What Skills Do You Need to Be a Data Scientist?

Because Data Scientists are typically more technically experienced, senior employees, the position tends to demand a combination of skills: the hard skills of a highly trained specialist paired with the soft skills of a senior employee in a leadership or decision-making position.

Let’s unpack a few of the most important technical Data Scientists skills routinely use. The top technical skills for data science can be organized into three major categories:

All that data has to come from somewhere. It also has to be consistent and organized for it to produce reliable insights. This isn’t as straightforward as simply casting a net—the Data Scientist should know how the data will be used, how to manipulate it into a usable form (that is, data cleaning and wrangling), and how to turn it into an effective database (in a word or two, database management). You might also hear these steps referred to as data extraction, data transformation, and data loading. Whatever you call it, familiarity with Excel and querying languages like SQL is indispensable.

Databases are, by their very nature, nice and tidy. But not all data is so cooperative. Data Scientists frequently work with unstructured data—information that doesn’t fit neatly into tables, such as audio and video, customer feedback replies, or social media posts. Because they’re not numerical or streamlined, finding ways to make this data usable can be a challenge—one that falls squarely on the Data Scientist’s shoulders.

Python, R, Hadoop, and Spark, among other analytical tools, help Data Scientists to quantify and analyze data sets using statistical methods, run tests, and create models that can be used across a wide range of applications, from finance to e-commerce to natural resources. Ultimately, the goal is to generate models that can derive new insights from data and predict unknowns.

The skills Data Scientists need to accomplish these tasks are as varied as the tasks themselves, but as a general rule, data wrangling, data exploration, analysis, and modeling lean heavily on a foundation of math and programming. This is also where data science–specific skills like machine learning and deep learning come into play.

Converting data from tables into charts and graphs—or even dashboards, which allow non-analysts to retrieve information in a more intuitive way—is an art unto itself. There are a range of tools Data Scientists use to accomplish this, including Tableau, PowerBI, Plotly, Bokeh, and Matplotlib, among others, each with its own strengths. It’s worth noting that software can’t tell you what type of visualization is most appropriate to highlight your findings—so a good understanding of the ways in which data can be visualized is a necessary first step.

The soft Data Scientist skills you’ll need are very easily honed in other fields. If you’re making a lateral move into data science, you may find you already have a strong grasp on a number of the following soft skills for data science:

It should come as no surprise that someone in a senior position, often working in a cross-disciplinary capacity, needs to be good at working with others.

Depending on the sector, Data Scientists may need to have a firm grasp on business principles (and their own company’s objectives) in order to channel their technical skills into productive channels. This means being able to spot areas for potential growth or increased efficiency, which can then be tackled using a data science approach.

Charts and graphs will only get you so far—at some point, you’ll need to engage with others to discuss how data science fits into your organization’s overall strategy. More often than not, the people you’ll be engaging with have a pretty tenuous grasp of data science, meaning you’ll need to be able to communicate about different objectives, strategies, and techniques the old-fashioned way: in plain English.

No surprise here. You might even think of data science as the skill of solving problems using data. For this, objectivity and good judgment are a must.

If data science is the “what” and “how” of problem-solving, data intuition is the “where.” There are no roadmaps here; much of the practice of data science relies on creativity and a sense of where to look—where hidden patterns might be lurking, waiting to be uncovered, and how data science might tease them out. This also involves a sense of how data is (or isn’t) structured, and how that structure can be manipulated from the initial vague idea for a test into a workable model and, ultimately, a final business decision. This is one skill that can’t be taught, only picked up through experience.