13 Skills You’ll Learn With a Master’s in Data Science

IBM defines data science as “a multidisciplinary approach to extracting actionable insights from the large and ever-increasing volumes of information collected and created by today’s organizations.” Modern businesses and organizations need an ever-increasing amount of data (and data science skills) to compete and thrive in our complex, data-driven world. The US Bureau of Labor Statistics (BLS) lists data science among its 20 fastest-growing job markets, projecting a 31 percent growth rate in the field between 2019 and 2029.

How did data ascend to its current apex? Intel co-founder Gordon Moore predicted in 1965 that computer chip processing power would double roughly every 18 months. What became known as Moore’s Law led the way for more than processing power; it also delivered quantum advances in data visualization, artificial intelligence, and cloud computing. Among the results: the advent of data science.

With the growth of data sources and increased storage capacities, the need for programmers and analysts to accumulate, decipher, condense and report reams of data is greater than ever. It’s a burgeoning field full of endless possibilities in need of skilled professionals adept at number crunching and computer programming.

The vast majority of data scientists hold graduate degrees like the Master of Science in Data Science. While you don’t technically need a graduate degree to work in this field, you’ll be hampered without one because so many of your competitors for jobs will have a master’s or PhD.

That’s why you should consider earning a data science master’s. This article covers 13 essential skills you’ll learn with that degree: eight hard skills and five soft skills. All will help you thrive in any organization.

What is a master’s in data science?

According to the BLS, data scientists “invent and design new approaches to computing technology and find innovative uses for existing technology.” The government agency categorizes data scientists as research scientists.

Many entry-level data science jobs require a master’s degree in data science. You’ll need to earn a bachelor’s degree first. While they don’t require it, most graduate admissions offices prefer a degree in a computer science-related field (e.g., computer science, information systems). Other qualifying majors include mathematics, statistics, engineering, and physics. That said, it is possible to enter a data science master’s program with an English major; you’ll just likely need to complete a raft of prerequisite courses before you can begin graduate work.

Many universities offer a master’s in data science; the list continues to grow as the field expands. Curricula cover data analytics, data engineering, applied statistics, quantitative analysis, and business intelligence. Most data science graduate programs include a capstone course, research project, practicum, or internship requirements that allow students to address real-world problems related to computational data science.

Earning a master’s degree in data science enables students to enter the computer and information research scientists career field. BLS data indicate that data scientists most often work for:

• The federal government: 28 percent
• Computer systems design and related services: 19 percent
• Research and development in the physical, engineering, and life sciences: 16 percent
• Software publishers: 8 percent
• Colleges, universities, and professional schools: 6 percent

Data science career options are plentiful. In fact, there’s an acute shortage of data scientists in the U.S. and abroad. Employment website LinkedIn ranked data science third in its top 15 emerging jobs in the U.S, projecting 37 percent annual growth.

Suffice it to say, data science is hot. But most jobs demand a delicate balance of data science skills and experience. What proficiencies can you expect to gain from an advanced degree? Let’s explore 15 skills you’ll learn with a master’s in data science.

What hard skills will you learn in a data science master’s program?

Data science is an evolving discipline requiring skills in mathematics, statistics, computer programming and data analysis. And that’s just for starters. Advanced technical skills can help you advance quickly by establishing a unique and valuable skill set. Many of the skills detailed below apply to a range of projects and tasks found in big data organizations.

Data analysis

Data analytics is integral to data science. In fact, the work of data scientists and data analysts overlap because data science is, at its core, advanced analytics. Data analytics is number-crunching on steroids.

The first semester of a master’s in data science typically focuses on classes with titles like “Scalable Data Systems & Algorithms” and “Statistical Machine Learning.” These teach the basics of data analysis and how to apply those concepts to technologies used in data science, including machine learning algorithms, artificial intelligence (AI), and advanced coding.

Data mining

Data miners sift through large volumes of data to find the nuggets of actionable items based on patterns and predictive analysis. Data mining shines when algorithms dig through huge amounts of data to locate and segment predictive outcomes. The key properties of data mining include:

• Automatic discovery of patterns
• Prediction of likely outcomes
• Creation of actionable information
• Focus on large data sets and databases

Large organizations rely on data mining to forecast business outcomes based on selected criteria. Predicting sales, revenue, and market changes requires sophisticated programming and coding skills gained through a master’s in data science.

Data visualization

Data visualization is the graphical representation of information and data. Once it’s been mined and analyzed, data is processed into physical form in the shape of tables, graphs, histograms, and maps. Organizations need to visualize current operations and predictive outcomes distilled into easy-to-understand reports for key stakeholders.

Data mining skills are invaluable to any business intelligence professional. Master’s in data science programs focus on developing the technical skills to operate the most popular data visualization tools such as d3.js and Tableau.

Data wrangling

Although it conjures images of cowboys and rodeos, data wrangling—also called data cleaning, data remediation, or data munging—actually refers to various processes that transform raw data into more readily used formats. Indeed, the “cleaning” of data is the underlying essence of data wrangling.

Finding gaps and outliers in data and purging irrelevant information from data sets for analysis requires skill sets in programming and statistical analysis. Learning data mining in a master’s of data science program often entails learning R and RStudio, a popular data mining system.

Artificial intelligence/machine learning

We’ve all seen movies and TV shows featuring robots and androids. They represent the furthest extremes of artificial intelligence (AI). Making computers more human-like with neural networks that enable decision-making, image and speech recognition, problem-solving, and translation creates an evolving, potentially endless future of computer evolution.

Practical applications for AI in the 2020s include typical back-office administrative and financial activities utilizing robotic process automation technologies. AI is also used in data analytics to predict future business outcomes and automated functions in customer service.

Machine learning (sometimes called deep learning) is a subset of AI that uses predictive analysis to run computer networks and programs. For example, machine learning will curate your Netflix or Spotify account to feed more recommended shows and songs based on your viewing or listening behavior.

Mathematics

Computers operate along mathematical principles. They start with code combining zeros and ones into binary data bits and corresponding text to program and display information. They advance to complex mathematical logic that makes operations, integrations, and networking possible. Undergraduate degrees in mathematics, applied mathematics or statistics often provide a springboard into data science.

A master’s in data science requires a whole lot more than the ability to add and subtract. A curriculum rich with advanced statistics, probability and nonlinear optimization, and linear algebra provides the math skills needed to run the sophisticated data analysis in today’s business intelligence jobs.

Programming

Computer programming is the most fundamental data science skill. Programmers work with SQL databases and programming languages like Java, JavaScript, Hadoop, TensorFlow, Apache Spark, and Python libraries. Data scientists can work with those programming languages and develop programs using statistical software like SAS and R. They even use old-fashioned Excel from time to time to sort and batch numerical data.

As noted earlier, data scientists use software tools including R and also Python, a programming language popular for its ease of use and readability.

Master’s of data science programs usually require some programming skills as a prerequisite for admission. Advanced course topicslike”Dynamic Programming and Reinforcement Learning” and “Database Management Systems” take programming to higher levels of design and querying of relational databases.

Software engineering

Data science is a more specialized field of statistical analysis with models like regression, optimization, clustering, decision trees, random forests, and predictive models fueled by potent software. Software engineers create operating systems and programs, while data scientists use those programs to collect and analyze data.

Schools that offer master’s in data science degrees often have concentrations in software engineering to teach advanced techniques in programming languages and query languages like SQL/NoSQL and Pandas.

What soft skills will you learn in a data science master’s program?

Developing and growing a career takes more than technical know-how. It also requires more people-oriented traits to navigate the office hierarchy and team-oriented structure found at most organizations. Here are five nontechnical skills learned with a master’s degree in data science that will make you a well-rounded professional.

Communication

Beyond speaking and writing well, communication as a soft skill in data science involves working with colleagues and presenting data analysis findings. Master’s in data science programs feature collaboration with classmates that translates to the real world.

Strong communication skills are essential to translate data scientists’ complex, technical work into plain language for stakeholders to understand. Data science programs often require capstone projedts involving teamwork and collaboration. You and your peers will hone communication skills as you develop your thesis and results in a process approximating workplace presentations.

Critical thinking

Critical thinkers see the world as a question to be answered. They challenge belief systems and investigate contrary evidence. They ask for information and analyze utilizing strategies that decipher new meanings. Employers seek employees who never stop learning and can analyze a wide range of subjects.

Is critical thinking a skill learned in a master of data science? It’s important enough that graduate schools often have classes devoted to the skill. It’s usually covered at the undergraduate level as well.

Ethics

Ethics in data science isn’t just about doing the next right thing. Understanding that data—in its simplest format—is open to interpretation and developed into a narrative is a great first start in computer ethics. Data’s capacity for doing good (and doing bad) drives ethical concerns across technology as it becomes more powerful and ubiquitous in society.

Developing ethics skills is important as issues of privacy, consent and legality arise with the spread of data science. Huge stores of human data, like those found on social media platforms, have implications across a huge swath of society. Most data science master’s programs have ethics as part of the standard curriculum.

Management and teamwork

Data science in organizations is often part of a management and team structure. The basic business acumen of teamwork and project management is vital in business intelligence departments. Understanding the structure and operation of organizations, revenue generation and allocation, and buzzwords ‘like key performance indicators’ equals business intelligence.

Advancing to a master’s degree in data science enables students to gain management and teamwork skills via their curriculum and team data science projects. Look for classes that feature management, financial, accounting, and marketing analytics to gain a holistic understanding of business problems, processes, and goals.

Planning and strategy

Along the same lines as management and teamwork, planning and strategy in data science involve understanding the greater uses for the data you’re collecting. Organizations need data to analyze profit margins and redundancies in operations, for example.

The growth of data science corresponds with the increased reliance on big data in business decision-making. Practically all organizations today analyze performance data to drive process improvement and managerial problem-solving. Predictive analytics like linear regression show decision-makers a future vision to determine the best course of action in the marketplace.

Course work in management as well as in financial, accounting, and marketing analytics is the sweet spot to earn the planning and strategy skills needed to analyze data and represent key findings in compelling ways for critical stakeholders.

Online master’s in data science versus traditional master’s in data science

COVID-19 has upended conventions across society, including in education. Although the trend of online learning in post-secondary education was already on the rise before the pandemic, colleges and universities have ramped up their virtual classroom course offerings in the wake of public health restrictions.

Data science is, by its nature, run on computers that make for a better online learning experience than other disciplines. But there are other advantages to online learning rather than the traditional in-person model.

Convenience

There are dozens of online master’s of data science programs in the U.S. They offer the convenience of any online learning experience, including at-home schooling and flexible coursework structure. Some online degree programs offer an accelerated curriculum that allows students to substantially reduce their time to earn their degrees.

Wider choice of programs

Online master’s in data science programs are plentiful if you’re looking for a recognized, accredited university that will lead to gainful employment and a successful career. You’ll undoubtedly enjoy a broader range of choices than you would if you limited yourself to what was available to you locally in an on-campus format.

Cost savings

Online programs typically charge similar, if not identical, tuition and fees as do on-campus programs. Still, you’ll save on gas and parking, and you’ll realize time savings by avoiding regular commutes to and from campus. Some online programs do charge substantially lower tuitions.

Should you get a master’s in data science?

Data science is a discipline on the rise. The pros of becoming a data scientist are compelling. Data science is one of the hottest branches of computer science in the business information field. Salaries are high and data scientists can advance quickly. Big data is driving job growth in data science and similar positions.

Jobs in data science are hailed by many sources as some of the best jobs in America. As you search for opportunities, remember that data science jobs may not be titled as such, depending on the employer. You can look for job titles like:

• Big data solutions architect
• Business intelligence analyst
• Business systems analyst
• Chief data officer
• Chief information officer
• Data engineer
• Data mining analyst
• Data modeler
• Data scientist
• Data strategist
• Data visualization developer
• Data warehouse analyst
• Machine learning engineer

As you can see, there are a plethora of similarly titled positions within data science. As for scientists in other fields, advanced skills and education learned at the master’s level transcend one pigeon-holed silo of expertise to offer a lifetime of flexible career options.

But is a master’s degree in data science worth it? As previously noted, most data scientists must think so; approximately 90 percent hold a graduate degree. It’s worth noting that that’s whom you’ll be competing with for your next data science job.

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