Skip to contentSkip to footer

Data Analyst Junior Interview Questions

Salaries
Interviews

1,602 data analyst junior interview questions shared by candidates

Home Assignment: Scenario - Analyze visitation patterns at Stop & Shop @ 2335 Dixweli Ave Hamden CT Data Sample 1 . Venue Visits - raw visits (March 1 st 2019 - August 31) Each record represents a device visit to the venue. 2. Devices Daily Activity - device (user) activeness per day; a record per device per day in the sample (i.e. for the devices appearing in the Venue Visits file). Coverage varies from 0 [no coverage at all that day] to 1 [full coverage] | Required Delivery - share Google Drive folder with the following items: • A customer-facing Google Slides deck which outlines your findings and insights you would add to them. Note: You will be evaluated on the packaging (look and: feel). • Python file with the script you used to analyze the data and generate the results (preferably Google Colab notebook, but any other type works i.e Jupyter notebook/ py File) • Google Spreadsheet file you used for visualizing the results and preparing the graphs, in case you used one Guidelines • Use Python to calculate the numbers • NO NEED to generate the visualizations/graphs using Python (only if it's easier for you] , • Use Google Spreadsheets to generate the graphs once you have the numbers on hand (or any other visualization tool if easier for you). Tip: You can use Kepler -g| to generate heatmap if needed Analysis • How does the user’s activity look like? Analyze the user's activeness distribution (number of active days) and present It User is defined as active on a given day if his coverage >- 0.75 For example, looking at the below record, we will count this day (7/1/2019) this user as coverage is >= 0.75 Note: you are not allowed to use Pandas/Dataframes in this question only (you CAN use pandas in the rest of the assignment) You should use Python standard Data Structures (lists/d ktionaries etc.) to solve this part. • Visitation Patterns - Placer's core metric is the foot-traffic estimation (I.e estimated number of visits) Analyze the visits and present the visitation patterns in the best way t represents the data provided as you see It. Notes: - The graphs should include visits of active users only v sits v/here the associated user (I.e. device id) was active for more than 90 days during the entire period (using what you've done in the first question!) - Use visit weight to present any visits data, visit weight is the extrapolated value of the raw visit, so if you want to get to the estimated number of visits to the location, you would sum on this field (as opposed to simply counting the records). - ♦ How to define a 'Loyal Customer? J Analyze the distance of the customer home location to the venue (think what is the best way to present this data) - How would you define a ‘loyal customer? Think of thresholds that make a Customer loyal
avatar

Junior Data Analyst

Interviewed at Placer.ai

3.6★
Apr 19, 2022

Home Assignment: Scenario - Analyze visitation patterns at Stop & Shop @ 2335 Dixweli Ave Hamden CT Data Sample 1 . Venue Visits - raw visits (March 1 st 2019 - August 31) Each record represents a device visit to the venue. 2. Devices Daily Activity - device (user) activeness per day; a record per device per day in the sample (i.e. for the devices appearing in the Venue Visits file). Coverage varies from 0 [no coverage at all that day] to 1 [full coverage] | Required Delivery - share Google Drive folder with the following items: • A customer-facing Google Slides deck which outlines your findings and insights you would add to them. Note: You will be evaluated on the packaging (look and: feel). • Python file with the script you used to analyze the data and generate the results (preferably Google Colab notebook, but any other type works i.e Jupyter notebook/ py File) • Google Spreadsheet file you used for visualizing the results and preparing the graphs, in case you used one Guidelines • Use Python to calculate the numbers • NO NEED to generate the visualizations/graphs using Python (only if it's easier for you] , • Use Google Spreadsheets to generate the graphs once you have the numbers on hand (or any other visualization tool if easier for you). Tip: You can use Kepler -g| to generate heatmap if needed Analysis • How does the user’s activity look like? Analyze the user's activeness distribution (number of active days) and present It User is defined as active on a given day if his coverage >- 0.75 For example, looking at the below record, we will count this day (7/1/2019) this user as coverage is >= 0.75 Note: you are not allowed to use Pandas/Dataframes in this question only (you CAN use pandas in the rest of the assignment) You should use Python standard Data Structures (lists/d ktionaries etc.) to solve this part. • Visitation Patterns - Placer's core metric is the foot-traffic estimation (I.e estimated number of visits) Analyze the visits and present the visitation patterns in the best way t represents the data provided as you see It. Notes: - The graphs should include visits of active users only v sits v/here the associated user (I.e. device id) was active for more than 90 days during the entire period (using what you've done in the first question!) - Use visit weight to present any visits data, visit weight is the extrapolated value of the raw visit, so if you want to get to the estimated number of visits to the location, you would sum on this field (as opposed to simply counting the records). - ♦ How to define a 'Loyal Customer? J Analyze the distance of the customer home location to the venue (think what is the best way to present this data) - How would you define a ‘loyal customer? Think of thresholds that make a Customer loyal

Viewing 1301 - 1310 interview questions

Glassdoor has 1,602 interview questions and reports from Data analyst junior interviews. Prepare for your interview. Get hired. Love your job.