Data Analysis Interview Questions

Jiggy - "import pandas as pd \# Sample data for the employees table data = { 'id': [1, 2, 3, 4, 5], 'first_name': ['John', 'Jane', 'Jim', 'Jake', 'Jill'], 'last_name': ['Doe', 'Smith', 'Brown', 'Taylor', 'Wilson'], 'salary': [30000, 25000, 45000, 20000, 35000], 'department_id': [101, 102, 103, 104, 105] } \# Creating a DataFrame from the data df = pd.DataFrame(data) \# Sorting the DataFrame by 'salary' in ascending order and selecting the top 3 lowest earners lowestearningemployees = df.sort_values(by="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

Catherine T. - "We want sales to grow, in order to have a growth in revenue. And customer usage as well as it allows to see if our product lead more engagement from our users. So to be able to see this overall evolution I would make a line chart for both : Sales : with month on x-axis and sales revenue on y-axis Customer Usage : with month on x-axis and a KPI allowing to measure customer usage (nblogins or nbsessions or nbgamesplayed, ... depending on the industry) on y-axis Moreover, after knowing th"See full answer

Sean L. - "df.loc[ isin()] is the crucial part of the solution."See full answer

Akash D. - "Investigation clear understanding the true cause"See full answer

Gabriel P. - "Hi, my solution gives the exact numerical values as the proposed solution, but it doesn't pass the tests. Am I missing something, or is this a bug? def findrevenueby_city(transactions: pd.DataFrame, users: pd.DataFrame, exchange_rate: pd.DataFrame) -> pd.DataFrame: gets user city for each user id userids = users[['id', 'usercity']] and merge on transactions transactions = transactions.merge(user_ids, how='left"See full answer

Arshad P. - "Schema is wrong - id from product is mapped to id from transactions, id from product should point to product_id in transcations table"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

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