Our 8-course, 32-credit online Master of Science in Business Analytics degree prepares you to become a data-driven thinker and develop or sharpen your ability to interpret complex data and guide your organization to making more informed and actionable decisions. Progress through your studies according to your schedule with as much or as little support as you need.
Key features of our Master of Science in Business Analytics include:
- Use the scientific method to plan and execute hypothesis driven analysis.
- Develop a strong background in research design structure and approach.
- Cover a range of statistical models, providing you flexibility to tackle both tactical and strategic business issues with many different data types.
- Acquire data mining skills to harness insights from large and complex data sets.
- Prepare yourself to leverage social media and other online sources to enhance marketing and business operations.
- Capstone project allows you to solve real world problems including projects from your employer.
Foundations of Data Management provide students exposure to fundamental data management skills used in modern information systems that support various operational and functional areas within a business organization. Topics covered in the course has an emphasis on how data is fundamentally identified, organized, described and managed as the most valued asset within an organization. Course emphasis is also on applied learning of concepts and skills for relational data modeling and querying. This course will help prospective Data Science and Analytic business professionals to develop and apply data management skills that will be essential to the success in subsequent coursework.
This course provides students with a foundation in statistical models, principles and theory to support the analysis of data. Topics addressed in this course include variables and their properties, measurement scales, probability and probability distributions, sampling distributions, the Central Limit Theorem, Bayes Theorem, methods of computing and summarizing measures of central tendency and variance, and univariate and bivariate estimation and hypothesis testing procedures. Students will learn and apply the R and Python programming languages to mine data, perform statistical analyses on data sets and prepare reports to address common data science and business research problems.
This course provides a comprehensive overview of empirical methods to address business research questions and hypotheses based on an organization’s business and technology environment and capabilities. Students will learn techniques to design and conduct empirical studies. The formulation of sound research questions and hypotheses will be explored, and experimental, quasi-experimental, qualitative and observational procedures will be applied to a variety of contemporary business issues. Data collection and sampling approaches will be covered.
This course covers contemporary parametric, non-parametric and Bayesian statistical procedures commonly used to advance business analytics. Statistical applications such as comparisons between batches; analysis of variance and covariance; linear, multiple and logistic regression; discriminant analysis; bivariate and canonical correlation, factor analysis, bootstrapping and other resampling methods; and decisions tree frameworks will be applied to a range of business situations. Evaluation of assumptions; data transformation; reliability of statistical measures; resampling methods; validation and interpretation of assumptions and statistics; and causation versus correlation will be covered. Fundamentals of machine learning related to static and dynamic statistical procedures will be addressed in the context of business research and analysis.
Very large-scale data sets, often called “big data” is becoming an invaluable asset to businesses but they require specific storage, organization, and processing tools and processes. The course covers the lifecycle of massive data analytics including the algorithms, techniques and tools needed to support big data processing. Commonly referred to as data mining, the course will explore the extraction of knowledge and patterns from large-scale data sets. The business applications that require data mining will be addressed.
This course focuses on the effective communication of data analysis and its insights and implications. Students will learn the principles and techniques for information visualization and representation as well as verbal and written communication. Students will develop proficiency in several of the latest tools for visualization. Students will use real-world business scenarios to gain experience designing and building data visualization and communication. Best practices will be highlighted and students will receive tailored individual coaching and feedback sessions to accelerate skill improvement.
This course will investigate the ethical and legal boundaries of data and the data privacy questions that emerge from developing, storing, analyzing and using data. Issues including intellectual property, data ownership, storage security and safeguards as well as the human impact of using data will be investigated. Case studies and scenario explorations in a range of industries from consumer products and retail to government and social justice will be used to illustrate and apply the issues, preparing students to manage these often grey issues during their career. The latest approaches, processes and frameworks for mitigating and managing data concerns and risks will be introduced.
Digital marketing including social media and web-based advertising have fundamentally transformed how goods and services are marketed and sold today. The strategic aspects, performance metrics and business value of social media and digital marketing analytics will be explored. Each student will develop a capstone project that will include collecting, analyzing and developing insights from social media and/or digital marketing data in an industry and business of interest to them. Faculty will work to pair students with partner companies to supply data and business challenges to use as the foundation of the capstone project. Students will be required to deliver their analysis and communicate their results to their partner company sponsor as well as their faculty and peers.
Recent Merrimack College Accolades
Talk to an Application Specialist today for more information about how the online Data Science and Analytics programs can help you achieve your professional goals at: 978-