How Do I prepare for Data Science Interview?
Appearing for any kind of interviews could increase your adrenaline level. Cracking interviews need massive amounts of preparation and research. More so within the given scenario of appearing for a knowledge scientists position, as only appropriate preparation and practice will get you cracking and performing well on a large day. If you’re an aspiring data scientist, you’re expected to possess a working knowledge or understanding, or the potential to perform over multiple firms, with a bag full of skills.
Continue reading to understand a quick step-by-step approach on specific areas of Aptitude, Technical know-how, and skillsets required to not only clear the interview but to also excel in the field of Big Data and Machine Learning.
You can watch this Video, to clear the Basics
What is Data Science In Simple Terms | Eduonix
The thing about data science is that its application, and hence expectation across industries varies to an outsized degree. The role is interpreted differently across companies, some might call a PhD Statistician as a data scientist, to others, it means proficiency in excel, while to some it may mean a generalist in Artificial intelligence and Machine Learning.
Step 1
read the work Profile, specifically for Skills, Tools and Techniques. If the work description isn’t self-explanatory or intimately, then some research on the corporate is non-negotiable. Be clear as to what type of data scientist position you are applying for.
The interview is typically a mixture of a flair Analysis, Technical Knowledge, Attitude Analysis.
The most organisation in recent times test the applicant on fundamental topics to gauge their fit in the company, attributes like Language Comprehension, Analytical Reasoning, Quantitative Aptitude etc…, can be easily cleared by reading abreast of an equivalent to brush your skills.
Step 2
Brush abreast of important and relevant concepts like these before the interview.
To test your technical understanding on the topic, most likely there’ll either be a technical round or an assessment, case study, which can essentially gauge your knowledge in statistics, programming, machine learning etc…, make sure you are fluent in relevant languages like R, Python, SQL, Scala and Tableau.
Step 3
will be to brush up on elementary topics like….
Probability — Random variables, Bayes Theorem, the Probability distribution
Statistical Models — Algorithms, Linear Regression, Non- Parametric Models, Time Series
Machine Learning, Neural Networks.
So here, essentially you will be tested by the medium of a case study or discussion, on your problem-solving capabilities. It will help if you’re ready to define the matter for them on the presented scenario, and link an equivalent to the suggested solution and its impact on business. While doing so, cite samples of case studies, or research papers for supporting the suggested solution.
Step 4
while you may come with the required skill sets and qualities, ensure through-out the interview you show the willingness to learn and flexibility in adapting to the current organisation, as Data Science and its applications are unique.
Step 5
to have a tight resume and pre-empt on ways you will link your experience with the given position during the course of the interview.
Step 6
carry out data science projects specifically if you are a fresher, there are many public domains available for the same. In addition, it is also advisable to take up MOOCs — Massive Open Online Courses to gain exposure to different as well as focused applications
I Found the One by EDUONIX Quite Relevant
Learn Data Science by Building Project Using R Programming
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Remember, in recent times the role of a knowledge scientist is viewed as someone who can bridge the gap between multiple features of a business. So it’s not expected or required of you to be a specialist altogether the aspects, but you ought to be ready to link the features, idea and provide solutions across domains. To stand apart in an interview you should not only show your individual strength and domain expertise but come across as a person with enough management skills, along with good communication and technical skills who can blend and get to the crux of a problem.