If you dream of a career as a data scientist, but just aren’t sure how to get yourself into the room/s where it happens, this guide is for you. Drawing from our years of experience in and around the world of technology, here are seven steps to turn the data scientist job of your dreams into your reality. Grab your laptop and some cold brew; let’s make it happen!
Quick Navigation:
- Research
- Educate
- Experience
- Network
- Contribute
- Maintain
- Look Forward
Step 1: Do Your Research!
You wouldn’t start a vacation without researching your destination first. The same approach applies to building a data science career. Before choosing this path, it is important to understand what data science is, what it is not, and how you can create a clear career plan.
There are also several sub-specializations within data science that are worth exploring. Understanding these areas early can help you decide which skills and career direction you want to pursue.
As the team at AWS explains, “Data science is an umbrella term for all aspects of data processing—from collection to modeling to insights. Data analytics, on the other hand, focuses mainly on statistics, mathematics, and statistical analysis. While data analytics focuses on analyzing data, data science covers the broader process of using organizational data to generate insights.”
Step 2: Educate Yourself
Data science is a challenging field that requires intellectual curiosity, logical thinking, and a problem-solving mindset. Because of this, continuous learning plays an important role throughout a data scientist’s career.
According to the United States Bureau of Labor Statistics, there are no formal prerequisites for entering a data science career. However, having a degree in a STEM discipline can be highly beneficial. Common backgrounds include statistics, mathematics, and computer science, all of which provide a strong foundation for aspiring data scientists.
Studies suggest that around 80% of data science professionals have at least one graduate degree, with nearly half of them earning a PhD. However, returning to school is not the only option. Those transitioning from another career can explore alternatives such as certifications, online courses from platforms like LinkedIn Learning or Coursera, and data science boot camps.
The most important step is developing the ability to think and solve problems like a data scientist. Building skills in programming (especially Python and R), statistical analysis, database management, data operations such as cleaning and organization, and data visualization can help prepare you for a successful career in the field.
[RELATED: Beyond Coding: The Soft Skills That Employers Look for in IT Job Candidates.
Step 3: Experience
Once you have built a strong foundation of theoretical knowledge, the next step is to apply those skills through real-world practice. This stage may feel challenging, especially if you are still building confidence, but it does not always require formal work experience.
Start by working with publicly available datasets and applying the concepts and techniques you have learned. The goal is to develop practical skills, build familiarity with common data science tools, and create projects that you can showcase to potential employers.
Every organization approaches data differently, so future roles may require experience with a variety of tools. Take time to explore commonly used platforms, including:
- Apache Spark
- Tableau
- Matlab
Step 4: Network, network, network!
Contrary to popular belief, the rise of virtual meetings did not eliminate networking as an important business practice. Building and maintaining a strong professional network remains essential throughout your data science career.
To create meaningful connections with other data professionals, start by identifying the environments where you feel most comfortable meeting people and discussing the field. If you prefer face-to-face interactions, attend industry events and conferences regularly to connect with new professionals.
If you are more comfortable networking online, there are also many opportunities available. As Ravit Jain, data scientist and founder/host of the Ravit Show, explains, “There are so many opportunities because these are the spaces where the real folks who are running businesses – CEOs, CTOs, Founders, and speakers – are watching for folks who could join their teams.”
Step 5: Contribute Whenever You Can
You may have heard that developers should contribute to open-source projects and the broader software community. The same idea applies to aspiring data scientists. Building your own presence and contributing to the data science community can help you grow your skills and expand your professional network.
As data science consultant and author Adam Ross Nelson explains, “The best way to become a data scientist is to be a data scientist. The best way to get better at data science is to practice, practice, practice.” Consistent practice is one of the best ways to develop your abilities.
If you are unsure where to start, consider creating your own opportunities. You can build communities, organize discussions, or host events focused on data science topics. As Ruhma Khawaja from Data Science Dojo suggests, you can create events with keynotes, panel discussions, open conversations, and breakout sessions focused on current and future industry trends. Don’t wait for opportunities to appear—create them yourself.
[RELATED: DataOps Can Bring Certainty to Uncertain Data.]
Step 6: Maintain Your Momentum!
“Data scientists are kind of like the new Renaissance folks, because data science is inherently multidisciplinary.”
— John Foreman, Vice President of Product Management, MailChimp
The field of data science continues to evolve alongside modern technology. To succeed in this space, it is important to stay updated with the latest developments, tools, and breakthroughs. Continuous learning should become a regular part of your career growth.
Make time to build your knowledge every day. Developing a consistent learning habit is often easier than trying to adopt a completely new approach later. You can also document your progress through a blog or personal website, which allows potential employers to see your commitment to expanding your data science skills.
As Shelly D. Farnham, Ph.D., Executive Director and Research Scientist at Third Place Technologies, explains, “Once you have the core concepts, to be able to be really excited about, and continue to seek out, new information is something that I look for, for example, when we are recruiting people.”
Step 7: Shape Your Future in Data Science
As you now know, data science is constantly evolving, and a career in this field will continue to change alongside new technologies. This creates opportunities to shape your career path, develop new skills, and adapt to future innovations.
Today, data scientists play an important role in some of the most advanced areas of technology. Some of the key areas where data science is driving innovation include:
- Artificial intelligence and machine learning
- Deep learning
- The Internet of Things
- Cloud computing
- Quantum computing
Wrapping Up
Now that you understand more about the path to a career in data science, you are ready to start building the future you envision. After learning key data science concepts, developing your analytical skills, and building a strong professional network, you will look back and see how each step helped you grow.
The journey may seem challenging at first, but every new skill and experience brings you closer to your goals. The difference between where you are today and where you want to be starts with taking that first step.
