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INDUSTRY ALIGNED ONLINE MASTERS DEGREES & CERTIFICATES
- Accounting
- Data Science
- Business Analytics
- Healthcare Analytics
- Management
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INDUSTRY-ALIGNED • PERSONALIZED APPROACH • 100% ONLINE
Powerful Credentials for Career Re-Entry and Transition
To ensure that our graduates stand out among peers when seeking promotions or new positions, we consulted industry leaders in designing master’s degree and certificate programs that incorporate a powerful combination of business, analytical, and strategic management skills.
Merrimack’s more than 70-year old tradition of academic excellence is represented through our advanced degrees designed for working professionals and offered 100% online. The competencies in our programs are built around current industry needs and trends so that you can be sure you are acquiring the skills and knowledge that go beyond today’s job requirements to future-proof your career. You’ll also receive a 10% tuition discount.
DATA SCIENCE AND BUSINESS ANALYTICS FOUNDATIONS CERTIFICATE
Foundations of Data Management course 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 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 basic statistical analysis, focusing primarily on descriptive univariate statistics. Topics addressed in this course include variables and their properties, measurement scales, descriptive analyses of continuous and categorical variables, Central Limit Theorem, univariate and bivariate estimation, and hypothesis testing logic and procedures. Students will be exposed to hands-on computational examples using R and SPSS as they learn how to apply the various statistical concepts covered in this course to real-life business situations.
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 SCIENCE CERTIFICATE
This course offers students a hands-on introduction to the basic concepts, practices and applications of developing forward-looking – i.e., predictive – statistical models. Content-wise, the class begins with a conceptual overview of multivariate statistics, followed by a discussion of commonly used types of multivariate predictive models. The second part of the course focuses on hands-on applications of model fitting and evaluation using scripring (R and/or SPSS Statistics) and GUI/object-based (SPSS Modeler) applications.
This course introduces data science students to the foundations of statistical machine learning, which are automated methods of broadly scoped – i.e., descriptive, predictive, explanatory – data analyses. The first part of the course offers a conceptual overview of established and emerging machine learning algorithms, followed, in the second part, by a discussion and hands-on introduction to some of the more widely used machine learning platforms (e.g. R, SAS Enterprise Miner, SPSS Modeler). Some of the specific topics include unsupervised and supervised learning, text mining, data preparation and result interpretation.
This course introduces students to the fundamentals of exploratory data analyses, broadly defined here as review of the available/focal data, and extraction of descriptive characteristics with the goal of generating valid and reliable insights. The course covers the basic data due diligence and curation considerations, key data preparatory steps, and offers an overview of a general descriptive data analytical framework. Analytic approach-wise, the course addresses analyst-led exploration utilizing classical statistical techniques, as well as automated data mining applications, while addressing topics of statistical inference, statistical significance, and outcome validity and reliability. Lastly, the course combines conceptual overview of the focal concepts and statistical reasoning, while also providing hands-on introduction to the practical side of data exploration. The general instructional approach used in this course is one that casts exploratory data analyses in the context of the data –> information –> knowledge continuum that underpins extraction of decision-guiding insights out of the available data as a way of answering business questions.
BUSINESS ANALYTICS CERTIFICATE
This course will offer Business Analytics students with a broad base introduction to multivariate statistical methods, with particular emphasis on explanatory and predictive techniques. The commonly used dependence and interdepence techniques are discussed, including linear and logistic regression, decision trees and select machine learning approaches. The course is focused particularly on topics relating to technique selection, result interpretation and translation of analytic outcome into decision-guiding insights.
As data analytic technologies became more advanced, it became progressively easier and easier to execute sophisticated analyses; also, as the volume of the available data exploded, more and more analyses began making use of very large sample sizes. Those trends have a direct impact on the validity and reliability of outcomes of statistical analyses, the investigation of which is the focus of this course. More specifically, in this course the students will take a closer look at topics such as the impact of sample size on statistical significance, and the relationship between practical materiality of findings and the more abstractly framed statistical significance.
This course introduces students to the fundamentals of exploratory data analyses, broadly defined here as review of the available/focal data, and extraction of descriptive characteristics with the goal of generating valid and reliable insights. The course covers the basic data due diligence and curation considerations, key data preparatory steps, and offers an overview of a general descriptive data analytical framework. Analytic approach-wise, the course addresses analyst-led exploration utilizing classical statistical techniques, as well as automated data mining applications, while addressing topics of statistical inference, statistical significance, and outcome validity and reliability. Lastly, the course combines conceptual overview of the focal concepts and statistical reasoning, while also providing hands-on introduction to the practical side of data exploration. The general instructional approach used in this course is one that casts exploratory data analyses in the context of the data –> information –> knowledge continuum that underpins extraction of decision-guiding insights out of the available data as a way of answering business questions.
Three Easy Ways to Earn Your MSA
If you already have an accounting undergraduate background, this 32 credit curriculum is for you. With this option you can focus on higher level accounting courses, complemented by a series of specialized electives in Business Analytics, Fraud and Forensics, or Taxation. These areas of concentration are considered critical in the Accounting industry, making you more marketable in the profession in the long term. The degree can be completed in a little as 16 months, and meets the additional credit requirements for CPA licensure.
Fill out the form above to learn more or to schedule a call with the Assistant Dean of Graduate Programs, Annarita Meeker, to personalize your degree completion plan!
Note: Our program meets the CPA licensing requirements in Massachusetts. All states have different licensing requirements and additional courses might need to be integrated into the curriculum based on the state board you are trying to license with. For specific CPA requirements by state, visit the NASBA website at www.nasba.org.
If you hold an undergraduate business degree in a discipline other than accounting, and want to develop expertise in competencies related to accounting, this 32 credit path is for you. This degree option integrates all of the core accounting courses required to gain a strong foundational background in accounting and prepares you for CPA licensure. No prerequisites are required.
Depending on your current and future career goals, you have the option to integrate electives toward a concentration in Business Analytics, Fraud and Forensics, or Taxation. These areas are considered essential in the accounting industry and will make your newly acquired skill sets more marketable in the future. If completing a concentration is one of your goals, additional elective courses may need to be added to your overall curriculum. This degree option can be completed in as little as 16 months.
Fill out the form above to learn more or to schedule a call with the Assistant Dean of Graduate Programs, Annarita Meeker, to personalize your degree completion plan!
Note: Our program meets the CPA licensing requirements in Massachusetts. All states have different licensing requirements and additional courses might need to be integrated into the curriculum based on the state board you are trying to license with. For specific CPA requirements by state, visit the NASBA website at www.nasba.org.
That’s OK! Merrimack offers you an efficient and simple path to MSA and CPA without having to take any additional prerequisite courses. This flexible, 38 credit curriculum integrates all of the foundational accounting and business courses you need to sit for the CPA examination and qualify for licensure. This degree option can be completed in less than 24 months.
Fill out the form above to learn more or to schedule a call with the Assistant Dean of Graduate Programs, Annarita Meeker, to personalize your degree completion plan!
Note: Our program meets the CPA licensing requirements in Massachusetts. All states have different licensing requirements and additional courses might need to be integrated into the curriculum based on the state board you are trying to license with. For specific CPA requirements by state, visit the NASBA website at www.nasba.org.
CORE REQUIREMENTS (32 CREDITS)
Foundations of Data Management course 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 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 basic statistical analysis, focusing primarily on descriptive univariate statistics. Topics addressed in this course include variables and their properties, measurement scales, descriptive analyses of continuous and categorical variables, Central Limit Theorem, univariate and bivariate estimation, and hypothesis testing logic and procedures. Students will be exposed to hands-on computational examples using R and SPSS as they learn how to apply the various statistical concepts covered in this course to real-life business situations.
This course introduces students to the fundamentals of exploratory data analyses, broadly defined here as review of the available/focal data, and extraction of descriptive characteristics with the goal of generating valid and reliable insights. The course covers the basic data due diligence and curation considerations, key data preparatory steps, and offers an overview of a general descriptive data analytical framework. Analytic approach-wise, the course addresses analyst-led exploration utilizing classical statistical techniques, as well as automated data mining applications, while addressing topics of statistical inference, statistical significance, and outcome validity and reliability. Lastly, the course combines conceptual overview of the focal concepts and statistical reasoning, while also providing hands-on introduction to the practical side of data exploration. The general instructional approach used in this course is one that casts exploratory data analyses in the context of the data –> information –> knowledge continuum that underpins extraction of decision-guiding insights out of the available data as a way of answering business questions.
This course offers students a hands-on introduction to the basic concepts, practices and applications of developing forward-looking – i.e., predictive – statistical models. Content-wise, the class begins with a conceptual overview of multivariate statistics, followed by a discussion of commonly used types of multivariate predictive models. The second part of the course focuses on hands-on applications of model fitting and evaluation using scripring (R and/or SPSS Statistics) and GUI/object-based (SPSS Modeler) applications.
This course introduces data science students to the foundations of statistical machine learning, which are automated methods of broadly scoped – i.e., descriptive, predictive, explanatory – data analyses. The first part of the course offers a conceptual overview of established and emerging machine learning algorithms, followed, in the second part, by a discussion and hands-on introduction to some of the more widely used machine learning platforms (e.g. R, SAS Enterprise Miner, SPSS Modeler). Some of the specific topics include unsupervised and supervised learning, text mining, data preparation and result interpretation.
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 introduces students to the current and emerging topics and considerations addressing the issue of data governance, usage and security; furthermore, this course also investigates the ethical and legal right and responsibilities of data analysts, and delves into 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 are investigated; case studies and scenario explorations in a range of industries from consumer products and retail to government and social justice are used to illustrate and apply the concepts discussed in class, with the goal of preparing students to manage these often grey issues during their career.
The final course in the Data Science curriculum, the Capstone is a practicum focused on an analytic skill set that is an essential part of a data scientist skillset: multi-source analytics. Unlike the earlier courses which combined theory and practice, this course is heavily skewed toward the latter, giving students the opportunity to focus the bulk of the 8 week course period on fine-tuning their hands-on computational programming and related skills.
CORE REQUIREMENTS (32 CREDITS)
Foundations of Data Management course 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 have 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 basic statistical analysis, focusing primarily on descriptive univariate statistics. Topics addressed in this course include variables and their properties, measurement scales, descriptive analyses of continuous and categorical variables, Central Limit Theorem, univariate and bivariate estimation, and hypothesis testing logic and procedures. Students will be exposed to hands-on computational examples using R and SPSS as they learn how to apply the various statistical concepts covered in this course to real-life business situations.
This course introduces students to the fundamentals of exploratory data analyses, broadly defined here as review of the available/focal data, and extraction of descriptive characteristics with the goal of generating valid and reliable insights. The course covers the basic data due diligence and curation considerations, key data preparatory steps, and offers an overview of a general descriptive data analytical framework. Analytic approach-wise, the course addresses analyst-led exploration utilizing classical statistical techniques, as well as automated data mining applications, while addressing topics of statistical inference, statistical significance, and outcome validity and reliability. Lastly, the course combines conceptual overview of the focal concepts and statistical reasoning, while also providing hands-on introduction to the practical side of data exploration. The general instructional approach used in this course is one that casts exploratory data analyses in the context of the data –> information –> knowledge continuum that underpins extraction of decision-guiding insights out of the available data as a way of answering business questions.
This course will offer Business Analytics students with a broad base introduction to multivariate statistical methods, with particular emphasis on explanatory and predictive techniques. The commonly used dependence and interdependence techniques are discussed, including linear and logistic regression, decision trees and select machine learning approaches. The course is focused particularly on topics relating to technique selection, result interpretation and translation of analytic outcome into decision-guiding insights.
As data analytic technologies became more advanced, it became progressively easier and easier to execute sophisticated analyses; also, as the volume of the available data exploded, more and more analyses began making use of very large sample sizes. Those trends have a direct impact on the validity and reliability of outcomes of statistical analyses, the investigation of which is the focus of this course. More specifically, in this course the students will take a closer look at topics such as the impact of sample size on statistical significance, and the relationship between practical materiality of findings and the more abstractly framed statistical significance.
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 introduces students to the current and emerging topics and considerations addressing the issue of data governance, usage and security; furthermore, this course also investigates the ethical and legal right and responsibilities of data analysts, and delves into 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 are investigated; case studies and scenario explorations in a range of industries from consumer products and retail to government and social justice are used to illustrate and apply the concepts discussed in class, with the goal of preparing students to manage these often grey issues during their career.
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.
CORE REQUIREMENTS (32 CREDITS)
Foundations of Data Management course 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 have 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 basic statistical analysis, focusing primarily on descriptive univariate statistics. Topics addressed in this course include variables and their properties, measurement scales, descriptive analyses of continuous and categorical variables, Central Limit Theorem, univariate and bivariate estimation, and hypothesis testing logic and procedures. Students will be exposed to hands-on computational examples using R and SPSS as they learn how to apply the various statistical concepts covered in this course to real-life business situations.
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.
As data analytic technologies became more advanced, it became progressively easier and easier to execute sophisticated analyses; also, as the volume of the available data exploded, more and more analyses began making use of very large sample sizes. Those trends have a direct impact on the validity and reliability of outcomes of statistical analyses, the investigation of which is the focus of this course. More specifically, in this course the students will take a closer look at topics such as the impact of sample size on statistical significance, and the relationship between practical materiality of findings and the more abstractly framed statistical significance.
This course will offer Business Analytics students with a broad base introduction to multivariate statistical methods, with particular emphasis on explanatory and predictive techniques. The commonly used dependence and interdependence techniques are discussed, including linear and logistic regression, decision trees and select machine learning approaches. The course is focused particularly on topics relating to technique selection, result interpretation and translation of analytic outcome into decision-guiding insights.
Introduces the key concepts, common data collection approaches, sources and methods of epidemiology and population-based health research. For all methods, issues associated with healthcare data quality control, validity and reliability will be covered. Medical insurance, vital records, epidemiological research data and other major sources of public health data will be discussed. This course will introduce students to common and newly emerging data collection sources and methods used in epidemiological research. It will also introduce students to the challenges corporate America is undergoing due to the increase in the prevalence of non-communicable diseases in the workplace, and what population health strategy solutions are implemented to mitigate cost and facilitate optimal population health (for self-insured and fully insured companies). The course will also prepare students to manage the collection and storage of biological specimens, understand the fundamental concepts and methods of biostatistics as applied predominantly to clinical research and public health analyses. Clinical trials will also be addressed.
This course will introduce the current structure and emerging trends shaping the US Healthcare System. Students will learn the complexities of the American healthcare system and the reasons it became so confusing and cumbersome. In addition, the foundation of healthcare data sources for the 3Ps (Providers, Patients, and Payers), the triple aim (Cost, access, and quality) and fundamentals of Health Information Technology (electronic health records, health information exchanges, clinical decision support, and the influence of big data and predictive analytics). Students will also learn how healthcare performance is measured according to existing quality frameworks such as National Quality Forum (NQF), Healthcare Effectiveness Data and Information Set (HEDIS), and the Agency for Healthcare Research and Quality’s (AHRQ)
Students will develop a capstone project that will include collecting, analyzing and developing insights from healthcare data in an aspect of healthcare that is of interest to them. Faculty will work to pair students with partner companies or institutions to supply data and business challenges to use as the foundation of the capstone project or students can develop a research plan individually with their employer or another business, organization or institution. The Capstone experience will also provide to students critical skills needed to succeed in the healthcare analytics workforce today and to ensure they succeed and are valuable in the workforce today and in the future, as the healthcare business evolves, such as understanding of optimal project management skills, process and performance improvement techniques (Lean), and leadership skills.
Merrimack’s Master of Science in Management (MSM) prepares non-business majors, business majors, recent graduates, and new professionals for careers in leadership and management. Our MSM program emphasizes creative problem-solving skills, integrity and responsibility, all with the global perspective essential for personal and professional success in today’s rapidly changing business environment. Students will benefit from:
- 100% online courses
- Flexible program
- 16 month completion time
- No GRE or GMAT required
Students in the MS in Management program can choose from five concentrations to focus on specific business skills: Organizational Leadership, Business Analytics, Marketing Management, Strategic Human Resource Management, and Quantitative and Digital Finance.
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.
- Top 50 Best Value Online Big Data Programs 2018 by ValueColleges.com
- 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
