Dual Master's Programs in Data Science & Business Analytics

If you're interested in combining technology with an innovative business strategy, you may want to consider a dual degree in data science and business analytics. These degrees are offered at the master's level and offer a comprehensive education in both fields. Schools offering Business Intelligence degrees can also be found in these popular choices.

How to Earn A Dual Master's Degree in Data Science and Business Analytics

As you might expect, dual master's programs in data science and business analytics feature a collection of business courses, data science courses, and courses that blend the two subjects. Students can get a solid grasp of data topics, like programming, regression, and coding, while simultaneously honing their business skills through courses in marketing strategy, communications, and marketing management. These programs typically take about two years (four semesters) to complete.

Data Science Programming

Some of the most common programming languages taught in data science and business analytics degree programs are R and Python, and these courses serve as an introduction to coding using those languages. The course begins with an introduction to the open-source Python or R environment before moving on to more complex topics, such as machine learning and artificial intelligence. Students can also learn how Python and R can be used to extract data from websites and combine information from different sources.

Advanced Business Analytics

Having mastered the basics of analytics, students can next move on to more complicated ideas in this advanced course. Topics in an upper-level business analytics course could include principal component analysis, data mining, matrix algebra, and deep learning. This course also makes use of more sophisticated analytical tools, including R, TensorFlow, and SAS Viya.

Data Science Statistics

Students in this course can learn about the role that math, in particular statistics, plays in data science. Common concepts range from probability distributions to hypothesis testing. Using R, students can also study inferential statistics, chi-square distribution, and time series regression.

Big Data Management and Modeling

This course focuses on the tools and strategies used to organize and manage vast amounts of data. Students can build on their knowledge of traditional databases as they use tools such as SQL and Access to organize information. Data management courses usually feature a mix of traditional discussion and hands-on exercises designed to show the real-world implications of the subject.

Marketing Strategy

This course aims to provide students with both a theoretical and applied understanding of how to implement a marketing strategy. Students can gain exposure to business-to-business and business-to-consumer strategies. The course also takes a global perspective as it examines modern problems and solutions to the new multinational business environment.

Global Marketing & Management

Students in this course can study the importance of approaching marketing and analytics from a global perspective. Students can examine the interconnected nature of the global market and devise solutions to improve growth and find ways to best take advantage of this new environment. Course topics may include product development, global competition, and profitability.

Applied Optimization

This course covers the essential skill of optimization, which is also known as mathematical programming or prescriptive analytics. Students can expect to learn about logical modeling, data collection, and solution interpretation. They can also examine how these skills can be put to use when making marketing and analytical decisions.

Digital Marketing Challenges

This course introduces students to several of the most prominent challenges that await them in their careers as data scientists and marketing analysts. Common topics include how to interact with an informed and aware consumer, how to market to a public that is more unique than ever, and how to appeal to a non-committal or disinterested client. Students can tackle these problems by analyzing case studies and actively discussing scenarios in the classroom.

How to Apply to a Data Science and Business Analytics Dual Master's Program

If you're interested in applying to a dual master's degree program in data science and business analytics, you'll need to provide transcripts from your bachelor's degree. Schools do not always require a specific major but having relevant coursework and a strong GPA (3.0 or above) can set you apart from other applicants.

Some schools may require the Graduate Record Examination (GRE) or Graduate Management Admission Test (GMAT) to assess your quantitative and verbal skills. Students who earned their degree outside the U.S. may need to take the IELTS or TOEFL to provide evidence of their English language skills. Other provisions can include letters of recommendation, a personal statement, and a resume. While not always required, many applicants also have several years of relevant work experience.

Dual master's degree programs in data science and business analytics cover a wide range of useful topics that are essential knowledge for aspiring marketing and big data professionals. Through these programs, students can gain important skills related to managing and analyzing data and then using the it to make informed business decisions.

To continue researching, browse degree options below for course curriculum, prerequisites and financial aid information. Or, learn more about the subject by reading the related articles below:

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