How to Land Your First Data Science Job: From Student to Data Scientist

According to one study, approximately 55% of data science job offers in 2022 were for entry-level positions. If you’ve been trying, without success, to land your first data science job, it can be frustrating. How have others managed to break into the industry when you haven’t?

Data science has been attracting more students and even established professionals with the promise of high-paying jobs and cutting-edge projects. It now takes more than talent or a bachelor’s degree to stand out from the rest.

In this article, we dive into the journey of an early-career data scientist and how you can go from student to associate data scientist. We highlight key areas to study, the importance of a strong portfolio and what goes into it, how to build an online presence as a data scientist, and how to prepare for the interview that will land you that first job.

Learning the fundamentals of this profession is the first step to moving from student to associate data scientist. However, this first step does not look the same for everyone.

Pursuing a Bachelor’s or Master’s Degree in Data Science

Earning an undergraduate or a graduate degree is a big advantage in this profession. An overwhelming majority of data scientists have a graduate degree, with a smaller percentage having a bachelor’s degree. The number of data scientists without a degree makes up a very small percentage, although this may change as more employers start to prioritize skills.

In any case, at least one study shows that as many as 74% of data science job listings currently require a degree of some kind. Therefore, choosing to learn the fundamentals by signing up for a bachelor’s or master’s degree is a good option to consider. Other advantages include:

• Structure: University curricula are professionally developed and structured. They include scheduled lectures, exams, grading, etc. This is a good option for learners who don’t want to figure out what to study.
• Comprehensive: University programs cover as much as possible to ensure students have a deep understanding of the subject matter. This is important for fundamentals like math and statistics.
• Networks: Networking is essential to transitioning from a student to an associate data scientist. University is a great opportunity to build networks with fellow students and alumni who could help you secure your first data science job.
• Soft Skills: University courses help students develop soft skills such as communication, collaboration, problem-solving, and critical thinking.
• Versatility: A university degree makes you more versatile and a viable candidate for other job opportunities that could be an indirect path to a data science career.

However, pursuing a formal degree will come with challenges, such as:

• Cost: University courses are expensive; you may need over $100,000 for a four-year undergraduate degree.
• Time: Bachelor’s degrees usually take 4 years to complete, while a master’s degree often requires 2 years.
• Theory-Forward: University courses lean towards theory rather than practical applications. Therefore, students may lack important practical skills when graduating.

An Alternative Path to Learning Data Science

If pursuing a degree is out of the question or if your degree is in another field, you can still gain the skills and knowledge to become an associate data scientist informally. This self-driven approach is typically focused on employability, which has pros and cons.

If you want some structure to your learning, you can sign up for online courses or bootcamps. You can also acquire these core skills independently by reading books, following YouTube tutorials, and working on projects. The advantages of this approach include:

• Focus: This approach allows you to focus on key areas of data science and even the specific technologies you want to use in your professional work.
• Self-Paced: Online courses typically allow you to finish at your own pace. Bootcamps are less flexible but last for weeks, not years, so you can schedule them for a convenient period.
• Affordable: You can pay $20 for an online course or enroll in a $15,000 bootcamp. Either option will likely be cheaper than a university degree.
• Employability: This approach allows you to focus on skills and qualifications that will get you employed. These are typically covered in bootcamps.

The downsides of this approach include:

• Too many options: There is an overwhelming number of courses and bootcamps, and some are less polished than others.
• Requires self-discipline: Without the structure offered by traditional universities, it takes a lot of discipline to take this route and get your skills to the required level.
• Not comprehensive: Both online courses and bootcamps skimp on the course content, which can result in a poor understanding of certain fundamentals. This can limit your ability to handle some of the advanced responsibilities of associate data scientists.

Data Science Fundamentals to Cover

Whether you are pursuing a degree or an online course, you’ll need a solid foundation in key areas in data science. These include:

• Programming: A junior data scientist needs coding skills for automation, building ML models, handling data, etc. You’ll need to master either Python or R. There are many online resources for learning Python, including Interview Query’s Python learning path.
• Data and Databases: A data scientist must work with data, so you must get comfortable working with databases and SQL. You should also learn data collection techniques such as using APIs and performing web scraping. You can refresh your SQL knowledge using our SQL learning path.
• Data Analysis and Visualization: Data scientists produce insights and present them to stakeholders, so you’ll need to know how to analyze data and create visualizations to present your insights.
• Machine Learning (ML): ML models can solve more complex problems than statistical models. A data scientist is expected to know how to use ML tools such as scikit-learn, TensorFlow, and PyTorch. Check out our modeling and ML learning path to learn more.
• Math and Statistics: A good understanding of math and statistics is vital in a data science career. Without these, you may choose poor models or make inaccurate conclusions. Find out more about this on our statistics and A/B learning path.
• Problem-Solving: Data scientists must be able to turn abstract problems in the real world into questions that can be answered using data science tools and techniques.

With these fundamentals in place, the next step to getting an entry-level data science job is to practice working on projects and build a portfolio.

A project portfolio is necessary for data science because it is the main proof of your skills. You’ll work on many test projects while learning data science, and some of these can be repurposed and packaged for your portfolio. However, it’s crucial to pick the right projects to showcase.

Choose a Domain

A portfolio focused on a single domain makes you a better candidate when applying for jobs. This focus shows recruiters and companies your skills and deep interest in that subject matter. Try to find a domain that interests you early so you can start specializing.

Prioritize Less-Obvious Projects

Certain projects have been in the data science community for years. Although they are useful for practicing and gauging your skills, they won’t help your portfolio stand out. There are many new datasets and original problems you can tackle on platforms like Kaggle.

Go Beyond GitHub

Having a GitHub is important, but some aspects of your portfolio will be easier to highlight on platforms like datascienceportfol.io. Additionally, having your portfolio on different platforms with different strengths will help you build a stronger online presence. This article highlights a few more places you can showcase your data science projects.

A strong online presence makes your work and profile easier to find. It also helps you build a network that can lead to a job referral. A few ways to build a good online presence include the following:

• Create a personal website where you discuss your background and interests at length. You can also publish tutorials and articles about interesting projects you’re working on.
• Create a LinkedIn profile and keep it up to date with details about your education, experience, and skills. You can also share your perspectives on issues, which is a great way to show your soft skills.
• Contribute to open-source projects, answer questions on Stack Exchange, and participate in online data science discussions.

Although you can land your first data science job through a recruiter, you’ll likely have to apply, like others. This part of the process is often the most challenging, even for talented students, but it doesn’t have to be.

Start With a Strong Resume

Resume screening is the first step in recruitment, so it’s crucial to have a strong resume that speaks to your qualifications in your absence. If you are not confident in your resume writing skills, consider hiring a professional to help you craft a better one. You should also make strategic edits when applying for certain positions so your application aligns better with the role.

Work on Your Interview Prep

Although you might be able to wing it, most successful candidates will tell you that good preparation is the key to acing an interview. For a data science job, expect to go through several interviews in which you’ll be tested on your:

• Technical and coding skills
• Interpersonal skills
• Culture fit
• Problem-solving, etc.

Answering practice questions is an excellent way to familiarize yourself with the types of questions asked during interviews. Participating in mock interviews can also help you assess and improve your responses to common interview questions. Some companies have unique interview processes, so it’s essential to research this to avoid being blindsided.

Many online platforms prepare you for the technical interview questions, but Interview Query takes a more rounded approach. We help data scientists prepare for all aspects of interviews, from the technical and behavioral questions to setting salary expectations.

Identify Opportunities to Get Experience

Any kind of work experience is valuable in an early-career job application. Additionally, your path to the associate data scientist position may start in a different position in the same company.

Internships and unpaid work are opportunities to apply your data science skills early in your career. This will drastically improve your hard and soft skills and add weight to your resume. These are also opportunities to get your foot through the door, network with professionals, and stake your claim as a viable option when an opportunity opens up in the company.

Demand for data scientists is high, but so is the number of people joining the field. You’ll need a strategic approach that starts with acquiring the right skills and knowledge to stand out from other applicants. Develop a robust portfolio and online presence to showcase your expertise. Last but not least, you need a strong resume, interview skills, and an openness to taking unpaid positions that can lead to an associate data scientist position.

Interview Query is committed to helping you get the job that will kick-start your career in data science, and we have many resources to help you at different stages of the journey. Our data science learning path will refresh your knowledge of the fundamentals, and our question bank contains data science interview questions that cover everything from SQL and Python to behavioral questions. If you have an upcoming interview, our mock interview feature can help you practice answering questions. You can even get coaching if you want industry veterans’ tips on tackling the interview.

The road from student to associate data scientist can be long and challenging. We hope this guide will be the key to getting your big break.