5 Beginner-Friendly Tips to Learn Data Science

A white, female data science student with glasses looks into the blue light of a computer

Interested in learning data science, but not sure where to start? The good news is that nearly every data scientist started exactly where you are. Many data scientists and analysts begin learning on their own, deciding if they will pursue formal education after they learn data science basics. From YouTube® to online courses and beyond, there are a ton of resources to turn to as you start your journey into the field.

For many, the data science journey is worthwhile. Employment of data scientists is projected to grow 36 percent from 2021 to 2031, according to the U.S. Bureau of Labor Statistics (BLS). An average of 13,500 job openings for data scientists are projected each year over the decade, and the median annual wage for data scientists was $100,910 in May 2021.1

This 5-step guide can help you get your footing as you start exploring one of the fastest-growing fields in today’s job market. Let’s get started.

1. Build an understanding of what data science is

While this certainly won’t happen overnight, the first step to learning data science is beginning to learn what it is—and what a data scientist actually does. There are a lot of misconceptions about the field, but one of the simplest explanations is this one from the BLS: “Data scientists use analytical tools and techniques to extract meaningful insights from data.”

If that sounds pretty broad to you, well, you’ve got the right picture. Data science is a vast ocean of possibilities and your career could go in hundreds of different directions.

Start doing some digging to understand the differences between data science and data analytics, and ensure that your interests lie in data science specifically. Despite being closely related to science, mathematics and other STEM-related fields, data science is an open-ended, creative career path that requires you to consistently ask questions—and theorize ways to answer them.

2. Start doing your own research

How do people learn data science? How do you dip your toe into this exciting world? Before focusing too heavily on building technical knowledge and coding skills, continue to build your awareness around the state of the industry, major events and trends in the field and where top experts believe it’s headed.

When you notice terms or trends that catch your interest, dig a little deeper and research those things more specifically. Since this field is so huge, it can be helpful to narrow your focus (especially at first) to get a practical look at how data science works. The best way to do this is through data science resources.

Reading and regularly checking in on content across a few data science blogs can be a great way to get up to speed and keep a pulse on the fast-paced industry. Reddit’s Data Science Community can also be a helpful place to begin as a resource to find general industry insights or specific answers about certain programming languages.

Whether you're interested in data analysis, machine learning, deep learning or any other specialty from data visualization to natural language processing, getting insight from industry experts and doing some research on basic concepts is a great place to start. It can help you solve problems, learn new skills and connect with like-minded people—all of which are important elements of a strong foundation.

3. Leverage resources to become self-taught

Before pursuing formal education, a ton of data scientists and coders start to learn data science online. Many start learning data science on YouTube, which is one of the most accessible resources for people without prior experience.

freeCodeCamp® is a popular YouTube channel option, with more than 7.8M subscribers and 1,000 videos. Something like this is a good option for getting free access to expert advice—especially if you don't have much prior knowledge. Watching someone explain a concept or demonstrate a process on video can really solidify learning.

GitHub® is another resource that’s home to a community of more than 100 million software developers and 4 million organizations. The platform offers a place for developers to build, scale and deliver secure software. Those who want to learn data science can experiment with SQL (structured query language) databases, connecting them to the site.

Since this platform offers programming and developing resources, you can also try your hand at many of the other tools data scientists find useful.

While diving into resources this expansive can be overwhelming, it’s also reassuring to see first-hand how far these developers and data scientists have gotten using these tools. The best thing about starting to learn data science is that you don’t have to do it alone; collaboration and knowledge-sharing is a major value of the community.

4. Explore preliminary education

It’s true that many data scientists get their start by being self-taught, but many also turn to more formal education as they begin to take the idea of a data science career more seriously.

Programs in data science can clarify the tools and roles of data science in the world. Plus, having instructors to guide you through this ocean of new information can save you a lot of time and headache.

Employed data scientists do often have a master’s-level degree, but that requires obtaining a bachelor’s degree first. Consider a Data Analytics Bachelor's Degree and work through your projects as you take data science courses that interest you.

Both of the above degrees may be finished in as few as 18 months, and they offer hands-on experience with real-world tools and in-demand technologies.2 With the Data Analytics Bachelor's Degree in particular, you’ll become proficient in coding languages such as SQL, R and Python®—giving you a head start if you choose to pursue a master’s degree later on.

5. Consider higher education

To secure an entry-level position, data scientists typically need at least a bachelor’s degree in mathematics, statistics, computer science, or a related field. That said, some employers may prefer or require that applicants have a master’s or doctoral degree.1

To expand your career opportunities during your data science job search, you may want to explore potential higher education pathways. Many aspiring data scientists choose to pursue a master's to gain the skills required for a more advanced role.

In such a fast-paced, fast-growing field, continued learning is essential to being a successful data scientist. Regardless of education level, many data scientists and data analysts continue to take courses throughout the duration of their careers to keep their skills up to date. Various boot camps, certification programs and licenses are offered for professionals to learn new programming languages and keep their data science skills current.

Though it can feel like data science education is an endless journey, remember that it all starts with taking the first step. Continue to do your research and build your foundation, and refer back to these beginner-friendly tips whenever you could use some direction.

Get your start in data science

These tips to help you learn data science may have clarified a few things about what data scientists do, but as you probably know, these careers go as wide as they go deep.

Get a more thorough look at what a career as a data scientist could look like, and check out “An In-Depth Analysis of the Data Scientist Job Description.”

Python is a registered trademark of the Python Software Foundation.

Youtube is a registered trademark of GOOGLE LLC.

freecodecamp is a registered trademark of freeCodeCamp.org.

GitHub is a registered trademark of GitHub, Inc.

1Bureau of Labor Statistics, U.S. Department of Labor, Occupational Outlook Handbook, [accessed June 15, 2023] www.bls.gov/ooh Information represents national, averaged data for the occupations listed and includes workers at all levels of education and experience. This data does not represent starting salaries, and employment conditions in your area may vary.

2Completion time is dependent on the number of transfer credits accepted and the number of courses completed each term.

About the author

Hope Rothenberg

Hope Rothenberg is a creative copywriter with agency, in-house, and freelance experience. She's written about everything from area rugs to artificial intelligence, and a ton in between.

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