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Attendees posted a few of the questions before the webinar while some of the live questions were also answered. The audience seemed very interested in finding out about data science education and foundation requirements and how to enter the field as a fresh graduate with limited experience.

Q: How to handle LinkedIn invitations from strangers and how to respond to a recruiter reaching out?

The best way to respond to recruiters is to take time composing the reply. You want to present yourself as very interested in the company and their business. You also need to be appreciative of the fact that the recruiter is reaching out to you. You can talk about their products and services, a project they are working on, or any new development which may require new hiring. Present yourself as a potential problem solver for their business.

Q: What are some of the important questions to ask during the data science interview?

You can ask about what kind of data they are working with. the company could be working with highly problematic data and that’s the reason they are hiring an expert. They could be having a data modeling or data management problem. So, it’s a good idea to find out what data problem are they facing. This will give you insight into your day-to-day activities and the job role.

Q: How to answer what are you expecting from this role?

This question is another way of asking how the company fits into your overall career plan. Here you want to justify your current position, maybe you are just entering the field of data science or switching careers or companies. You need to justify why you’re choosing this particular company and the role.

Q: Sharing new ideas with the interviewers about the company would be a good sign?

It is definitely good to share new ideas but keep in mind that first, you need to understand the problem they are having. To propose a solution, you need to have a good understanding of the problem.

Q: How can I answer questions about the most important metrics for an ad marketing campaign?

To answer these questions it is important to have an end goal in mind. Ask yourself what the company is trying to achieve at the end of the day with the help of this metric. For example, if the company is using the number of clicks on a webpage and not considering the end goal of signups then this will not give them a clear picture of the campaign’s success. One of the pages has a 60% click rate but zero signups while the other has only a 20% click rate but a 90% signup rate then, in this case, the latter would be considered more successful. So, answering questions about marketing metrics please keep in mind the end goal.

Q: What is the best thing I can do while in college to land a job in data science after graduating?

The Best thing to do is gain experience, and one of the best ways to gain experience is from community projects. Look for charitable organizations or community organizations that might not have a big budget to hire someone but are willing to have volunteers lead them in the right direction.

For example, an environmental organization looking to collect donations. They have data about different potential cities to set up donation drives. You could conduct population & demographic analysis to find out about the best cities for setting up the donation drives.

Q: There is a lot of competition for entry-level data science jobs. How do you stand out?

Yes, it is challenging especially if you’re talking about the Indian sub-continent. If we talk about the US, then the scenario is different. The number of jobs is abundant compared to the supply of talent, but there’s also another challenge of having the right skill set and experience. Companies are looking to hire individuals with particular skill sets. So it is important to keep improving your skills and gain experience to be able to compete. Having skills other than that of data science can also help to differentiate you from the competition. Try to learn about other business functions to create a more holistic profile.

Q: What are the things that data scientists should keep in mind when searching for his/her first job?

Sometimes it’s better to go for smaller companies as they could provide you more valuable experience. You could really make an impact working for a smaller company as only a few people are running the data science projects. While most of the competition is looking to get into tech giants it might be a good idea to start your career with a smaller company where competition is less, and more opportunities are available to learn and grow.

Q: Do I need Master’s/Ph.D. or an advanced degree to get into data science?

It’s not necessary to get advanced degrees to start your career in data science. Although it’s very important to have a good foundation which you can get from your bachelor’s or some other degree as is the case with most technical fields. But getting advanced degrees does not always guarantee you the best job. It’s equally important to gain experience with community projects, internships, or trainee opportunities. Having a Ph.D. means you have become an excellent researcher and are experienced in working on very difficult problems. This sometimes means opportunities available for the advanced degree holders may be somewhat limited.

Q: Where can you practice machine learning?

Going to hackathons is a good way to practice your machine learning skills in a comfortable setting. It’s also a good environment for guidance and feedback to improve your machine learning skills. You can also start practicing on Kaggle.

Q: What kind of portfolio is required to get into an entry-level Data Science job?

Working on your foundation is very important for entry-level data science jobs. Having a good foundation in mathematics and statistics is required. Being able to understand the metrics and business problems is also required for most data science roles. Understanding linear algebra, conditional probability, Bayes theorem, and central tendencies are necessary. Having a strong foundation helps you with the tools of data science and making analysis. Your portfolio should showcase an understanding of the core concepts and familiarity with some of the commonly used tools.

Q: How to transition from one career to another? For example, from cloud computing development environment to data science or from marketing and automation to data Science or from software engineering to data science.

There are always some transferable skills. If we talk about digital marketing, there is a lot of analytics in this field and requires data science.

If you’re looking at the big production systems, there are many components of software engineering involved. So being skilled in software engineering and data science would be a great advantage. For cloud computing, you can deploy your models if the company is big enough for the heavy-duty infrastructure. You need to find a role where your skills are transferable.

Also, if you are already working somewhere your current organization would be the best place to make the transition into another function. After that, you can definitely look for a company where your preferred role is available and where data science is encouraged.

Q: What’s the interviewer’s approach when hiring fresh data scientists?

Conceptual clarity is very important even if you don’t have years of experience in different data science domains. Make sure whatever you mention in your resume you should be very clear about the concept behind it. The Interviewer will also evaluate your understanding of basic concepts which includes Mathematics, Statistics, and Machine learning. This will give the company sense of how much effort is required to train the candidate.

Q: How do I tell a story about myself and my projects to stand out?

It is very important to provide the interviewer an opportunity to look at the work you have done. For that purpose, you can use the GitHub repository to make your analytics available. Including links to your repository on your resume is a good idea too. Even better is to build a portfolio on WordPress to get noticed.

Portfolio websites are becoming more common nowadays. If you look at the companies hiring pages, they do ask for a LinkedIn profile, GitHub repository, and your website. So, this is a great opportunity to showcase your work efficiently. Your portfolio should not be limited to your code and output only, but should also include some writing sample that describes your output. It’s always a good idea to showcase your communication skills. Most of the time, the hiring person is evaluating if you’re able to clearly communicate your analysis and findings so communication becomes an essential skill.

If you put your work online it becomes easier for the hiring team to research you. So at the time of the interview, they have a better idea of your abilities which could make a big difference.

The webinar was a perfect combination of practical information and guidelines to kick start your career in data science. A great deal of the discussion applies to candidates applying for a role outside of the data science domain.

It’s important for candidates to have a conceptual understanding of the field and demonstrate an interest and understanding of the company they are applying for. To start your career in data science, your first step is to have a strong foundation of the core subjects. The next step is to build your portfolio. Make sure to always be working on your experience. Volunteering for a community project is a great way to practice your skills. Having strong technical skills along with interpersonal and communication skills will help you stand out from the crowd in this highly competitive job market.  Don’t forget about applying for smaller companies. Your role will be more involved, and the lessons you learn from mistakes and successes will be more profound.

Thanks for reading! I hope this has given you a good understanding of data science career options and how to best prepare for an interview. Here is another awesome blog on 101 Data Science Interview Questions to help you get fully prepared for the interview.

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