Data Analysis Interview Questions

Katherine B. - "We have detailed monitoring and meetings dedicated to discussing the health of the conversion business. When I’ve seen drops in the conversion rate, the first thing I do to diagnose the issue is to work backwards through the conversion funnel. For example, if I see a drop in user adoption rates, I will evaluate if there are any product experiments that could be negatively affecting adoption. Likewise, was there a technical outage that could have caused a drop? Segmentation and cohorting is also"See full answer

Katherine B. - "For ROI for strategic bets, we want to evaluate short term and long-term returns on our investment as well as ensuring we have quantitative and qualitative milestones to measure progress towards the long-term goal. For quantitative evaluation, I would first outline resource investment from upfront capital investment, infrastructure resourcing and clearly capture the opportunity cost of the investment. Then I would set leading success indicators, and business metrics over the timeline of the inv"See full answer

Archit G. - "line/ trend charts are the simplest method to identify churn "See full answer

Chao peter Y. - "The question is incomplete --- the code only passes if you return the data frame sorted by BOTH department name AND rank. While in the problem description, it mentions to only rank by department name: "The results should be ordered by department name." Not a big difference I know, but students shouldn't need to look into the solution to get the necessary knowledge to answer the question."See full answer

Himanshu G. - "First, I would start by defining what growth means in the context of this new feature whether it's user acquisition, engagement, retention, or revenue. Next, I’d identify clear KPIs that directly align with that growth goal. For example, if the feature aims to improve engagement, I’d track metrics like daily active users, session duration, or feature adoption rate. Once the KPIs are in place, I’d run an A/B test comparing user behavior with and without the feature. This would be followed by de"See full answer

Caleb S. - " import pandas as pd def findimprovingstudents(transcript: pd.DataFrame) -> pd.DataFrame: summary = transcript.pivottable(index='studentid', values = 'yearlygpa', aggfunc = 'sum',columns = 'year').resetindex() summary['average_gpa'] = round((summary[2023] + summary[2022] + summary[2021])/3,2) return summary(summary[2023] > summary[2022]) & (summary[2022] > summary[2021])] #yn > yn-1, yn-1 > yn-2, yn-3 debug your co"See full answer

Himanshu G. - "First, I’d start by checking the alignment of each idea with our core business goals. If any idea doesn't directly contribute to those goals, I’d deprioritize or eliminate it upfront. Next, I’d use a scoring model like RICE (Reach, Impact, Confidence, Effort), especially because effort is a critical factor when resources are limited. This gives us a structured and quantifiable way to rank the ideas. Once we have a prioritized list based on scores, I’d take it a step further and evaluate key as"See full answer

Sean L. - " debug your code below departments = pd.DataFrame({ 'id': [1, 2, 3, 4, 5], 'name': ['Reporting', 'Engineering', 'Marketing', 'Biz Dev', 'Silly Walks'] }) employees = pd.DataFrame({ 'id': [1, 2, 3, 4, 5, 6], 'first_name': ['John', 'Ava', 'Cailin', 'Mike', 'Ian', 'John'], 'last_name': ['Smith', 'Muffinson', 'Ninson', 'Peterson', 'Peterson', 'Mills'], 'salary': [20000, 10000, 30000, 20000, 80000, 50000], 'department_id': [1, 5, 2, 2, 2, 3] }) projects = p"See full answer