I test drove the new Career Path feature at MyTechlogy and was pretty impressed by the kind of insights it threw up overnight. I think young professionals in data analytics or data science will find it helpful if they’re looking for data that will help them make a decision about their own careers. Last week I’d blogged about how to ace an IT interview. Most of those tips are easily applicable to data analytics interviews as well.
Data analytics is also a huge area when you consider the landscape of tools, technologies and methodologies that enable and support it, and people have different viewpoints about the scope of this discipline, and it begins and ends. The terms data analytics and data sciences are also commonly used interchangeable, although there are differences between the two. It’s easy to get confused and drawn into owning too many areas of intellectual or professional challenge in these spaces, so it helps to focus and prepare well for what interviewers in the area of data analytics are really looking for. So, in addition, to the ones in my previous article that was actually for IT professionals but could also be useful for data professionals, here are some tips that are specifically for those working in data analytics this time.
Know what your core capabilities are.
Everybody would like to hire the perfect data analyst, which is a combination of someone who can knows how to work with data to structure it, apply statistics to it, as well as write all the code to extract it, transform it and store it before and after it is processed. The fact is that most data analytics professionals are not really strong enough in every single one of these areas, and that’s ok. One should play to one’s strengths in an interview and not try to wing it in unknown areas as that could turn out to be a fatal mistake. Most employers understand this, and what they usually look to do is create a team of people with complementary strengths.
Have your case study portfolio ready.
What this means is to sift through your experience before the interview and recall various situations that each presented a different type of problem and how you solved it. There could be problems in data cleansing, or selection of the right statistical techniques, or of knowing which tool was the best suited, and so on. Given the range of topics that could be touched upon it would help to be ready in advance with suitable examples of your expertise in different areas, not that it may be necessarily required to be able to demonstrate many.
Display creativity, but don’t go too far!
Data analytics professionals are necessarily creative people. Employers would look for signs or examples of that creativity, and you may be aware of it. However, in demonstrating creativity, one has to stay within the boundaries of practicality and the discipline of organizational process and culture. People perceived to have ways of thinking that are too different all the time and in every situation may be judged as difficult to manage.
Be aware of competing tools and technologies.
You may have strengths in one particular set of tools or technologies but not have much exposure to competing ones. For example, one might have good experience with SAS, but not with R or Python. Even if one has no exposure to competing tools, it is good to acquire (through reading) a basic awareness about them and about how they could be different. A good data analytics person cannot afford to be too dogmatic about technology preferences as things keep changing and every company has its own analytics problem situations and solutions. One must keep an open mind, acknowledge new possibilities, and show an interest in knowing more about them. Versatility is good.
Display an appreciation for business realities.
Every analytics professional knows that if only she had access to more and better quality data, and could spend more time experimenting with one analysis technique after another, and keep refining outputs through more and more iterations one day the perfect model would be available, or the clearest analysis would be found. However the real world business workplace usually doesn’t have time for what could be done in a more academic setting. Being practical, displaying an understanding of cost and metrics, and appreciating that speed is of the essence in terms of methodology as well as processing time used to arrive at an outcome would be preferred over a consistently pedantic approach to solving problems.
Be communicative and pleasant.
Data analytics professionals have to work with colleagues in various functions and disciplines. They need to be able to communicate and build bridges, and preferable have leadership qualities. While this can be demonstrated through examples of success in previous assignments, additional credibility in this area can be earned by being communicative, expressive and amiable throughout the interview.