Tips to Crack That Data Analytics Job Interview and Start Earning Big Bucks

Have you been applying for data analytics jobs? Well how do you feel when you get that all important call back asking you to attend an interview? Elated and excited, but just a little bit tense as well? Well it’s natural, but what you do need to do is some very specific preparation before the interview, so that you go in well prepared and ready to nail it.

I came across this very pertinent article in the GlobalPost , that I would like to share with you. The article titled, “How to Face an Interview for Data Analyst”, gives some very sound advice for someone attending a Data Analyst interview. It talks about the broad types of topics that interviewers could focus on, like Data Gathering or Data validation, while also giving tips on the best way to answer them.

This is also a good time to revisit some important tips for interviews that Sarita Digumarti, COO of Jigsaw Academy shared in an earlier post 5 Tips to Crack an Analytics Interview:

1. Allocate most time in your preparation process for reviewing your knowledge of the analytical tools specified :

Be very proficient with the analytics tool specified: Most often for junior level positions, the most important criteria in an interview tends to be expertise with an analytical tool (like SAS or R). The emphasis tends to be around data processing and preparation. Spend time reviewing concepts of data import and manipulation, especially how to read non-standard data (mixed data formats, multiple input file types etc), how to join multiple datasets efficiently, how to conditionally select columns, rows or observations in data, and finally, how to do heavy duty processing, typically macros or SQL

2. If you have prior experience with analytics or data related processing or analysis, review the business process end to end as part of your interview preparation:

If you have prior work experience related to analytics or data, interviewers will certainly spend time asking you to explain the business process and the responsibilities of your specific role. They are looking for you to have a broad understanding of the end to end business process, and where your particular role fits in. It is important for you to show that you understand the source of your data, how it is processed, and how it is ultimately used.

3. Be prepared with at least two business case studies:

Interviewers will want to assess your knowledge of business analytics, not just the tool proficiency. Spend time reviewing analytics projects you have worked on if you have prior analytics experience or training. Be prepared to tell them what the business problem was, what were the data processing steps, what was the algorithm used for creating the models and why, and how were the model results implemented?  You may be asked about challenges you faced at any of these stages, so do review issues and challenges in you past projects and how they were resolved.

4. Review statistical concepts:

Since analytical algorithms are based on statistical concepts, you will need to be prepared to answer questions related to fundamental statistical concepts, like hypothesis testing outcomes and rejection criteria,   model validation measures, and statistical assumptions that need to hold for implementing different types of algorithms. A quick review of statistical concepts is a must as part of the interview preparation process.

5. Communicate effectively:

All the preparation in the world is not enough if you do not communicate effectively. Mentally practice answering mock questions. Focus on questions related to past experience and business process, with full answers so that you are not thinking too much on the fly at the actual interview. Of course you cannot anticipate every question, but if you spend time articulating answers to some questions, you will be better prepared with coherent answers.

Suggested Read:

Language of SAS related questions asked in interviews 

Got an Interview Call for an Analytics Position? Go Prepared

66 Job Interview Questions For Data Scientists