The demand for data science professionals is skyrocketing. Here are the top programming languages needed for a career in data science.
As we’ve written about, data analysis (or data science) is the process of collecting, transforming, and modeling data to discover information that can inform business strategy and decision-making.
Demand for these kinds of data skills is expected to grow by 39 percent in the next two years according to this IBM report. It’s a ‘quant crunch’ that shows no signs of slowing, with companies using data analysis more broadly than ever before.
While data analysis can be applied to any size of data set, it’s the newly voluminous amount of data associated with our actions in the digital age —the digital traces of our lives—that is fueling this new demand. It’s not an exaggeration to say that it is changing the way the world does business.
Winston Lee, a student in Brainstation’s part-time Data Analytics course, is a good example of the new frontiers for analytics. Lee’s family business is the ceramic powder Zirconia, and he intends to use data analysis to find new and profitable applications for this raw material.
But Lee is not limiting himself to Zirconia. “Like many others,” he says, “I believe that data is integrated into every aspect of our lives and therefore by having the skills to collect, clean, organize, contextualize, draw patterns, and present data is very useful in any business.”
The question is…
What Skills Do You Need to be a Data Analyst?
The best data analysts have a mix of technical and soft skills, which allows them to become a valuable member of their company’s decision-making process.
This mix of skills includes:
Hands-on Experience With Various Tools
Successful data analysts will need to keep up-to-date with the latest and greatest tools associated with data analysis, including:
- Spreadsheets and querying languages like XML and SQL
- Programming languages and frameworks like Python, R, and Hadoop
- Visualization tools like Tableau, PowerBI; Plotly, Bokeh, and Matplotlib
They will also need to have experience with one or several leading analytics platforms, including Google Analytics and Adobe Analytics.
Creative and Analytical Thinking
Data analysts are tasked with understanding both the data and the company’s business well enough to extract meaningful and actionable inferences from raw numbers. They’ll therefore need to understand the challenges facing the company and its industry, and be able to think outside the box to develop effective questions and creative solutions.
More specifically, they’ll need the analytical skills to be able to identify patterns, trends, and relationships, make and test inferences, form judgements, and draw conclusions.
This is one of the reasons why biostatistics professor Jeff Leek once wrote that “the key word in data science is not data; it is science.”
That said, analysis like this does require…
A Way With Numbers
It can probably go without saying, but just in case, a data analyst must be comfortable working with numbers.
Mathematical modeling, performing statistical regressions, multi- and univariate analysis, and other kinds of manipulation of raw data are necessary to isolate and extract relevant information.
An Eye for Detail
Data analysts must 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.
Christian Bonilla, an Analytics Software Designer, puts it this way: “The discipline of querying databases…forces you to clean up lazy thinking, because computers don’t allow vague questions.”
An Ability to Communicate Ideas
For data analysis to serve its purpose, it must be effectively communicated to the organization’s leaders. Data analysts, therefore, rely on their written and oral communication skills to make their findings understandable and compelling.
Here’s how the authors of Doing Data Science described the communication objectives for a data analyst:
“She’ll communicate with team members, engineers, and leadership in clear language and with data visualizations so that even if her colleagues are not immersed in the data themselves, they will understand the implications.”
As part of this, analysts must be able to work well with managers, developers, users, and clients on a regular basis. Clarity of expression, active listening skills, confidence, and the ability to collaborate and cooperate are essential.