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With six-figure salaries and incredible media buzz, it’s no wonder more people want to move into data science.
But there’s a persistent trope that you need a master’s degree, or even a PhD, to land a job as a data scientist.
Where does this leave data professionals from industrial companies who want to make the move into data science? If you’re a statistician, analyst, or engineer in finance or energy, do you have to head back to school before you can qualify for such roles?
The short answer is no, but only if you have a specific set of skills. The reason many data scientists today have advanced degrees is that they happened to sit at the intersection of math, computer science, and analytics that allowed them to participate and develop this emerging field.
Employers are more interested in individuals who can do the work and bring the right skill set, so if you can answer yes to these questions, you can find work as a data scientist without going back to school. That said, you may need additional training to get up to speed with emerging technology and data science techniques or methods.
Do you have a strong academic foundation in STEM subjects?
Data science as a job title is a relatively recent invention. In reality, data scientists are individuals with a unique combination of skills in math, computer science, and analysis who can generate unique insights from large quantities of data.
So if you already have a STEM background, like an undergraduate degree in computer science, statistics, or math, it’s more important for you to obtain on-the-job experience, programming knowledge, and familiarity with machine learning techniques.
Another important factor is a mindset shift. Traditionally, professionals in quantitative analysis roles use data to explain phenomena. On the other hand, data science is focused on using data to predict outcomes. Plus, current data analysts making the move to data science will need to really understand the theory behind data science and identify optimal techniques, because there is not a one-size-fits-all approach.
One of the reasons PhDs in physics or math have excelled in transitioning to the field of data science is because of their constant formulation and testing of hypotheses. Properly defining the objective of a data science project and applying the right tools to arrive at an answer are important skills.
Can you identify both the why and the how of a data science project?
Discussions around data science careers tend to focus on the how, when in reality the biggest issue is nailing down the why or what.
As Vasant Dhar explains in his paper Data Science and Prediction, “Given a large trove of data, the computer taunts us by saying, If only you knew what question to ask me, I would give you some very interesting answers based on the data. Such a capability is powerful since we often do not know what question to ask.”
You may know how to navigate all the latest tools, but without a fundamental understanding of how to approach problems, it’ll be challenging to work on projects that relate to the company’s bottom line.
“The most important part of the data science economy is the quality of data that goes into the process of data construction,” says Austin Young, Recruitment Consultant at Hays.
Can you effectively communicate insights to domain experts?
Data scientists must work with data from a variety of domains and then relay that information in a way that non-data scientists can understand.
At present, many graduate data science programs are targeted to individuals with undergraduate STEM degrees. The biggest value proposition: Teaching them how to communicate findings to business leaders and other stakeholders. A data scientist needs to be able to link their insights to business goals and communicate the value of these insights to other stakeholders and decision makers.
If you’re someone coming from a corporate environment, these are skills you likely already have.
Are you comfortable using the tools employed by today’s data scientists?
In addition to their problem solving capabilities and STEM background, data scientists also need familiarity with a range of tools and languages for programming, data storage, data visualization, and more. This includes, but isn’t limited to:
● Programming languages: R and Python
● Distributed computing environments: Hadoop, Spark
● Data visualization and reporting tools: Tableau, Plotly, PowerBI
● Machine learning techniques: regression, classification, reinforcement learning, etc.
● Working knowledge of the job’s domain (e.g. finance, healthcare)
Continuous learning is important, and it’s never a bad idea to brush up on your skills or learn a new tool through a class or course. But if you’ve got a strong STEM background and experience managing data, then making the shift into the emerging world of data scientist is more than possible without a new piece of paper. But a certificate may increase your odds of landing a job as a data scientist.
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