Data Scientists rely on a number of specialized tools and programs developed specifically for data cleaning, analysis, and modeling. And while the BrainStation Digital Skills Survey revealed that Excel is the most widely used program in the field, it also showed that Data Scientists rely on it much less than Data Analysts do.
What Are the Most Common Tools for Data Science?
In the BrainStation Digital Skills Survey, Data Scientists cited statistical programming language Python as their most-used tool. Data Scientists also reported using a much wider variety of secondary tools, including SQL and Tableau. This tracks with the traditional understanding that Data Scientists have a more senior level of experience and training—additional skills and knowledge that can provide more exposure to a programming language like Python or other related technology, which are applied to the following areas:
Programming Languages for Data Science
While there are a handful of statistical programming languages, R and Python are by far the most popular data science programming languages. 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 can run complex statistical functions, including regression analysis, linear and nonlinear modeling, statistical tests, and time-series analysis, among others. R is better suited to smaller datasets, while Python comes in handy for Natural Language Processing applications. For some seriously heavy number-crunching, there are Hadoop-based tools like Hive.
One of a Data Scientist’s most important tools is RStudio Server, which supports a development environment for working with R on a server. Open-source Jupyter Notebook is another popular application, comprising statistical modeling, data viz, machine learning functions, and more.
Machine Learning Tools
Machine learning tools apply artificial intelligence to give systems the ability to learn and become more accurate without being explicitly programmed. The tools used for machine learning depend to a large extent on the application—whether you’re training the computer to identify images, for example, or extract trends from social media posts. Depending on their objectives, Data Scientists might choose from a wide range of tools including h2o.ai, TensorFlow, Apache Mahout, and Accord.Net.
Data Visualization Tools
Visualization tools help Data Scientists present complex data in an endless array of charts and graphs—a task that can be as much art as it is science. Using programs like Tableau, PowerBI, Bokeh, Plotly, and Infogram, Data Scientists can convert millions of unwieldy data points into easy-to-read—even beautiful—chord diagrams, heat maps, scatter plots, and more.
In addition to these broad categories of tools, Data Scientists also need to be very comfortable with both SQL (used across a range of platforms, including MySQL, Microsoft SQL, and Oracle) and spreadsheet programs (typically Excel). Although the basic premise behind spreadsheets is straightforward—making calculations or graphs by correlating the information in their cells—Excel remains incredibly useful after more than 30 years, and is virtually unavoidable in the field of data science.
We’ve already hinted that Data Scientists rely on a wide range of tools, but the results of our Digital Skills Survey reveal just how wide that range really is. Even given a long list of popular programs from which to select their most-used tools, 32 percent of respondents chose “other”—suggesting that regular use of a long tail of highly specialized programs is, in fact, the norm.
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