Preparing For Data Science Interview

Preparing For The Various Rounds Of Interviews:

Aptitude Test

As the most basic round for any interview, it covers fundamental topics such as English language comprehension, quantitative aptitude and analytical reasoning. While this round requires minimal preparation, lazily scanning through your Wren and Martin and high school grade quantitative aptitude questions will certainly help in brushing up the important concepts.

Technical And Problem-Solving Interview Round

This is where your technical grasp over the subject is tested — especially your programming language proficiency, knowledge of statistics, optimisation and machine learning. The main languages that one is expected have mastery over are Python ,SQL, R, Scala and Tableau. Knowing Java and C++ also helps in adding depth to your programming skills. Since this round will deal with the brutal basics of the languages which you have stated in your CV, brushing up on their basics is of utmost importance.

Both freshers and experienced candidates will be questioned on key and rudimentary topics like:

• Probability — Random Variable, Bayes Theorem, Probability distribution
• Statistics — Sampling Theory, Hypothesis testing, Summary statistics
• Statistical models — Linear Regression, Non-parametric models, Time Series
• Machine Learning — Bayesian ML, SVM, Decision Tree, Logistic Regression
• Understanding of neural networks

THE PROBLEM-SOLVING ROUND OFTEN INVOLVES A CASE STUDY WHICH REQUIRES THE CANDIDATE TO DEFINE THE PROBLEM FOR THE SCENARIO PRESENTED, AND EXPLAIN THE BUSINESS IMPACT OF THE SOLUTION.

During the process, bringing in examples of case studies and findings of research papers to support the solution will help improve the candidate’s score. The round may also require you to evaluate the robustness, scalability, implementation issues, and so on, of an existing plan and provide alternatives. Freshers may also be asked to explain the mandatory projects carried out for the fulfilment of their academic courses and the rationale behind the methodology and solutions. Hence, it is important to be well-versed with your projects.

In the case of more experienced candidates, they may be tested along similar lines. But they may be asked to talk about their real world projects, questioned on the related domains and the impacts of their model on the business.

Willingness To Learn

The debate surrounding “building talent vs buying talent” has employers split into two groups. On one hand, building talent helps employers incubate and nurture talent according to the needs of the company, while on the other hand, buying talent provides the incentive of hiring people with highly specialised skills. As a data science aspirant, it is important to showcase the skills you have already acquired and honed, but also express willingness to learn in the current job and being open to adaptation.

Showcasing Your Inner “Unicorn”

Roger Huang of Springboard says:

“A DATA SCIENTIST IS A UNICORN THAT BRIDGES MATH, ALGORITHMS, EXPERIMENTAL DESIGN, ENGINEERING CHOPS, COMMUNICATION AND MANAGEMENT SKILLS, BUT THEY AREN’T SPECIALISTS IN EVERY ASPECT.”

In the current business environment, a data scientist’s role is one of a bridge between multiple facets of a business. Though it is impossible to be an expert in all aspects of it, a data scientist has the unique distinction of being able to ideate and provide solutions across multiple disciplines. Thus, this point ties in with the previous one where it is important to make your technical individuality felt but in the process show the potential employer that you have what it takes to be the unicorn that a good data scientist is expected to be.