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Be Competitive with a Graduate Certificate in Data Science

The online graduate certificates in Data Analytics Foundations, Data Science, and Business Analytics will position you to develop and deliver game-changing, career-elevating insights and strategies in as few as 24 weeks.

Learn practical applications from leading practitioners who teach from a curriculum created in cooperation with the industry’s top employers. And you can start with a 12-credit certificate and apply the credits towards the 32-credit Master of Science in Data Science or Business Analytics program.

  • 100% Online

  • Complete in as little as 24 weeks

  • Apply credits towards a Master’s Degree

3 Graduate Certificate Options

All three courses in each certificate program are offered fully online and offer multiple start dates. For beginner students, we recommend the Data Analytics Foundations Certificate. For advanced professionals, these students can request to begin with the Data Science Certificate or Business Analytics Certificate. Courses can be applied to a master’s degree if they wish to pursue a Master’s Degree in Data Science or a Master’s Degree in Business Analytics.

  • Data Analytics Foundations Certificate

  • Data Science Certificate

  • Business Analytics Certificate

LEARN MORE – EARN MORE

Data Science and Business Analytics job growth is robust and data-driven professionals with advanced skill-sets and credentials are in high demand. Potential career paths and earnings, include:

$0
Data Scientist
$0
Sr Business Analyst
$0
Research Scientist

Average Annual Salary Source: PayScale.com, Obtained May 2017

Choose Your Path to Success

Data Analytics Foundations Certificate

Foundations of Data Management provides 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 emphasize how data are identified, organized, processed and managed, in a manner warranting being considered one of the most valuable organizational assets. The course emphasizes a combination of theoretical ‘why’ and experiential ‘how’ within the confines of relational data modeling and querying. It is intended to help students develop fundamental data management skills that are essential to succeeding in subsequent courses.

This course provides students with foundational knowledge of descriptive and inferential statistics. The scope of this course is limited to univariate and bivariate statistics that are commonly used to conduct basic analyses of data, including reading-in data, reviewing individual variables and their properties, conducting any requisite data corrections and enhancements, developing an overall descriptive baseline, and taking initial steps of discovering and testing possible relationships. Also included in the course scope is an introductory overview of the basics of probability and sampling theories. Topic-wise, students will develop an understanding of numerous core statistical notions, including variable types and the underlying measurement scales, variable distributions, sampling distributions, the Central Limit Theorem, Bayes Theorem, measures of central tendency and variance, statistical significance, hypothesis testing, tests of difference, as well as correlation and cross-tabulation.

This course equips students with foundational concepts and techniques required for telling compelling stories with large complex data sets, using static and interactive graphics to depict outcomes, networks, time and maps. The importance of visualizing information for many analysts is often overlooked or downgraded, but if the visualization is ineffective, the decision-making processes and knowledge discovery will be compromised. This is a project-based course that begins with reviewing concepts of human perception and cognition and perceptual accuracy and preferences, followed by exploration of the basics of graphic design and making a “good” graph. Students explore why some data visualizations present information effectively and others do not, all while learning to appreciate visualization as a key component of Data Scientists’ and Business Analysts’ skill set.

Data Science Certificate

This course introduces students to the fundamentals of statistical learning, framed within the confines of exploratory data analyses. Students will learn common strategies and techniques used in extracting valid and reliable insights out of available data, all framed within the combination of theoretical ‘why’ and experiential ‘how’ competencies. Building on the foundation acquired in Foundations of Data Management and Foundations of Statistical Analysis courses, the Data Exploration course immerses students in basics of statistical significance testing and story-telling focused on identification of meaningful data patterns and associations. Prerequisite: Grade of B or higher in DSA5020, Foundations of Statistical Analysis.

This course offers students an introduction to the basic concepts and applications of statistical learning models. The course is designed to immerse students in statistical prediction related multivariate statistical concepts, including an in-depth discussion of commonly used types of multivariate predictive models, all focused on strengthening students’ comprehension of core statistical notions, including regression and classification, non-linear modeling, and tree-based estimation. The course focuses on hands-on applications of model fitting and evaluation using programming applications in R or Python.

This course offers students and introduction to the fundamentals conceptual, operational and experiential aspects of machine learning, or broadly defined algorithmic capability to manipulate, process, amend & analyze data using appropriate applications. This is an introductory course, designed to endow students with the foundational theoretical and experiential knowledge of automated pattern detection approaches focused on four key outcomes of categorization, prediction, identification and detection, and further framed within the confines of supervised and unsupervised learning. The course is meant to offer an overview of this highly complex and rapidly evolving field; as such, it focuses on established approaches, key developmental trends, and hands-on applications of select techniques.

Business Analytics Certificate

This course introduces students to the fundamentals of statistical learning, framed within the confines of exploratory data analyses. Students will learn common strategies and techniques used in extracting valid and reliable insights out of available data, all framed within the combination of theoretical ‘why’ and experiential ‘how’ competencies. Building on the foundation acquired in Foundations of Data Management and Foundations of Statistical Analysis courses, the Data Exploration course immerses students in basics of statistical significance testing and story-telling focused on identification of meaningful data patterns and associations. Prerequisite: Grade of B or higher in DSA5020, Foundations of Statistical Analysis.

The goal of this course is to introduce students to the dual role and the dual benefit of dependence-focused multivariate statistical modeling techniques, commonly used in estimating the likelihood of outcomes of interest, and delineation of the key drivers of those outcomes. The former, often referred to as ‘predictive analytics,’ is focused on making forward-looking predictions, while the latter, commonly referred to as ‘prescriptive analytics,’ is geared at delineating and describing factors contributing to those predictions. The course is focused on developing rudimentary understanding of those two broad data analytical dimensions, and on introducing students to some of the more widely used predictive and prescriptive statistical techniques, including linear and logistic regression models, decision trees, as well as some of the more recent, machine learning focused techniques such as random forest.

This course introduces students to the fundamental conceptual, operational and experiential aspects of automated data mining approaches and techniques, focusing primarily on the use of pattern recognition algorithms to address common business problems involving classification and prediction. In this course students develop basic theoretical and experiential knowledge of supervised and unsupervised data mining approaches, framed in the context of the common data mining related challenges including dimensionality, regularization, overfitting and generalization. Various pattern and association rules and pattern discovery modes are also addressed, including compressed, sequential and spatial patterns, as well as multi-level and multi-dimensional associations. Lastly, common data mining applications, including clustering and prediction, are discussed and illustrated.

Admission Requirements

  • Application (no application fee)

  • Undergraduate transcript from an accredited college/university

  • Résumé demonstrating relevant work experience

  • Data Analytics Foundations Certificate: Prerequisite course – college-level statistics or completion of statistics module

  • Data Science & Business Analytics Certificates: Foundations Certificate or equivalent academic/work experience

Frequently Asked Questions

A: There are six convenient start dates offered per year for the online graduate certificate in Data Science, Business Analytics, or Foundations. Contact a Merrimack online Application Specialist at 978-206-6708 to find the next start date that works with your schedule.

A: For beginner students, we recommend the Data Analytics Foundations Certificate. For advanced professionals, these students can request to begin with the Data Science Certificate or Business Analytics Certificate.

A: Each four-credit course is eight weeks in length. There are two, 8-week modules offered in the fall, spring, and summer semesters. This schedule allows students to begin the program at six different times during the year.

A: The graduate certificate program typically takes 24 weeks to complete.

A: No, there is no application fee.

A: The tuition for each certificate program is $952 per credit (or $11,424 in total) plus a $77 per course fee. Scholarships and tuition discounts (based on partnerships with your employer) may help offset the cost of the graduate certificate program. Please contact your Application Specialist today at 978-206-6708.

A: A comprehensive fee of $77 per course helps to support the cost of providing the online courses, online library access, academic support and career services, all of which can be accessed virtually. Students pay the comprehensive fee when they take one or more classes during a module. On-campus services are available upon request.

Discover the Merrimack Difference

100% ONLINE

Designed for working adults, the combination of self-paced learning with live, online and instructor-led sessions ensures you get the professor interaction you need in a flexible format.

EMPLOYER ALIGNED

This degree was developed with industry leaders to ensure the curriculum includes the employable skills that today’s hiring managers are looking for in candidates for top positions.

PRACTICAL APPLICATION

Faculty members are leading practitioners in the data science and analytics fields, bringing their experience to the curriculum with applicable projects and assignments.

TOP-RANKED

Recognized by U.S. News & World Report and the Princeton Review, Merrimack College ranks among the top colleges and universities in the country.

MERRIMACK COLLEGE ACCOLADES

At Merrimack College, we're proud of our long history of providing quality degrees to students entering the job market. Our faculty are more than just teachers. We are committed to helping you grow — academically, personally and spiritually — so that you may graduate as a confident, well-prepared citizen of the world.

  • Money magazine 2019 Most Transformative College
  • U.S. News & World Report 2020 #46 Best Regional Universities/North
  • U.S. News & World Report 2020 Top 10 Most Innovative School Regional Universities/North
  • The Princeton Review 2020 Best Regional College
  • Transitioning Athletics to Division 1 and joining the Northeast Conference in 2019-20

DATA-DRIVEN Leaders

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