Online Master of Science in Data Science

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Online M.S. in Data Science Curriculum

Data collection and interpretation is everywhere in today’s fast-moving world. Almost every industry benefits from the knowledge and expertise of data scientists.

If you want to make it as a data science professional, an advanced degree in the field can provide you with an array of useful skills that may prepare you for rewarding careers. When you complete a well-structured data science curriculum, you have the opportunity to build exposure to such areas as data mining, programming, coding, and analysis.

Maryville University Online MS in Data Science Curriculum

We collaborated with industry leaders in data science to design a graduate-level program that reflects the field’s most sought-after skills. That means you’ll complete a 36-credit-hour curriculum focused on everything from machine learning and mathematics to predictive modeling and programming. Your coursework is also project-based — which means you’ll complete hands-on projects that allow you to see the practical application of your studies.

Data Science Required Courses (24 credits required of DSCI 500+ level)

DSCI 501Math Modeling 3 Credits
DSCI 502Introduction to R3 Credits
DSCI 503Introduction to Python3 Credits
DSCI 504Introduction to SQL3 Credits
DSCI 507SAS Programming3 Credits
DSCI 508Machine Learning 3 Credits
DSCI 512Predictive Modeling3 Credits
DSCI 598Capstone Project3 Credits
DSCI 609Forecasting Principles
DSCI 613NoSQL Database3 Credits
DSCI 614Data Mining3 Credits
DSCI 617Big Data Analytics3 Credits
DSCI 618Experimental Design3 Credits
DSCI 619Deep Learning3 Credits
DSCI 624Data Analysis & Visualization3 Credits
DSCI 698Thesis/Research3 Credits

Data Science Electives (12 additional credits required of ACSC, DSCI, MATH 500 level or higher)

MATH 506Statistical Modeling II3 Credits
MATH 570Probability I3 Credits
MATH 571Probability II3 Credits
MATH 572Mathematical Statistics3 Credits
ACSC 514Theory of Interest3 Credits
ACSC 515Financial Mathematics3 Credits
ACSC 516Corporate Finance I3 Credits
ACSC 594Actuarial Seminar I3 Credits
ACSC 595Actuarial Seminar II3 Credits
ACSC 599/699Internship3 Credits

To ensure the best possible educational experience for our students, we may update our curriculum to reflect emerging and changing employer and industry trends.

What are some common skills and competencies taught in an M.S. in Data Science curriculum?

A typical master’s degree in data science can provide you with a comprehensive understanding of the ever-changing landscape of data collection and interpretation. Typically, you’ll have the chance to specialize within the degree program, tailoring your experience to individual career interests and goals.

Some of the skills gained through a data science curriculum may include:

  • Computer coding and programming. Several programming languages are particularly relevant for students pursuing data science careers. Master these programming languages, and you can interpret data, track trends, and make predictions, all valuable skills in a wide array of industries. An in-depth understanding of specific programming languages, such as Python, SQL, SAS, and R, is covered in the data science curriculum.
  • Data mining. With so much information available, it’s important for you to be able to decide which data is useful. Data mining skills allow professionals in the field of data science to extract and predict patterns, even in large data sets. When you hone your data mining skills, you can use this information to make predictions and recommendations to inform strategy and decisions for your business or organization.
  • Machine learning. Adaptability is key in today’s technological world. Companies often call on data scientists to train machines to understand data and take appropriate actions. Machine learning allows computer programs to adapt to new circumstances through experience, making processes more efficient.
  • Experiential learning. The technological and data landscape is quickly and constantly evolving. When you study from a master’s-level data science curriculum, you can learn through doing, allowing you to adapt to a field where new advances emerge daily. By developing the dynamic ability to shift and grow based on current trends and hands-on experience, you can keep your skills relevant even as technology undergoes changes.
  • Statistics and mathematics. Develop a basic understanding of statistics and mathematics through your data science curriculum, and explore how these subjects apply to data science as a whole. Learn to use mathematical theorems and principles in your work, and you can build a professional advantage through your understanding of statistics and calculus.

What are some common master’s in data science courses?

An advanced data science curriculum can provide you with practical skills that prepare you for real-world success. The coursework itself combines core coursework with electives chosen based on areas of interest, allowing for a truly personalized degree. Some of these courses include:

  • SAS Programming. Focus on the development of programs that analyze data through a statistical model in SAS programming (also known as statistical analysis system programming). Learn skills that involve managing databases, interpreting raw data and trends, and updating database systems. When you complete an SAS programming course, you’ll have the chance to learn the ins and outs of this language, as well as discover practical applications in a variety of organizations.
  • Big Data Analysis. Every day, companies and organizations collect a massive amount of information. That data is then filtered, interpreted, and made accessible. While programming skills often provide the technical know-how, big data analysis can allow you to become a data science expert to parse information and present your trends and findings to key stakeholders. In a big data analysis course, you can learn strategies for examining data and using it to make informed, data-driven recommendations and decisions.
  • Statistical Design. A firm grasp of statistics, mathematics, and design should give graduates with advanced data science degrees a strong foundation in the interpretation process. A deep understanding of statistical design allow you to make stronger predictions and suggestions based on the output of various databases and programs. For this reason, statistical design is an important component of an advanced data science curriculum.
  • Deep Learning. Deep learning is a form of machine learning that focuses on creating a digital neural network — much like our own brains — which can then interpret and learn from wide swaths of data. Skills in deep learning can help you devise machines that are able to learn and adapt quickly. Follow project-based assignments to gain an in-depth understanding of how neural networks apply to artificial intelligence, learn the applications of deep learning, and see how you can develop your own deep learning neural network.
  • Predictive Modeling. Test your problem-solving and communication skills as you apply statistical learning to contemporary business problems. Through the study of various models, such as linear and tree-based models, you can learn to design a predictive model and forecast outcomes.

Learn more about the M.S. in Data Science curriculum.

If you’re excited by the possibilities presented through data science, consider an online Master of Science from Maryville University.

This advanced degree, earned entirely through an easy-to-navigate online learning platform, can equip you with invaluable tools in today’s digital economy. When you choose to pursue your master’s in data science, you can build the skills to seek employment in a variety of sectors and industries.

Take the next step toward a rewarding future and see what our online M.S. in Data Science can do for you.