as last car"See full answer

Vishal S. - "Product Understanding - Ads are what you see from companies as stories, posts, reels. Post are from users (connections). We have to design an experience which produces maximum engagement while generating ad revenue. Clarifying Questions - Is it specific to posts/stories/reels ? Is there an existing post to ads ratio or do we have to start from scratch? Is it specific to a device/OS? Is it specific to a region/user demographic? Assumption - Existing posts to ads ratio"See full answer

Himanshu G. - "To model ROI for a product launch, the first step is to define the timeline you're targeting Example 6 months post-launch, 1 year, or even 5 years. Tip: Start with a 1-year ROI projection to estimate near-term returns, and build a 3-year projection to evaluate growth and scalability. ROI is essentially the net return over that period: Profit=Revenue (within timeline)−Total Cost (from project start) Total Cost includes both fixed and variable costs incurred since t"See full answer

Aman M. - "I would want to start by asking a few clarifying questions? What business goal are we trying to achieve with this feature? What decisions will this feature help drive? Is calling feature (audio/video) currently available on 1:1 chats? Does the feature allow for cross-device switch? Once I get some clarity, assuming the goal is to increase user engagement and help drive repeat messenger usage with its user base. I would want to evaluate a few motivation for this feature with looking at"See full answer

Shatabdi P. - "def lowestearningemployees(employees: pd.DataFrame) -> pd.DataFrame: selectedcolumns = employees[['id','firstname','last_name','salary' ]] sorteddf = selectedcolumns.sort_values(by='salary', ascending=True) return sorted_df.head(3)"See full answer

Dr. chantel P. - "Currently, I work in banking fraud detection. Recently, my teammate and myself were asked to assist the Check Deposits team in enhancing their fraud detection ML solution by evaluating new data sources and eliminating past data sources for possible lift. We retrieved data from Hadoop and Netezza databases including deposit transaction data, mobile & computer device, authentication logs, account balance, and customer demographic data. We then created new and recreated old features including tem"See full answer