5 Essential Skills for Success in Data Science Training Programs

Quantitative ability, problem-solving mindset, programming proficiency, effective communication, and team player skills

Photo by Sharon McCutcheon on Unsplash

According to IBM, in 2019, businesses were creating and storing almost 2.5 quintillion bytes of data every day. Big Data is big business and businesses are swimming in oceans of valuable data. As one of the fastest-growing, multibillion-billion dollar industries, corporations, and organizations are trying to make the most out of the data they already have and determine what data they still need to capture and store. In addition, there continues to be an incredible need for data scientists to make sense of the numbers and uncover hidden solutions to messy business problems. With all the exciting opportunities in data science, educating yourself about data science is a great way to gain the skills and experience needed to stand out in this competitive field.

If you interested in learning about data science, it is a good idea to ask yourself this question:

Do I have the necessary background to succeed in a data science or business analytics training program?

Whether you are interested in pursuing a degree in data science from a university or in self-study for instance via massive open online courses (MOOCs), it is important that you understand the prerequisites for data science before beginning your journey.

To answer the question above, we examined the admission requirements from various universities that offer a master’s in business analytics or data science such as Harvard University, Columbia University, Kansas State University, Duke University, Syracuse University, Walden University, and the University of California Berkeley. We found that for an overwhelming majority of these programs, the most important prerequisite for data science or business analytics is strong quantitative ability. See admission requirements from these universities below.

Duke University: A bachelor’s degree in science, technology, engineering, mathematics, business, economics, or an equivalent quantitative major by the start of the program.

Kansas State University: Completion of STAT 350 and STAT 351 (or equivalent statistics courses). Familiarity with computer programming and applications highly recommended.

Columbia University: A bachelor’s degree in a related field of engineering or science.

Harvard University: There are no prerequisite courses required, but the quantitative experience will be vital to your success in the program.

Syracuse University: An undergraduate degree in business, statistics, math, engineering, finance, information technology, physics, supply chain, or economics.

University of Tulsa: A four-year degree in business, engineering, or science.

Walden University: A bachelor’s degree in data science, computer science, information technology, or an equivalent subject.

In the next section, we discuss the top 5 general skills (based on admission requirements from university training programs considered above) essential for success in a data science or business analytics program.

a) A high level of quantitative ability: A high level of quantitative ability as evidenced by a bachelors degree in a quantitative discipline such as mathematics, physics, computer science, accounting, or engineering. A bachelor degree might not be required, but at least strong math skills are essential for success in data science.

b) A problem-solving mindset: Data science by definition a scientific discipline that uses data to build models that can be used to draw out meaning patterns or for predictive purposes. Data science is thus a problem-solving discipline. Therefore, a strong problem-solving mindset will give you an advantage.

c) Programming proficiency: Essential programming skills are required in data science. Familiarity with the following libraries/packages is essential:

Ggplot2PandasNumpyMatplotlibSeabornScikit-learnCaretTensorFlowPyTorchKeras

d) The ability to communicate effectively: As a data scientist, you might have a great portfolio of technical skills, but if you can’t communicate effectively, you won’t be able to convey your ideas clearly. It is therefore crucial that strong technical skills be combined with communication savviness. As data science is a very broad field, most real-world industrial projects would require you to work with a team of personnel with different backgrounds such as data analysts, data engineers, business executives, project managers, etc. Good communication skills will help you to thrive in such an environment.

e) Ability to work in a team: Being a good team player world help you to thrive in a business environment and maintain good relationships with other members of your team as well as administrators or directors of your organization.

In summary, we’ve discussed 5 essential skills required for success in data science training programs. Anyone with the right motivation and passion can learn the foundations of data science. However, a background in an analytical discipline such as physics, mathematics, computer science, engineering, or economics would serve as an added advantage and help boost your chances of success in the program.

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Physicist, Data Science Educator, Writer. Interests: Data Science, Machine Learning, AI, Python & R, Personal Finance Analytics, Materials Sciences, Biophysics

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