7 Steps to Data Science Success

by Sheila Mulholland
September 19, 2023
7 Steps to Data Science Success

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: 

  1. Research 
  2. Educate 
  3. Experience 
  4. Network 
  5. Contribute 
  6. Maintain 
  7. Look Forward  


Step 1: Do Your Research! 

You wouldn’t set off on a fabulous vacation without having Googled your destination at least once. Don’t start down the data science career path with any less preparation – make sure you understand ahead of time what data science is, as well as what it isn’t, so you can formulate a solid plan. There are also sub-specializations that you’ll want to get clear about early on, in case you decide you’d like to explore them in even more depth later in your career. As the team at AWS puts it, “Data science is an umbrella term for all aspects of data processing—from the collection to modeling to insights. On the other hand, data analytics is mainly concerned with statistics, mathematics, and statistical analysis. It focuses on only data analysis, while data science is related to the bigger picture around organizational data.”

Step 2: Educate Yourself 

No, we’re not being redundant – data science is an intense field requiring some degree of intellectual rigor as well as a deep-set curiosity and logical approach to exploring the world and problem-solving, so there’s a lot of learning involved throughout the process. While, as the United States Bureau of Labor Statistics confirms, there are no “formal” prerequisites for a data science career, it is often highly beneficial if you have, or are able to attain, a degree in a STEM discipline. Some of the most common STEM degrees tend to be in statistics, mathematics, and computer science – all of which will work well for a budding data scientist.

According to some studies, around 80% of all data science professionals have at least one graduate degree, with a little under half of that number going on to attain a PhD. However, if the thought of heading back to school doesn’t exactly thrill you, or if you’re transitioning from another career into data science, you might consider alternative educational options, like certifications, online coursework from providers like LinkedIn Learning or Coursera, or data science boot camps. What’s most important here is to introduce you to thinking and solving problems like a data scientist, and to strengthen your critical thinking and analytical capabilities; other skills you’ll want to build include programming ability (typically, data science relies on Python and R), statistical analysis, database management, data operations (e.g. cleaning and organization), and data visualization.

[RELATED: Beyond Coding: The Soft Skills That Employers Look for in IT Job Candidates.]

Step 3: Experience  

Once you’ve got all of this excellent, if theoretical, knowledge, the next logical step is to put it to the test with some real-world, practical experience. This step can sound pretty scary, especially if you’re not yet confident of your skill set and what you know – but it does not necessarily have to be formal, on-the-job experience. Just make sure you can get your hands on some (freely available) data and start applying the best practices and concepts you’ve learned. The goal is to create solid habits, build your understanding of standard data science tools, and yield results that you can showcase when calling on prospective employers. 

Each organization deals with data in a specific way, so you may end up using any combination of data science tools in your future roles – including some of the ones listed below. Make sure that you set aside time to explore: 

  • Apache Spark 
  • Tableau 
  • Matlab 

Step 4: Network, network, network!

That’s right – contrary to popular belief the Zoom happy hour did not kill off networking as a crucial business practice, so it’s vital that you cultivate a solid network throughout your career in data science. To foster authentic connections with your fellow data scientists, first, determine where you’re most comfortable meeting new people and conversing about the field. If you’re a “face-to-face” type of person, most comfortable engaging with others when you’re physically proximate, be sure you’re regularly heading out to industry events and conferences so you have a steady stream of new people to meet. If you’re more comfortable operating online, that space is also rich with possibility; 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’ve probably heard that it’s important for developers to contribute to open-source projects and the wider software developer community. Well, it’s equally important for you, as a data scientist in the making, to forge your own path and make your own contributions within your field. As renowned data science consultant and author Adam Ross Nelson identifies, “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.” This is that practice.  

If you feel stumped as far as where and what you can contribute, don’t be afraid to jump in and create your own community and opportunities! Ruhma Khawaja, writing for Data Science Dojo, elaborates that you can even “…[create] unique events forums where there will be keynotes, panel discussions, open salons, and breakouts to discuss current and future industry trends”. Don’t wait for the crowd; make them come to you! 


[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 expands at a pace matching that of modern technology, so you’ll want to prioritize keeping yourself up to speed with relevant developments and breakthroughs in the space. You cannot rest on your laurels! Make time to add new concepts to your body of knowledge every day; it is easier to stick with an existing habit than to force yourself into an entirely new paradigm. You may opt to document this growth via a blog or personal website, which can be showcased for future employers to see that you’re dedicated to expanding your understanding of data science. “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,” explains Shelly D. Farnham, Ph.D., Executive Director and Research Scientist, Third Place Technologies. 


Step 7: Shape Your Future in Data Science  

As you now know, data science is always evolving, and you can expect that a career in the data science space will follow suit. This presents an exciting opportunity for you to guide your career trajectory and align your skill set to complement current and future technological innovations, ensuring that you’ll always make a contribution to progress. At the moment, data scientists are often mission-critical when it comes to some of the most exciting and newest developments in tech — just a few of which are listed below: 

  • 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 an exciting job working in the data science space, you’re ready to start building the career you’ve been dreaming of. After you’ve learned key data science concepts, strengthened your analytical skills, and cultivated a thriving professional network of fellow data scientists, you’ll look back one day and wonder how all of it ever seemed so daunting. The difference between that day and today starts with you. 


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