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How to Prepare for Data Science Interviews?

After only a decade and the 2.5 quintillion bytes of data generated every day carries such immense value in revolutionizing enterprise operations. Today, data-driven decisions matter most in business. This explains the high demand for professionals who can crunch data and extract value to inform decisions. Top third-performing companies in various industries attribute their increased productivity and profitability to data-driven decision-making. The data science field is growing rapidly, and the future is promising for aspiring data scientists as more companies seek to hire top talent to be part of strategic management.

The data science job market is expected to grow at 30% this decade, and the annual median salary for data scientists has just crossed the $100,000 mark. In addition to acquiring a data science certification and maybe earning some years of experience in the industry, you also need to learn the tips and tricks of preparing for an interview to ace your first and subsequent interviews. This is because despite there being a high demand for data science skills, associated roles remain highly competitive. It is important to note that the interview is more than merely a demonstration of technical skills.

Here are some valuable guidelines for preparing for a data science interview.

What does a data scientist do?

A data scientist is a professional who uses his/her math, statistical, and programming skills to gather and analyze large sets of data to identify challenges and create data-driven solutions that meet specific business needs. These professionals are good with numbers and can work with massive sets of structured or unstructured data through collection from multiple sources, wrangling, modeling, analyzing, and interpreting data.

Data scientists also develop pipelines, infrastructure, architecture, and other data tools that support a streamlined flow of data throughout the data life cycle. They work closely with the management of an organization and other stakeholders to understand business requirements and objectives and determine how these can be achieved through the insights gained from data.

Experienced data scientists are sometimes responsible for formulating data policies and best practices for the entire data life cycle in the organization, right from collection, pre-processing, storage, analysis, and interpretation.

Important data science skills

When interviewing for a data scientist role, interviewers look beyond the technical skills to determine whether a candidate is a good fit for the position. As we have seen above, data scientists work together with management and other business stakeholders. Therefore, in addition to crunching data and interpreting analyses, they also need to communicate analysis findings and data-driven decisions in a way that others understand.

Some top skills for data scientists include:

Technical skills

  • Math and statistics
  • Python, R, Java, Scala, and SQL languages
  • Big data frameworks like Apache Spark and Hadoop
  • Machine learning, deep learning, and natural language processing
  • Data mining
  • Visualization

Soft skills

  • Effective communication and presentation
  • Analytical thinking
  • Critical thinking and problem-solving aptitude
  • Leadership
  • teamwork
  • Business acumen

Are Data Science Interviews Tough?

Like any other interview for a technical role, interviewing for a data science role can be intimidating. You are expected to tackle all or most of the technical questions, including mathematics, statistics, and coding, as well as scenario questions. Some interviewers will ask live technical questions to test your knowledge and skills in real-world settings.

Also, you need to have researched and mastered the information about the hiring company and the open position. Finally, it can be hard to convince the interviewing panel that you are a good fit for an open position that demands more years of experience than you have.

For this reason, in addition to having an in-depth understanding of fundamental concepts of data science, look for the best ways of proving your skills before the interviewing panel. This could include a strong project portfolio and contribution to community forums.

How Do I Prepare for a Data Science Interview?

Whilst you will research and understand data science concepts, the goal of the interview is to test how well you can apply your knowledge and design effective solutions to business challenges in real-world situations.

1.  Brush up your coding skills

A data science job rarely lacks coding interview questions. Coding questions are asked in a data science interview to test a candidate’s technical skills, thought process, and programming skills. Do many data science practice questions, particularly on data manipulation, SQL and other popular languages, algorithms, data structures, and best practices in programming. Also, focus on solving real-world problems to hone your thought process and logical and critical thinking ability.

2.  Data science fundamentals

Whether you are seeking an entry-level or experienced level position, you need to demonstrate your knowledge of fundamental data science concepts. Brush up on data wrangling, exploratory data analysis, machine learning, deep learning, principle component analysis, outliers, data visualization, data scaling, data imputation, and other core data science concepts.

3.  Mock interview

Having prepared adequately for the coming interview, you may find that you still need to build confidence to face the interviewing panel. Practicing with a mock interview to help you ease tension and identify which areas you need to polish before attending your interview.

4.  Research the company and the role you are applying for

At the very basic, your skills should match those required in the advertised position. Read the job description properly, take note of the key responsibilities for the role and be sure that you can deliver if given a chance. This also helps you to anticipate and prepare for the topics that are highly likely to be covered in the interview questions.

Secondly, you want to demonstrate your enthusiasm for the role by researching more about the company that you want to work for. What products/services do they offer, and how can the data scientist role add value to product delivery? This will also help you to ask relevant questions in the interview if given a chance.

Check out the company website, its mission, vision, and objectives, go through its service charter, know about its management and board of directors, and any other information that may be helpful during your interview. Also, researching the main interviewer in the panel will help you prepare adequately for your interactions in the interview. Finally, connect with key employees on social sites if possible.

5.  Your project portfolio

A strong project portfolio gives you the added advantage in the interview as it showcases your expertise in programming languages, exploratory data analysis, machine and deep learning, and your personal interests in data science. Take time to review your project portfolio and tailor it to the role you are applying for. Focus on the most relevant and recent projects, and be sure to showcase your technical ability, communication skills, and other soft skills in your project portfolio. Also, revise your projects thoroughly and know them in detail.

Several ways that you can showcase your portfolio are:

  • A portfolio website
  • An active GitHub profile
  • A Kaggle account
  • Competitions and hackathons
  • A professional LinkedIn profile
  • Contribution to forums and open-source projects

6.  Tailor your resume

It takes the first 8 seconds of going through your resume for a recruiter to decide whether or not you are the right fit for the job. You need to tailor your resume to the role you are applying for to create a good impression. Your resume should detail your:

  • Contact information
  • Career summary
  • Work experience
  • Relevant projects
  • Data science skills
  • Educational qualifications
  • Awards/recognitions
  • Certifications/internships

7.  Your turn to ask

Many recruiters will give you a chance to ask questions at the end of the interview. It is worth preparing the questions to ask as these will demonstrate your interest in working for the company. You may want to ask about the company itself, its organizational structure, product offerings, tools and technologies used by the company, the data science team, the availability of opportunities for growth, and other questions associated with the data science role.

 Conclusion

The data science job market is growing rapidly. Data science interviews test your knowledge and skills of data science concepts and your ability to apply them to solve business challenges. Be sure to brush up on your knowledge, and build both technical and soft skills and business acumen. Also, don’t forget to practice your answers and presentation in the interview.