Think of a project where you were working with a relatively large data set. Describe to me the process you had to take to gather and prepare the data for analysis.
Working with large data sets can present challenges, so hiring managers want to know that you have the experience to handle them if they arise. They want to be sure you have encountered challenges in your previous work and learn about the steps you took to overcome them. They are also interested in whether the project you worked on and the impediments you encountered are similar to the ones their data analyst team deals with.
"I have had experience working with large data sets delivered to us from outside vendors. Many times, these data sets were survey responses for marketing research projects with large sample sizes. Upon first receiving the data set, I checked the validity of the data by running predetermined frequencies and queries. Doing so would often reveal issues such as missing data, data type issues, and errors in skip patterns within the survey. I would work with the vendor to correct these issues before beginning further analyses of the data. Once the data issues were resolved, I would load the data into a data analysis tool to begin my analysis. Sometimes I would work with a data engineer to load it into an appropriate tool that could handle the size of the data set."
Walk the interviewer through the project you plan to present step-by-step. Share any challenges you might have faced and how you successfully overcame them. If you have been fortunate enough not to face any challenges, stick to the details of your project and the steps you took while working with the data.
Community answers are written by other candidates practicing this set. Reading how others frame the same question is one of the fastest ways to sharpen your own.
Anonymous Answer
When conducting my applied econometrics dissertation, I created a dataset in excel with columns including, year, country, and growth rate. The data set was for 70 countries and had around 1750 rows. I verified that each data source was legitimate before using it.
During my technology internship, I had to restructure extremely large datasets for our clients to meet the import requirements of the in-house software. I leveraged excel tools such as VLOOKUP here to optimize the process.
Cindy's Feedback
Great start, and it sounds like you have a lot to work with. I'd recommend adding more detail to spell out your process. The question is seeking a step-by-step answer that reveals a sound, accurate process.
Anonymous Answer
I have worked with large data sets. I have used the data set that is available publicly, therefore, I never got the opportunity to gather them, however, I have to spend a lot of time preparing and cleaning data for analysis. I start with running some queries for exploratory analysis. Doing this reveals issues such as missing data, data type issues, and errors in skip patterns within the survey. If possible, I would reach out to the right person to understand the source of error and come up with the right way to address and resolve these issues before the data analysis. When the issue gets resolved, I would start working with the analysis and always reach out to the right person if I need some help such as if I need a technology-related issue, I would reach out to a DBA or data engineer or if it is domain related issue, then I may reach the knowledge expertise or data steward to understand more and more.
Marcie's Feedback
It sounds like you have solid experience working with large datasets, which is great. You've done an excellent job explaining how you have cleaned and prepared data for analysis. You state that you don't have experience in gathering data because all of the data you've worked with to date has been available publically. But even so, didn't you have to take the data from that location? Despite the fact that it's public, you still had to gather and compile it, right? If so, definitely don't discount yourself when it comes to gathering data. Consider rephrasing your answer as seen below.
I have a lot of experience in working with large datasets. I have efficiently gathered and compiled data from publically available sources many times before and have also spent significant time cleaning and preparing data for analysis. In general, I start by running some queries for exploratory purposes. This tends to reveal issues like missing data, mismatched and missing data types, and skip pattern errors. If needed, I reach out to the right person to determine the source of the error and then I devise a way to address and resolve it. Once the issue has been resolved, I start carrying out the analysis, collaborating with various teams as needed.
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