The Data Science Skills Gap: What Most Online Courses Don’t Cover

“Data scientist” topped LinkedIn’s list of the Most Promising Jobs of 2019 thanks to generous salaries and a high number of job openings. So it’s no surprise that a growing number of people are taking an interest in the field and wondering what they need to learn to get started in it.

Those hoping to build a career in data science will find more online resources than ever before. Online learning platforms like Udacity, Udemy, Coursera, Pluralsight, and many more offer varying courses that teach the tools and concepts needed to get your feet wet in the data science field. But with so many options, it’s hard to know which courses are teaching you what you really need to know on the job.

The shortfalls of online learning platforms

When it comes to data science, most of these online courses cover at least some of the essential topics: teaching you programming languages like Python, familiarizing you with the general concepts and vocabulary around data science, and introducing you to the tools you need to build neural networks, like Tensorflow and Keras.

Unfortunately, just having a firm grasp of the tools won’t make you an effective data scientist. Many aspiring data scientists (and the people who hire them) discover a gap between what they’ve been taught and what they need to know once they’re on the job.

Real-world data science skills

Here are some examples of the on-the-job skills needed in data science—ones that are often glossed over by traditional online learning platforms:

  • Knowing the right questions to ask: So you’ve discovered the data and mastered the tools to leverage it. Both are important steps, but the actual data science doesn’t happen until you start asking and answering questions from that data. And the real value comes when you start asking the right questions. The best questions are precisely formulated and lead to airtight answers on which actions can be based. Effective data scientists learn how to use the results from one question to refine their next approach in the question-test-analyze loop.
  • Business savvy: Having a solid understanding of your field of business will make a valuable difference in how you apply your data science skills on the job. That includes understanding the business structure, its needs, and its revenue drivers. This knowledge will help you leverage data in a smart way and make the right connections. The firmer your understanding of the business, the more you can rely on your intuition as you mine for lucrative insights and opportunities.
  • Picking the right model: In most data science classrooms, you will learn about the different machine learning models at your disposal and their respective drawbacks. What might get glossed over, however, is how you decide which one to use, given that none will be a perfect fit for every scenario (and you likely won’t have the time or resources to try them all). Model selection is a skill that involves matching a model to your input variables and the questions being asked to produce the kind of output metrics that will be most useful.
  • Communication: The results you find are worthless if you can’t communicate them in an effective way and demonstrate their application and value to the business stakeholders. Often the term “storytelling” comes up in data science, and for good reason. When it comes to data science, communication is all about distilling mathematical results and translating them into easy-to-understand, actionable insights. And that doesn’t just mean being a good speaker or writer—the ability to visualize data through charts and graphs can be invaluable to helping your audience understand your findings.
  • Real-world application: Mastering the language and tools of data science without hands-on practice is like learning a foreign language from a book without ever having a conversation in it. The best learning comes from practicing with real data sets, like those from the UCI Machine Learning Repository.

Closing the skills gap

Code Pilot has reimagined the online learning process and built a model to help address these gaps. The applied data science discipline focuses on the process as much as the tools, with special emphasis on asking the right questions, choosing and evaluating a model, and deploying and versioning models in production.

Code Pilot also focuses on a specific toolset, without redundancies, so developers can move forward with hands-on learning more quickly. Real machine-learning problems use both structured and unstructured data sets. And lessons in data visualization and insight ensure that developers can clearly communicate the value of their findings and deepen their practical abilities.

Learn more about Code Pilot’s four-week applied data science discipline, including a free preview.