Developing Tomorrow’s Leaders
at MaHIMA
IN-DEMAND ONLINE MASTERS DEGREES & CERTIFICATES
Accounting
- Business Analytics & Intelligence
- Product Management
- Management
- Computer Science
- Data Science
Merrimack College is Proud to Partner with…
$75
Per Credit Employee Discount
Yes, tell me more about master’s degrees & graduate certificates from Merrimack!
INDUSTRY-ALIGNED • PERSONALIZED APPROACH • 100% ONLINE
Powerful Online Credentials for the Working Professional
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. The tuition discount you receive thanks to your employer’s partnership with Merrimack College means you can spend more time focusing on your degree, and less time thinking about tuition costs.
Online MS in Accounting
Online MS in Data Science
Online MS in Business Analytics
Online MS in Healthcare Analytics
Online MS in Management
Online Graduate Certificates
Online MS in Accounting
Build your skills in accounting, data interpretation and strategic management and fast-track your credentials and career opportunities in this high-paying, growing career field.
32-38 Credits | No GMAT Required
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.
Online MS in Data Science
8 Courses | 32 Credits | Finish Between 12 to 16 Months
CORE REQUIREMENTS (32 CREDITS)
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 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.
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.
This course introduces students to the fundamental concepts surrounding legal rights and responsibilities associated with data capture, storage and leveraging data for decision-making. Given the very diverse mix of topics falling under this broad umbrella, the aim of the course is to provide a general overview of the applicable aspects of the US regulatory and legislative framework, and then to offer more topically-focused overview of the key notions falling within the domains of data-capture related rights and responsibilities, data governance design and management, data security and privacy, information quality, and the ethical aspects of data access, usage, and sharing.
This course is the culmination of students’ learning – its intent is to offer students opportunities to apply the knowledge acquired in the program, in a directed, hands-on setting. As such, the course is a practicum built around solving applied industry data analytical problems, using available raw data. While substantial data processing, preparation, and outcome estimation guidance is offered throughout the course, the course does not encompass formal lectures, pre-determined assignments or examinations; instead, student’s performance in the Capstone course is based solely on successful completion of an assigned, student-specific project.
Online MS in Business Analytics
Stand out with the desperately sought-after combination of analytical skills and business acumen needed to solve critical challenges and capitalize on big opportunities.
8 Courses | 32 Credits | Finish Between 12 to 16 Months
CORE REQUIREMENTS (32 CREDITS)
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 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.
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.
This course introduces students to the fundamental concepts surrounding legal rights and responsibilities associated with data capture, storage and leveraging data for decision-making. Given the very diverse mix of topics falling under this broad umbrella, the aim of the course is to provide a general overview of the applicable aspects of the US regulatory and legislative framework, and then to offer more topically-focused overview of the key notions falling within the domains of data-capture related rights and responsibilities, data governance design and management, data security and privacy, information quality, and the ethical aspects of data access, usage, and sharing.
This course is the culmination of students’ learning – its intent is to offer students opportunities to apply the knowledge acquired in the program, in a directed, hands-on setting. As such, the course is a practicum built around solving applied industry data analytical problems, using available raw data. While substantial data processing, preparation, and outcome estimation guidance is offered throughout the course, the course does not encompass formal lectures, pre-determined assignments or examinations; instead, student’s performance in the Capstone course is based solely on successful completion of an assigned, student-specific project.
Online MS in Healthcare Analytics
Prepare for a thriving career translating data into actionable insights for healthcare professionals and stakeholders in one of the fastest growing industries.
8 Courses | 32 Credits | Finish in as little as 7 Months
CORE REQUIREMENTS (32 CREDITS)
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.
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.
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.
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 introduces students to the US healthcare, a dynamic and complex system in which data analytics plays an increasingly greater role, influencing the ever-greater array of its many aspects. Students will develop a foundational knowledge of the mechanics of the US healthcare system, and will gain insights into how data-driven decisions influence clinical and business outcomes, with particular emphasis on value-based metric framework commonly used to manage outcomes. In a more operational sense, students will be introduced to roles and responsibilities of public, private and regulatory healthcare professionals in the context of their organizational roles, capabilities, and manners in which they make use of diverse sources and types of healthcare-related data.
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.
Online Graduate Certificates
3 Courses | Finish in About 24 Weeks | No GMAT Required
DATA SCIENCE AND BUSINESS 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 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.
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.
BUSINESS ANALYTICS CERTIFICATES
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.
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.
PRODUCT MANAGEMENT: LIFE SCIENCES FOUNDATIONS CERTIFICATE
This course introduces students to the range of activities along the new product development pathway, including ideation and concept development, concept refinement, detailed product design and engineering, design for manufacturability, design for usability, prototyping, scale-up and launch. It introduces multiple tools (used in different industries) to manage product development activities, including phase gate systems, agile development for software, lean development for products, etc. It also introduces scheduling, budgeting and monitoring tools to project and measure new product programs. (4 credits)
This course introduces students to the various skills a product manager must deploy for product success including product definition, positioning, pricing, placement and promotion. It also introduces the relationship skills needed to manage cross-functional interactions and indirect and virtual teams. The course also includes an overview of the financial tools used to guide product realization and financial metrics used to measure product success. (4 credits)
Innovation is at the very core of life sciences, which are based heavily on cutting edge science and technology. At the same time innovation is very challenging in life sciences settings, due to the heavily regulated environment. This course introduces students to the tools and techniques used in life sciences setting to allow for innovation to flourish while maintaining the tight controls necessary in a critical to life industry. Topics include general innovation tools like ideation and design thinking as well more specific tools such as open innovation and design contests. Additional topics include product lifecycle management and regulated product utilization management. (2cr)
This course will introduce students to the regulatory aspects of product development in life sciences, such as Quality System Regulation 21CFR Part 820 and Design Controls. It also introduces the premarket approval process with FDA including 510K, PMA, etc. Also addressed are the regulatory aspects of marketing claims, including evidence-based claims, approved indications and off-label usage. The arcane process of reimbursement (getting paid) for life sciences products is also introduced. Also covered are the unique aspects of intellectual property (IP) in life sciences. (2 cr)
PRODUCT MANAGEMENT: SOFTWARE, WEB, AND MOBILE FOUNDATIONS CERTIFICATE
This course introduces students to the range of activities along the new product development pathway, including ideation and concept development, concept refinement, detailed product design and engineering, design for manufacturability, design for usability, prototyping, scale-up and launch. It introduces multiple tools (used in different industries) to manage product development activities, including phase gate systems, agile development for software, lean development for products, etc. It also introduces scheduling, budgeting and monitoring tools to project and measure new product programs. (4 credits)
This course introduces students to the various skills a product manager must deploy for product success including product definition, positioning, pricing, placement and promotion. It also introduces the relationship skills needed to manage cross-functional interactions and indirect and virtual teams. The course also includes an overview of the financial tools used to guide product realization and financial metrics used to measure product success. (4 credits)
Innovation is at the very core of life sciences, which are based heavily on cutting edge science and technology. At the same time innovation is very challenging in life sciences settings, due to the heavily regulated environment. This course introduces students to the tools and techniques used in life sciences setting to allow for innovation to flourish while maintaining the tight controls necessary in a critical to life industry. Topics include general innovation tools like ideation and design thinking as well more specific tools such as open innovation and design contests. Additional topics include product lifecycle management and regulated product utilization management. (2cr)
This course will introduce students to the regulatory aspects of product development in life sciences, such as Quality System Regulation 21CFR Part 820 and Design Controls. It also introduces the premarket approval process with FDA including 510K, PMA, etc. Also addressed are the regulatory aspects of marketing claims, including evidence-based claims, approved indications and off-label usage. The arcane process of reimbursement (getting paid) for life sciences products is also introduced. Also covered are the unique aspects of intellectual property (IP) in life sciences. (2 cr)
PRODUCT MANAGEMENT: COMPLEX TECHNOLOGICAL PRODUCTS FOUNDATIONS CERTIFICATE
This course introduces students to the range of activities along the new product development pathway, including ideation and concept development, concept refinement, detailed product design and engineering, design for manufacturability, design for usability, prototyping, scale-up and launch. It introduces multiple tools (used in different industries) to manage product development activities, including phase gate systems, agile development for software, lean development for products, etc. It also introduces scheduling, budgeting and monitoring tools to project and measure new product programs. (4 credits)
This course introduces students to the various skills a product manager must deploy for product success including product definition, positioning, pricing, placement and promotion. It also introduces the relationship skills needed to manage cross-functional interactions and indirect and virtual teams. The course also includes an overview of the financial tools used to guide product realization and financial metrics used to measure product success. (4 credits)
Innovation is at the very core of life sciences, which are based heavily on cutting edge science and technology. At the same time innovation is very challenging in life sciences settings, due to the heavily regulated environment. This course introduces students to the tools and techniques used in life sciences setting to allow for innovation to flourish while maintaining the tight controls necessary in a critical to life industry. Topics include general innovation tools like ideation and design thinking as well more specific tools such as open innovation and design contests. Additional topics include product lifecycle management and regulated product utilization management. (2cr)
Design Thinking is a systematic approach to innovation and creative problem-solving that can be used in many disciplines. This course will introduce the tools and methods that underpin Design Thinking, providing students ‘…the ability to combine empathy for the context of a problem, creativity in the generation of insights and solutions and rationality to analyze and fit solutions to the context’ [David Kelley, founder IDEO]. Included are techniques to understand users’ motivations and to gather deep insights, as well as ways to learn from failure: innovation entails taking risks and trying new things. Also included are introductions to communication methods central to innovation including visual storytelling and video prototyping. (2 cr)
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.
- #10 Top 50 Best Value Online Big Data Programs 2020 by ValueColleges.com
- U.S. News & World Report 2022 #34 Best Regional Universities North
- U.S. News & World Report 2022 #41 Best Value Schools
- U.S. News & World Report 2022 #3 Most Innovative Schools
- Money Magazine’s Best Colleges 2020
- The Princeton Review 2021 Best Northeastern Regional College
- Apple Inc., Apple Distinguished School
BUSINESS LEADERS START HERE
“As a new manager with expanding responsibilities at times I’m faced with resource constraints. The Data Science program has given me the ability to support my team by taking on some of the analytic work when we have resource constraints. Not only am I gaining a greater understanding of analytics, but I am able to apply my new knowledge every day in the workplace.”
