Data Analysis Skills

There are a variety of hard and soft skills needed to be a Data Analyst. Data analysis is ultimately about retrieving and expressing the information contained in data, so a Data Analyst skill set needs to be broad—ranging, from creative thinking and an eye for detail, to effective communication. But at the foundation of all the data analysis skills you need are the technical skills required to become a Data Analyst, beginning with a solid understanding of a few key programming languages. A Data Analyst course should provide both the technical training and hands-on experience needed for data jobs.

There are a number of technical skills that are required for a Data Analyst job, including a knowledge of SQL, various programming languages, and data visualization software.

There are other hard skills for Data Analyst jobs that you will need to develop — markup language XML, for instance, and analytics platforms like Google Analytics and Adobe Analytics — but with the following four data analytic skills under your belt, you’re well on your way to becoming a professional Data Analyst.

Here are the top technical skills Data Analysts need:

Short for “structured query language,” SQL is just what it sounds like—a formal language designed for querying a set of structured data, such as a database. Querying, in this case, simply means giving the computer a command to manipulate that data in some way. Data on its own is inert; SQL is a way to update that data, retrieve specific data or subsets of it, identify relationships between its variables, and so on. As a language, SQL is used across a range of platforms, including MySQL, Microsoft SQL, and Oracle.

At over 30 years old, Excel has proven that simplicity has its advantages. But just because Excel is simple in its basic premise (a spreadsheet that can perform calculations or graphs by correlating the information in its cells) doesn’t mean it’s not also incredibly useful. When it comes to working with data in its rawest form—including a wide range of simple visualizations, from pie and bar graphs to stacked area plots, dual-axis and combination plots, sparklines, and bubble plots—Excel remains the go-to program.

While there are a handful of statistical programming languages, the bulk of data analytics is achieved using R or Python. R is purpose-built for data analysis and data mining; the more widely used Python is a general-purpose programming language that also happens to be well-suited to data analysis operations. Both are used to run complex statistical functions including regression analysis, linear and nonlinear modeling, statistical tests, and time-series analysis, among others.

What’s the point of identifying meaningful trends in data if you can’t communicate them to others? Visualization tools pick up where Excel leaves off, letting Data Analysts take complex datasets and present them in a more intuitive way—typically through charts and graphs, a task that can be as much art as it is science. This is where software designed to turn millions of data points into sophisticated visual representations comes in, via programs like Tableau, PowerBI, Bokeh, Plotly, and Infogram, which can produce easy-to-read—and even beautiful—chord diagrams, heat maps, scatter plots, and more.

In addition to technical data analytics skills, there are a number of soft skills all aspiring Data Analysts should develop, including:

It should go without saying, but—a Data Analyst has to be comfortable working with numbers. And not just plugging figures into a piece of software; they need to have a head for abstract concepts like mathematical modeling, statistical regression, multi- and univariate analysis, and other ways of manipulating raw data to isolate and extract relevant information.

Great Data Analysts notice what others overlook. Once they spot it, they must then be precise. Precision in the number-crunching, yes, but also (and perhaps more importantly) in their approach. Human beings are great at understanding the intention behind a vaguely worded question—computers are not, so queries need to be exact.

So you’ve got a handle on a company’s data. Do you understand its business objectives well enough to know how that data can be applied? A top-tier Data Analyst understands the challenges facing the company and its industry, sees the right questions to ask, and how to develop creative solutions for them. More specifically, they’re able to identify patterns, trends, and relationships, make and test inferences, form judgments, and draw conclusions.

For data analysis to serve its purpose, its insights must be effectively shared with others—and charts and graphs will only get you so far (see also: asking the right questions, above). For this reason, Data Analysts also rely on their written and oral communication skills to make their findings understandable and compelling.

With these soft skills, Data Analysts are better equipped to work closely with Managers, Developers, clients, and users on a regular basis.