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Empowering you to transform data into insight to achieve business goals.

There’s a digital revolution taking place at this very moment. Harnessing the power of data, innovation, and collaboration, businesses are thinking bigger and progressing further. In a world where data is king, professionals skilled in data management and the practical application of technologies like machine learning are in high demand. Effectively analyzing big data helps businesses become better, smarter, and faster – which is why experts in data analysis are urgently required. Looking ahead, the need for data analytics experts will only surge higher and demand for analysts far exceeds the number of qualified professionals out there.

With courses focused on advanced analytics and data operations, you’ll learn how to translate data into a usable asset. Our Master’s of Science in Data Analytics will prepare you for a career in a technology-driven business environment — you’ll delve deep into Python programming, advanced statistical analysis, and data mining and warehousing. Clark will prepare you to lead your organization to better business decisions and outcomes with confidence and skill by giving you the tools to find the story behind vast amounts of data.

International Students

Candidates with science, technology, engineering, and mathematics (STEM) degrees are sought after across most major industries in the United States. When you graduate with a STEM-designated degree such as the Master of Science in Data Analytics, you may be eligible to remain in this country for up to 36 months on Optional Practical Training (OPT).

Why a Master’s in Data Analytics at Clark University

  • Flexible online format with access to a robust portfolio of electives, allowing you to construct an experience that drives your own individual career aspirations.
  • Distinctive practitioner-scholar instructor model brings pragmatic real-world expertise to life in the classroom.
  • Suited to fit the needs of the modern workforce, with a curriculum that fosters both technical and ‘soft’ skills, like storytelling with data.
  • Develops system architects to prepare data for advanced analytics.
  • Longstanding history of close collaboration with local and regional organizations, ensuring research, internship, and employment opportunities.
  • Highly ranked institution with robust experience in technically oriented programs and quantitative methods.
Apoorva Arbooj

One reason I chose Clark is that all the professors are also practitioners. They combine textbook learning and years of research with first-hand experience working in the industry. They helped me bridge the gap between academics and industry.

Apoorva Arbooj M.S. ’21 - Data Analytics

Integration Engineer at Luma Health

SPS Peer Advisor

Dean Cascione

The 4th Industrial Revolution is Upon Us

Dean Cascione dives into data to give students the skills needed to help businesses as they digitally transform through automation, artificial intelligence, machine learning, and rapid technological innovation.

Data Analytics provide enterprises with valuable business, operational, and security intelligence to uncover trends, expose anomalies, foster continuous improvement of business-critical systems, and ultimately gain a competitive advantage.

The Essentials

Program Overview

Designed for flexibility with your busy schedule, our program can be completed on a part-time basis either online or on campus. Students studying full-time can earn their degree in less than two years. Start dates for the program are in January and September.

Learning Outcomes and Competencies – School of Professional Studies

The following sections talk about the specific programmatic outcomes for each credential.  For programs at the Masters level, a core of five core operational competencies informs our theoretical framework for all Clark University School of Professional Studies Master’s degrees. Graduate certificates are not held to the same holistic standard as they are considered to be narrower in focus and more applied in practice.  For the credentials at the Masters level, those competencies are:

Core Competencies for SPS Master’s Degrees
Organizational Systems OR Foundational Elements for STEM Programs Developing an appreciation and understanding of the interdependence of the parts of a system will result in effectively and efficiently assisting an organization by developing its strategy and delivering its intended mission.  For STEM credentials, a solid foundation in analytical and diagnostic competencies which will enable the student to succeed from a technological perspective.
Ethics and Social Responsibility


The SPS curriculum will stress the importance of ethics and corporate social responsibility, so all SPS students are aware of the advantages of ethical behavior in business and professional life, and can act from a moral point of view. The notions of ethics and social responsibility are extended to STEM programming through the lens of the issue of data and programming integrity that can inform systems and analytical architecture that is applied in a fair and equitable manner.
Applied Research As a professional, SPS graduates will have the ability to call upon research methodologies to solve practical problems organizations and individuals encounter.  Our professional focus demands that informed research is a core value to knowledgeable problem-solving.
Workplace Dynamics, Communication, and Career Management OR Core Technologies Necessary to Meet STEM Industry Standards Workplace dynamics involve the relationships among the members of an organization, including departmental and interpersonal relationships. The capacity to communicate effectively is an essential skill for the successful professional. Career is an integral component of a professional’s life, and career can be maximized by an awareness of opportunities available consistent with individual talents.  For STEM-based programs, core technological applications and industry standards will be presented to form a foundation of programming and problem-solving competencies for a successful workplace experience.
Theoretical Grounding Each SPS degree is part of a field of study based upon a collection of theories that have proven to be effective when applied to challenges. Students will develop an appreciation for how arguments are used to explain, predict, and understand phenomena.


MS in Data Analytics

Operational Competency Learning Outcomes
Foundational Elements for STEM Programs – Linear Regression and Time Series AND Mathematical Statistics
  • Simple and multiple regressions, serial correlation, and heteroscedasticity.
  • Analysis of residuals and stepwise analysis techniques.
  • Time series analysis including smoothing and extrapolation of time series.
  • Linear time series models, model building procedure, and forecasting, as well as case studies. Teach in R as appropriate.
  • Interval estimation.
  • Point estimation including sufficiency, Rao-Blackwell theorem, completeness, uniqueness, Rao-Cramer inequality, and maximum likelihood estimation.
  • Tests of hypothesis: uniformly most powerful tests, uniformly most powerful unbiased tests, likelihood ratio test, chi-squared test, comparison of means and variances. ANOVA, regression, and some nonparametric tests.
Ethics and Social Responsibility – MSIT3860 Data Management for Information Technologies Planning

  • Synthesize and communicate the data needs for BI systems.  Design and communicate data projects’ goals and plans for integrity and organizational goals and mission.
  • Define and communicate the roles and responsibilities of BI data users and administrators for organizational good.


  • Design and communicate data management system administration, and security procedures and policies in compliance with laws and organizational ethics.
  • Design and communicate procedures for data backup and recovery, upgrade, and migration to uphold data integrity and privacy concerns.


  • Describe and compare and contrast database architectures (relational, hierarchical, star, cube, etc.) and data warehousing approaches (Kimball’s vs. Inmon’s).
  • Design and communicate data models for initiatives.
  • Design and communicate procedures for data integration.
  • Describe components of data storage solutions.
Core Technologies Necessary to Meet STEM Industry Standards – MSIT3090 Python Programming AND MSIT3350 Data Mining with Splunk
  • Learn to apply the terminology, methods, and skills associated with the application of Python in Data Analytics.
  • Review and become proficient in basic Python functionality such as control structures and methods, classes, arrays, and strings.
  • Be able to apply more advanced techniques like inheritance and polymorphism, creating user interfaces, and exceptions and streams.
  • Be able to write Python scripts to perform a number of data acquisition, translation, and analysis tasks. Gain experience importing various open-source modules to visualize data sets. Students will be able to discuss the importance of mining machine data in real time.
  • Students will be able to discuss the difference between Business Intelligence and Operational Intelligence.
  • Students will be able to discuss and explain the 5 dimensions of Big Data.
  • Students will be able to discuss descriptive, predictive, and prescriptive analytics.
  • Students will be able to install and configure the latest version of Splunk.
  • Students will be able to use Splunk to gather, analyze, and report data.
  • Students will be able to create Splunk dashboards and visualizations that make data meaningful.
  • Students will be able to develop Splunk Apps.
Applied Research – MSIT3999 Capstone Knowledge: Demonstrate competency in the area of data analytics through the application of the essential elements acquired from core and elective courses. Capability to construct and implement solutions to solve real world issues.

Skills: Demonstrate critical thinking; specifically employ appropriate analytical models and apply critical reasoning processes to evaluate evidence, select among alternatives, and generate creative options in furtherance of effective decision making.

Professional Behavior:  Demonstrate teamwork and leadership skills, specifically function in a variety of work groups using appropriate leadership skills and styles.

Communication: Demonstrate effective communication skills when working with stakeholders and diverse constituents. Evidence of ability to write capstone required documents clearly, concisely, and analytically for various stakeholders.  Ability to confidently present in public setting with appropriate use of visual aids.

Theoretical Grounding – Machine Learning
  • Developing a basic understanding of the fundamental concepts, terminology, and theory of machine learning.
  • Learning to use some common tools for machine learning.
  • Learning the methodologies of machine learning, including:
    • Data gathering, preparation, and archiving
    • Choosing specific techniques and algorithms
    • Model training and hyper parameter tuning
    • Model evaluation
    • Visualization

Incoming students with a strong math or programming background (i.e., candidates holding a B.S. in Computer Science) may waive up to 2 required courses and replace them with two other course options.

We also recognize the valuable experience and perspectives that working professionals bring to the class. If you are a student with three or more years in a professional position or hold an industry-standard certification, you can apply for the Assessment of Prior Learning (APL) credit.

You may be awarded APL credit for up to two (2) graduate courses, enabling you to complete your degree more quickly and cost effectively. (An administrative fee is applied if the APL credit is approved).

  • Driving business growth through the use of data and advanced analytics
  • Leading strategic projects to create opportunity for data-driven decision-making
  • Investigating and diagnosing complex data interaction issues
  • Data warehouse design and optimization
  • Architect analytics solutions to find hidden opportunities and gain competitive advantage
  • Applied Machine Learning
  • Data Visualization
  • Data Mining
  • Data Warehouse and Applied SQL


10 course units

  • 8 core courses
  • 2 electives

Course Catalog

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