Preparing for Data Science Interviews

Abhishek Vijayvargia
Nov 1 · 3 min read

Data science, machine learning, artificial intelligence, deep learning, self-driven cars, super intelligent chatbots, face recognition and a lot of things are making a buzz today in the market. Using these buzz can increase the valuation of a startup by ten times or increase your salary by five times. Today a large number of software developers, business analyst, Software Quality analyst, product manager wants to become a data scientist. It always interests people to understand how Alexa or Siri understands the voice commands and serving them better with time.

Path to learn data science is simple. You start by a web search on machine learning and data science and get a lot of links providing free learning videos, blogs, hands-on tutorials. You choose the one which is coming either on the top of the search page or which has a small content and start learning it.

After a month or two of hard work, you get the idea, clearly differentiate what is machine learning and deep learning, can separate the problem in classification and regression and use an algorithm like linear regression or random forest to create the model in R or python. Now you feel comfortable, start looking for a data science job, get a long list and create a resume mentioning your skills. Companies take your profile, call you and start the interview. You get excited about your first job in data science. You revise the concept and get yourself ready for the interview. You feel confident in getting the job.

The interview starts with your introduction. Now interviewer jumps into the question asking more deeper about data science and you start feeling uncomfortable with your current learning. You know how regression work but overfitting? What is that? Yes, you know about regularization but what is the difference between two techniques? You learned it but never thought in terms of the computational difference between various techniques.

Today a good number of courses are available which can teach you the data science concept but you need to work hard for an interview. In this article and coming articles, I will discuss the skills needed to develop to crack data science interviews. I will write separate blogs for each of the skills and write what is needed in that.

To crack the data science interview we need the following skills

  • Programming language (R, Python, Java)
  • Knowledge of data structure and Algorithms
  • Data science pipeline (start from getting the data and making a model with good performance)
  • Good command over Statistics
  • Big data tools (For some profiles)
  • Mathematics (Linear Algebra, Calculus, Probability)
  • Data Exploration (Using tools or by programming language)
  • Data Formatting
  • Machine Learning Algorithms (Must have!!)
  • Recent advances in data science and machine learning (how a chatbot works, how search engine generate an accurate ranking of pages)
  • Projects which can highlight your data science work

I will go on these topics in more detail in upcoming blog posts. Stay tuned and keep learning!!

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