Top data science skills to master

student research

Learning these in-demand technical data science skills will set you up for success in your career.

Data science professionals help businesses from all industries make strategic decisions by drawing insights from big data. Those who master the most in demand skills and methodologies can enjoy a rewarding career and establish themselves as a sought-after technology professional.

Programming languages

Many of the methodologies data scientists use to draw insight from data depend on applying computer programming languages. Python and R are often noted as the most popular languages, but you should also learn SAS, Java, JavaScript, Julia, Scala, MATLAB, TensorFlow and Go.

Math and Statistics

A strong background in math and statistics is essential for a data scientist. You will use probability and statistics to explore and understand data. Likewise understanding multivariate calculus and linear algebra is important to building machine learning models.

Learn the skills and methods you need to enjoy a rewarding career in data science with Meharry.

Responsible data stewardship

The information data scientists access must be handled in an ethical, responsible manner. Therefore, it is important to implement best practices for data management and stewardship, as well as conduct research in an ethical manner that maintains data security and privacy.

Machine learning

From Netflix recommendations to suggested news articles on Facebook, machine learning is everywhere in the digital landscape. Machine learning is a method to analyze data with little human involvement or programming. Joseph Roccca writes in that machine learning is “incredibly useful for difficult tasks when we have incomplete information or information that’s too complex to be coded by hand. In these cases, we can give the information we have available to our model and let this one ‘learn’ the missing information that it needs by itself. The algorithm will then use statistical techniques to extract the missing knowledge directly from the data.”

Deep Learning

Deep learning is a type of machine learning and artificial intelligence. It uses neural networks that combine machine learning algorithms to solve tasks. Data scientists use deep learning to make the process of collecting, analyzing and interpreting data easier. Becoming Human: Artificial Intelligence Magazine says that future applications include “. . . technologies such as: finance, autonomous vehicles, and health care.”

Data visualization and communication

Data visualization is a graphical representation of findings from a data science method such as machine or deep learning. Visualization is essential to communicating complex results, especially to a less technical audience. Effective data visualization will help you communicate your insights so that they impact business strategy.

Cloud computing

Data scientists use cloud computing to access databases, frameworks, programming languages and operational tools. Rashi Desai emphasizes the importance of cloud computing in, saying that “familiar with the fact that data science includes interaction with large volumes of data, given the size and the availability of tools and platforms, understanding the concept of cloud and cloud computing is not just a pertinent but a critical skill for a data scientist.”

Data Wrangling

A common real-world data science problem is acquiring data with imperfections that isn’t ready for analysis. This is where data wrangling comes in. As you may expect, data wrangling is the process of dealing with data that is not ready for analysis. It involves transforming and mapping data to prepare it for statistical analysis and decision modeling. Strong data wrangling skills will reduce the time it takes to process this data and allow your team to focus on analysis.

Database management

Database management involves using Python, SQL, SAS, and QGIS programming languages to store, analyze, and manage information. It is a highly valued skillset because of the massive amount of information involved with data science. Phoebe Wong, senior director of data science & analytics at the Voter Participation Center writes in writes that, in the industry setting “. . . getting the raw data from various sources to be ready for modeling is usually 80% of the work.”

How to acquire these top skills for data scientists?

Data science is a competitive field that requires developing expertise. So pursuing a master’s program that will help you acquire these industry-relevant skills and real-world experiences is critical to success in a data science career.

Next steps

Learn more

The School of Applied Computational Sciences offers two master’s degrees—M.S. Data Science and M.S. Biomedical Data Science. Explore these programs and see how they can launch your career.


Read about the skills and methodologies you will learn through our M.S. Data Science and M.S. Biomedical Data Science courses.

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