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Data Science: Standing Out After College

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Often candidates for entry level data science positions find that they need to differentiate themselves is by having domain knowledge. Very rarely are employers looking to employ an entry level data science graduate for solving a general or a company wide problem. Most of the times employers are looking to solve a reasonably narrow set of issues or are looking for experience in a particular domain. While domain knowledge is not a substitute for experience, it can help a candidate gain extra skills or working with domain specific software tools. Understanding the nuances of the field and the requirements differentiates a candidate from other candidates with generalized data science knowledge and skill sets. The reason for is that companies hire data analysts or data scientist with prior experience with their “particular problem” and rarely hire all round generalist data science candidates.

You can get experience in data science by choosing to solve problems in a particular domain while you are in college. Doing so helps you both (1) get practical working knowledge of data science and (2) learning to apply data science in a particular field/domain. Each field has its own requirements and the kind of problems that they are looking to solve. Many domains also use specific software tools and packages which often come in handy when looking for jobs, interviewing and then performing the job.

What are the different domains that are currently popular?

While data science seems to be fairly ubiquitous, in reality only few fields really use or hire data analysts or data scientists. Some of the popular fields is data science in tech companies which is mostly about having a deep knowledge and experience in computer science, healthcare, pharma, biology, finance and economics. There is also some data science that is going on in public policy and government related projects.

Every student in data science should look towards gaining domain specific knowledge in addition to their bachelors in data science. Some of the possible choices for a minor or work experience are: economics, business management, business logistics, operations research, medicine related majors, health management, public health, political science, public policy, computer security, sciences, etc. How to get domain knowledge or experience?

The domain experience can be gained through either (1) a major or minor in one of the above fields and/or (2)internships and research opportunities in the above fields. Most data science and computer science undergraduate programs have a capstone project where you can focus on a field/domain to gain experience.

Another option is to look for more of data analyst, research analyst positions in a company before transitioning to a data scientist role within the same domain. It is easier to use the domain knowledge and the work experience to transition in a domain as compared to changing fields and job titles. Once you have gained more experience and knowledge, then you can look into transitioning into a company that solves data science related problems in a different domain.

There is also the option of doing a masters in data science and focusing on more of machine learning and advanced programming skills in a particular domain before applying for jobs. Capstone Projects and Cracking The Interview:

Domain expertise can be the differentiating factor during both the resume filtering and interviews. While all interviewees have to pass the technical round consisting of coding and data science questions, that does not guarantee a job. Many interviewees are often asked questions beyond those faced in a classroom setting or learned during a course work. Answering these questions are best done through discussions of past data science projects, solutions methodology, the challenges and nuances of the project.

Most undergraduate data science curricula end with a Capstone Project. The Capstone Project is a great way to design a data science project in a particular domain and showcase your critical thinking, coding and presentation to a technical and non technical audience skills. The beauty of a Capstone Project is that the focus is really about you and your work and not on the domain itself. You are not constrained to find a job in that specific domain only. E.g. if you do a data science project in finance, that does not imply that you will have to find employment in the financial sector. You can translate your experience to find a data science job in engineering or health related fields. Your Capstone Project is what you are going to use to answer the question” tell me about any of your past data science projects”. Some domain do need a very specific set of tools or packages or skillsets. So working in those domain during your Capstone Project gives you experience with those required tools. If you find the domain very interesting, then you could look at the possibility of doing a minor in that domain and really expanding your knowledge base in addition to gaining the data science skills.

Many students sharpen their coding and data science skills by doing challenges found on Kaggle and Leetcode. In addition to your Capstone Project, you can also look into Coursera and EdX, who offer Capstone Projects (for free) for further practice or for looking into a different domain.

Data Science Bootcamps

If you are data science, statistics, math, computer science major, engineering graduate, or have graduated in any other quantitative major, then you probably do not need to attend a data science bootcamp. Data science boot camps can be fairly expensive. If you have graduated in a non quantitative major and have not had much exposure to statistical analysis and/or coding, and would like to be a data scientist, then you may wish to look at a data science bootcamp or do a masters in data science. If you are still an undergraduate in college and would like to become a data scientist, then we recommend enrolling into quantitative and coding classes at your college to gain the necessary skillset.

Additional Skills Required to Succeed in a Data Science Job

A data scientist’s job does not stop at analyzing the data and presenting the results. A data scientist often needs to communicate the results of their project to an audience with varying levels of technical experience. Sometimes data scientists may be called upon to make business cases based on their results. Communication and teamwork are important to succeed in a data science career (or any other career). As an aspiring data scientist, doing group projects and presentations will help you as much as the Capstone Project in rounding out your resume and skillset.

Another recommendation for students is take a couple of courses in humanities, business and/or marketing in addition to their technical courses.

Data science is a much hyped career and has a lot of exciting opportunities in the future years. Almost every company will require smart analysis of their data and make business decisions guided by the analysis of the data. To partake in the data science career track, you will need the necessary technical knowledge and skills, a beginner’s mind as it is a fast change career and learning will be continuous and the willingness to expand your skills across different areas.