Data science careers are booming as employers seek data analysts, big data engineers, and data architects to help them manage huge data streams.
We live in a world of data.
Although we associate the term “data” with the tech sector and firms like Microsoft, Google, and Amazon, the truth is that data and the data scientists who manage it all are everywhere. Firms in such diverse sectors as retail, finance, telecommunications, healthcare, and agriculture are looking for people who can help them collect, filter, interpret, and analyze the data that keeps their businesses running.
But what does data science really involve, and what do data scientists actually do? If you pursue a master’s degree in data science, what kind of career opportunities can you expect? In this blog post, we clear away the mystery of this career path, exploring why getting a master’s degree in data science might be the one best thing you can do to boost your career.
What is Data Science?
Data science is the study of data to extract meaningful insights for business.
Data scientists are part mathematicians and part computer scientists. They handle, organize, and interpret massive volumes of information with the goal of discerning patterns. They usually work on behalf of organizations where their data insights are used to develop business strategy. As professionals with a foot both in IT and business, data scientists have a unique perspective on what makes things tick.
Data scientists tap into a wide range of skills that include math, statistics, computer programming, and machine learning — always looking for unseen patterns in the numbers they collect. They construct complex machine learning algorithms to build predictive models and artificial intelligence (AI) systems that can generate the insights that analysts and business managers will ultimately use to improve their businesses.
As they take a project from start to finish, data scientists traverse several stages of what is known as the data science lifecycle.
First, they define the problem. Then, they gather data, filter it, and merge different datasets. They analyze the data, then ultimately build models using algorithms to create a machine learning model. Once a model is built, data scientists test it to make sure it is solving the problem it was designed to address.
The final step is to communicate with stakeholders, often through data visualization charts and graphs. Stakeholders can then make informed decisions about how to take their business to the next level.
Skills Needed to be a Successful Data Scientist
Great problem-solving abilities and communication skills are the fundamental qualities needed in this job, but there are several other skills that are absolutely critical to success.
A firm grasp of programming languages is key to manipulate and organize data. Typical languages data scientists use include Python, R programming, SQL, and Scala. You’ll also need to be familiar with database languages, like MongoDB and MySQL.
Data Visualization and Analysis
A major aspect of this role is knowing how to create charts and graphs that provide an easy-to-grasp picture of the patterns you discover for stakeholders.
Math and Statistics
You’ll need a strong command of statistics and mathematics, including familiarity with linear algebra, calculus, probability distribution, regression, dimensionality reduction, and vector models.
Machine Learning, Deep Learning, and Artificial Intelligence
An understanding of machine learning, artificial intelligence, and deep learning — a subset of artificial intelligence — is important, as these are tools you will use to perform your job.
As mentioned earlier, you’ll need to be able to analyze a problem by breaking it down into multiple parts. You will also need enough creativity, logic, and persistence to tackle a problem until you’ve found a solution.
Today, most data scientists focus on data collection, data cleaning, building dashboards and reports, data visualization, and statistical inference, as well as communicating their findings to key stakeholders.
But as the pace of technology continues to ramp up, it is likely that the role will evolve. Many of the tasks that are today conducted by data scientists may be performed by computers and AI. A key skill in the future will therefore involve the ability to adapt to new technologies and to learn on the fly.
While data scientists must have all the technical skills to extract, understand, and analyze data, one of the most important skills is knowing how to communicate well in order to answer business questions and explain complex results to nontechnical stakeholders.
What Are the Advantages to Having a Master’s Degree in Data Science?
A master’s degree in data science to your resume can offer the following benefits:
A Deeper Understanding of Theory and Practice in Data Science
You’ll gain in-depth training in mathematics, statistics, and computer science compared to what you received in your undergraduate studies. You’ll also develop critical thinking and quantitative, domain-specific skills likely to remain in demand even as this fast-paced field quickly evolves.
A Path to Leadership Positions
With more education under your belt, you can advance to higher-level positions such as machine learning engineer, artificial intelligence engineer, data architect, enterprise architect, or applications architect.
Potential for a Higher Salary
Those with more education tend to get more substantial salaries. The average base pay for a data scientist in the United States is about $124,000 according to the Bureau of Labor Statistics, but those in senior positions can expect to make as much as $160,000 or more.
What Type of Jobs Can You Do with a Master’s Degree in Data Science?
The world of data science is exploding, with an ever-expanding number of roles in this dynamic field. A few of the most common data science jobs include:
- Artificial intelligence engineer: uses traditional machine learning techniques to create models that power applications based on AI.
- Data scientist: through statistics, math, and programming, analyzes data sets and identifies patterns and problems that might benefit organizations.
- Data engineer: looks for trends in large data sets and builds algorithms to help organizations mine useful information from raw data.
- Machine learning engineer: creates and runs automated software programs capable of building predictive models from large data sets. The programs “learn” from the information collected, helping them develop more accurate predictive models.
- Software engineer: develops code and operates data tools.
- Data modeler: manages data and creates structures for it through software
- Data analyst: analyzes a company’s data to identify trends, find insights and solutions based on that data, and project future outcomes.
- Big data engineer: collects and prepares large quantities of data.
What Type of Advanced Topics do Data Scientists Work On?
While data scientists can work on almost any project in any field, data scientists with a master’s degree may have a greater selection of in-depth roles to choose from.
You might find yourself working in government or private industry, in healthcare, tech, or transportation. Roles you may never have thought of include:
- A disease mapper. At a health department, you could build predictive epidemiological models to forecast the spread of infectious diseases. Chicago, for example, has adopted an algorithmic approach to food safety, sending inspectors to locations where they think there could be a high risk of foodborne illness transmission.
- A cyber city analyst. In city government, you could manage numerous urban data flows ensuring that city systems work the way they should. In Kansas City, Missouri, for instance, government officials rely on “pothole prediction” technology to prevent potholes from developing.
- An autonomous transport specialist. On behalf of an autonomous vehicle manufacturer, a data scientist could build scenarios and crunch data yielding meaningful ways to improve products and safety.
- A roboticist. On behalf of companies in fields ranging from healthcare to manufacturing, you could build algorithms to help robots acquire new behavioral patterns and the ability to operate semi-autonomously. Robots could then be used in more industries where there are labor shortages, including in big agriculture where “agrobots” can now even assess the ripeness of a fruit.
Are Data Science Careers in Demand?
The short answer is yes. In fact, the data science field is on fire, with jobs expected to grow by 36 percent between now and 2031. As computers and data infiltrate every corner of our lives — from healthcare to digital marketing to financial services, technology, retail, media, and telecommunications — there is a growing need for someone to manage and interpret it all.
How Can I Pursue a Master’s in Data Science?
If you’re interested in pursuing a master’s degree in data science, you have made a great choice. This cutting-edge field is expanding, and there are many options for advanced training, including graduate degree programs and graduate certificates.
A master’s degree in data science promises a fascinating career with solid job growth for years to come.