How to Become a Data Scientist with No Experience

In 2019, the U.S. Bureau of Labor Statistics (BLS) believed 30,810 people were working in “Data Scientists and Mathematical Science Occupations” in the U.S.¹ By 2020, the category had almost doubled, ballooning to an estimated 59,680.² And by 2021, the BLS created a stand-alone occupation category for Data Scientists, estimating 105,980 people were working in jobs with that title.³

That’s a snapshot of a career in high-growth mode with little sign of slowing down. The latest BLS estimates suggest 245,900 data scientists are working in the U.S.—and the agency predicts 34% growth in jobs in that field over the next decade.⁴ Compare that to the average predicted growth across all professions for that period, which is 3%.

Less than a decade ago, data science was an obscure career, treated largely as a statistical afterthought by the BLS. Today, it’s one of the fastest-growing professions in the U.S., with abundant opportunities and above-average compensation (the BLS puts the median salary for data scientists at $112,590—more than double the national median for all workers, $49,500⁴).

And as is often the case when a profession is relatively new and in the midst of massive growth, a lack of experience is not necessarily a barrier to entry.

This article looks more closely at how to become a data scientist and secure that first job without experience.

What Does a Data Scientist Do?

As a data scientist, you’ll typically be involved in gathering large amounts of data, organizing it, analyzing it, and presenting actionable conclusions based on that analysis.⁵

A data scientist might track inventory in a supermarket to develop a predictive algorithm to determine when particular items need to be restocked or reordered. Or you might analyze election data to find patterns that might anticipate future voting behavior or explain a particular election result.

Broadly, data science sits at the intersection of computer science and business, using the latest programming and modeling tools to extract practical insights from raw data.

Essential data science skills include specific IT knowledge—such as programming and data engineering—and analytic and communication skills, such as statistical modeling and data visualization.⁶

Steps to Become a Data Scientist

How hard is it to become a data scientist?

You can get a job in data science without prior experience, but that isn’t the same as lacking training or understanding of what data scientists do and how they do it.

To become a data scientist, you need to show an employer you have the necessary knowledge and enthusiasm for the job. That can be achieved by acquiring relevant skills and demonstrating core competencies, which can be broken down into seven essential steps for becoming a data scientist.

Step 1: Build Foundational Skills

Every data scientist needs an understanding of the most popular programming languages: Python, R, and SQL. You’ll also need a solid grounding in statistics and probability, the twin foundations of many modeling and predictive techniques.

Finally, you’ll need an understanding of data analysis and visualization, as well as basic database management skills, such as querying.

Step 2: Learn Machine Learning Fundamentals

As a data scientist, you’ll need to know how AI and machine learning works,⁷ which requires engaging with concepts like supervised vs. unsupervised learning (essentially, the difference between knowing the answer the machine is expected to produce and not), model evaluation and tuning (how well a model is doing and how to improve its performance), and the use of popular machine learning libraries, such as TensorFlow, PyTorch, and scikit-learn.

Step 3: Enroll in a Data Science Program

Structured learning can be a great way to accelerate skill acquisition and ensure new knowledge focuses on practical application and outcomes. Your ideal option will depend on your circumstances. Perhaps you need a few online courses to get acquainted with specific programs or skills. Maybe you’d benefit from a bachelor’s degree for a thorough grounding in fundamentals. Or it could be that a master’s degree will best complement your previous education and experience.

Step 4: Build a Portfolio

Data scientists can build a portfolio to showcase their abilities and demonstrate how they analyze and interpret data.

Creating projects from real datasets shows employers how you analyze actual problems, and a well-developed project is a perfect example of your grasp of end-to-end workflows. Host your projects on GitHub or a personal website, and develop material that shows your future employer your data science proficiency.

Step 5: Gain Practical Experience

Even before you get your first job, you can get valuable data science experience. Internships and entry-level business analyst roles can give you some exposure to data science. Keep an eye out for freelance opportunities or volunteer for data projects at your current job. Competitions on Kaggle (a popular data science platform) can be a good way to get experience and feedback.

Step 6: Learn Data Tools and Technologies

Get whatever exposure you can to big data tools and technologies, including:

• Cloud platforms such as AWS, Azure, and GCP
• Spark and Hadoop, the major big data frameworks⁸
• Business Intelligence and visualization tools such as Tableau and Power BI

Step 7: Network and Connect with the Data Community

Find connections, industry groups, and thought leaders on LinkedIn, and look out for data science meetups and conferences to attend. Open-source communities are an excellent source of pooled knowledge and enthusiasm, as well as often providing opportunities to collaborate on projects you can then add to your portfolio.

Level Up For a Career in Data Science

With a compelling portfolio, relevant internships and volunteer work, active networking within the data science community, and a thorough understanding of the profession’s foundational skills and tools, you can transition into data science as a high-caliber candidate who will stand out to employers.

The Online Data Science, M.S. from New York Institute of Technology is designed to drive your career forward with focused, relevant training in programming, statistics, and machine learning–and a showcase independent research project to apply your skills to a significant technical area in the field. You can complete the 100% online, 30-credit course in as little as 10 months, emerging as a graduate in a proven high-demand field with an advanced degree respected by employers.

For more information, explore the course curriculum and admissions requirements online. To discuss how New York Tech can advance your career in data science, schedule a call with an admissions outreach advisor today.