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Data Science

Our 8 course, 32-credit online Master of Science in Data Science degree prepares you to harness the power of big data using the latest tools and analytical methods. Progress through your studies according to your schedule with as much or as little support as you need.

Key features of the Master of Science in Data Science include:

  • Curriculum based on real world application of skills based on the DAMA International DMBoK Framework.
  • Learn to apply all elements of the data process in the context of the data workflow of companies and organizations today.
  • Data management and data engineering are covered in depth, as well as advanced statistical modeling, algorithms, and machine learning.
  • Whether you are an expert or a novice programmer, the program is personalized to enhance your ability to use cutting edge tools across the data life-cycle.
  • Capstone project allows you to solve real world problems including projects from your employer.


Foundations of Data Management

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.  

Foundations of Statistical Analysis

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.  

Research Design, Structure and Decision Making

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.

Data Science Statistics

The goal of this course is to provide students with an introduction to many different types of quantitative research methods and statistical techniques for analyzing data.  Measurement, classification, clustering and causal inference will be explored and classical linear regression and the basic techniques of time series modeling will be introduced.  Inferential statistics including probability modeling and distributions will be covered.  Many different statistics and techniques for analyzing and viewing data will be introduced, with a focus on applying this knowledge to real-world data problems.

Machine Learning

This course introduces data science students to the foundations of statistical machine learning.  A subset of available technology tools and platforms for supporting machine learning will be introduced (e.g. including Python, R, Hadoop and Spark, SAS, SPSS, Rapid Miner, etc.). The goal of machine learning is to develop algorithms used to create predictive models. Topics will include unsupervised learning where algorithms will be developed using statistical methods to find structure or patterns in data.  Another key topic will be developing algorithms to predict outcomes in new situations (supervised learning) including a focus on applications with time-sequenced data.  The topic of extending machine learning algorithms to scale up and work on large data sets will also be addressed.

Data Visualization

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.

Data Ethics, Law and Privacy

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.

Capstone - Applied Data Science and Machine Learning

This capstone for data scientists builds on the key ideas in machine learning with a focus on practicum. Students will create an individual project in an industry and business issue of interest to them.  Through this, students will learn how to apply Data Science and Machine Learning techniques to a complex problem as well as evaluate and interpret results. Visual representation of the findings and a reflection on privacy or ethical issues associated with the investigation will help tie together key concepts from the program into this final project.

Recent Merrimack College Accolades

princeton-review bestregional-colleges-northmoney-bestcolleges2015_150forbes-topcolleges2015_150

Talk to an Application Specialist today for more information about how the online Data Science and Business Analytics programs can help you achieve your professional goals at: 978-208-4007

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