Developing Tomorrow’s Leaders
at MaHIMA

IN-DEMAND ONLINE MASTERS DEGREES & CERTIFICATES

  • Accounting

  • Data Science

  • Business Analytics

  • Healthcare Analytics

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$75

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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.

CORE REQUIREMENTS (28 CREDITS)

The emphasis of this course is on developing and analyzing financial statements. The course explores how particular transactions and differing accounting options may affect financial statements. Topics such as income recognition, inventory alternatives, leases, pensions, and other current issues are studied, through case analysis, to understand their impact on the financial statements. These topics are covered in the larger context of industry cases in which multiple companies are analyzed and evaluated using profitability and risk ratios. A final project (case) will integrate financial analysis, forecast and valuation.

Course Learning Objectives

Upon completion of this course, students should be able to:

Gain in-depth understanding of Generally Accepted Accounting Principles (GAAP)
Interpret financial statement data and disclosure
Analyze financial statement using different analytical methods
Value companies using different valuation models

This advanced course in auditing will further develop the student’s skills and research abilities in assurance services. Case studies are utilized to further develop the student’s audit and assurance skills. Emphasis will be on risk analysis, development of research skills in accounting and auditing, audit documentation, and development of leadership, teamwork and communication skills including effective report writing. An introduction to fraud examination and the impact of information technology on the audit process will be included.

Course Learning Objectives

Upon completion of this course, students should:

Be able to communicate in a professional manner on issues related to assurance services and the client’s business environment.
Be able to identify alternative audit and sampling approaches to testing the client’s financial statement assertions.
Make informed and responsible judgments and exercise professional skepticism about client business and fraud risks, control environment, and form an opinion on fairness of the client’s financial statement presentation
Understand the importance of working as a team and each team members responsibilities on an audit engagement

This course covers ethical and professional conduct including the role of rules of ethics and legal aspects relating to the practice of public accounting. The course is case based and includes the AICPA Code of Professional Conduct, AICPA and SEC independence rules, as well as current developments in the ethical and legal environment as it relates to public accounting. Case analyses of recent corporate scandals are a component of the course. The mandates included in the SARBANES-OXLEY legislation will also be covered.

Course Learning Objectives

Upon completion of this course, students should:

Be able to recognize ethical issues in a business context.
Be able to identify the stakeholders, alternative courses of action, and costs and benefits of each alternative in the environment of AICPA professional ethics and legal frameworks as they relate to the situation.
Be able to make informed, responsible, and ethical judgments between available alternatives in specific business and professional situations.

This course examines C corporations, S corporations, Limited Liability Corporations, Limited Liability Partnerships, and partnerships as taxable entities. Topics include the theory and political environment of taxation, income determination, deductions and credits, acquisition and disposition of property, and related gains and losses. Additional topics include distribution of income and liquidation of business entities Tax planning and research are emphasized.

Course Learning Objectives

Upon completion of this course, students should be able to:

Understand types of business entities and tax implications of these various ownership structures.
Understand basic tax concepts and apply relevant Internal Revenue Code (“IRC”) sections and regulations and case law.
Understand the tax changes brought about by the Tax Cuts and Jobs Act of 2017.
Understand your obligation to make informed and ethical decisions regarding tax planning strategies and tax savings opportunities.

This course will strengthen students’ skills and abilities to analyze data at a level required by top employers. The course will delve into assignments that build mastery of productivity tools commonly used by managers. Several workshops will develop skills for using computer-based technologies to locate, access, evaluate, manipulate, create, store, and retrieve information to express ideas, perform sophisticated analysis of data, and communicate information and results with others. Students learn how to build spreadsheet models to perform scenario analysis, analyze the impact of uncertainty, and formalize trade-offs in the context of business decisions. Students will integrate a variety of supporting materials to deliver a thorough business presentation. By the end of the course, students will have a more analytic view of business decision-making and be more adept at analyzing data and presenting results in a business context.

Course Learning Objectives

Upon completion of this course, students should be able to:

Define and successfully analyze problems in a business context and in a communication style and approach that is clear, precise and understandable
Select analytical tools, techniques and approaches to best identify solutions and that in turn will assist you with conveying your message
Communicate findings through both oral and written presentations; the biggest hurdles for many people in this area is the ability to overcome fear and to be able to have your audience “trust” you and that message or messages that you are conveying
Collaborate with a team to enhance the above; the ability to identify with and project a team approach that gives each member of the team credibility; how to pick up on when one’s analytical skills are not being received effectively i.e. Body language
Communicate effectively in cross cultural environments in our global environment

Effective writing and speaking skills are necessary for a career in management. This course is designed to help students develop a process for thinking and writing strategically. Students will learn how to analyze message, purpose, and audience; develop strategies for structure and style; construct persuasive arguments; and review for tone, organizational flow, and quality of evidence. This course will enable students to develop and demonstrate their ability to deliver formal and informal presentations and written reports in the context of addressing business challenges. Students will also learn communication strategies, principles, and methods as well as interpersonal skills that are essential for success in business. Students will have the opportunity to receive instructor and peer feedback.

Course Learning Objectives

Upon completion of this course, students should be able to:

Have a working knowledge on how the communication process (sender, message, channel, receiver, interpretation) are directly related to successful business communication.
Understand the importance of effective communication in business and society, and the mastery of such communication – including persuasive writing, oral, and presentation skills.
See advancements in their own writing and speaking abilities in a variety of settings and business contexts, including channels and methods to effectively communicate visual data.
See improvements in students’ own skill levels in interpersonal, small group, public, and persuasive communication.

This course deals with concepts, methods, and applications of decision modeling to address various marketing issues: segmentation, targeting, positioning, new product design, advertising and promotional planning, and strategic planning. Topics include gathering primary and secondary data, questionnaire/instrument design, experimental design, data analysis, and presentation of results. Students use substantiated facts and inferences to make managerial decisions. Students make analytical presentations in a client-based atmosphere.

Course Learning Objectives

Upon completion of this course, students should be able to:

Understand important concepts, theories, and models related to marketing planning, marketing strategy, branding, marketing research, and consumer behavior
Deploy critical thinking skills
Execute problem identification and analysis, strategic alternative identification and evaluation
Develop meaningful marketing recommendations and implementation planning

This course examines how managers work to integrate operations, marketing, finance, information systems, and management processes to achieve competitive advantage. Students will examine how to analyze the external environment and assess the capabilities of an organization to craft competitive strategies. Different strategic perspectives will be reviewed to understand the competitive dynamics within a strategic group. Case examples will be reviewed to consider how managers combine analysis with creative problem solving to achieve innovative strategies, to create new markets, and to compete in novel ways.

Course Learning Objectives

Upon completion of this course, students should be able to:

Identify the main concepts in strategic management, including the different levels and components of strategy, sources of sustained competitive advantage, and the interconnection among organizational decisions.
Discuss the pros and cons of innovation-based strategies.
Analyze how managers responsible for executing strategy lead their organizations through successful strategic changes.
Perform the necessary diagnoses and analyses to make decisions about valuable strategic options and express them in written and oral communications.

ELECTIVES (4 CREDITS)

This course will provide the student with an understanding of accounting as an information system. Specifically, the course will give the student hands on experience with understanding the structure of an accounting system and also the use of accounting software as a tool. Frameworks for analyzing internal control systems and understanding the structure and risks of IT based systems are also covered.

Course Learning Objectives

Upon completion of this course, students should be able to:

Understand the concept of separation of duties
Have the ability to read and understand a flowchart
Utilize accounting software, specifically QuickBooks

This course considers the reporting environment and rules of the Financial Accounting Standards Board as it relates to Not-for-Profit entities and the rules and procedures of the Governmental Accounting Standards Board as it relates to governmental entities. The course will investigate and examine the reporting practices of federal, state, and local governments and specific not-for-profit entities such as hospitals, universities, and not-for-profit organizations.

Course Learning Objectives

Upon completion of this course, students should be able to:

Understand the issues unique to not-for-profit and governmental accounting where the accounting system lacks a “bottom line” profit motive.
Understand the accounting processes and methods, including entries and financial statements unique to not-for-profit and governmental entities.
Be able to use research tools unique to the not-for-profit environment such as Guidestar and the data available from States’ Attorney General’s Office.
Be able to analyze not-for-profit and governmental entity financial statements.

This course will focus on the Uniform Commercial Code (UCC) and Common Law practices related to commercial transactions, particularly those relevant to accountants and auditors. Topics include contract law, the Uniform Commercial Code, agency law, partnerships, corporations, and limited liability companies, securities regulations, bankruptcy, property laws, and accountant/auditor liability.

Course Learning Objectives

Upon completion of this course, students should:

Students will understand how contracts are formed, parties’ rights and responsibilities pursuant to a contract, methods of enforcement and remedies in the event of a breach.
Understanding the application of legal principles in the accounting profession.
Appreciate the means by which courts analyze legal cases in deciding and applying legal issues in the context of business regulation and control.
Students will gain an understanding of the various court systems and administrative agencies authority in the regulation of business and will gain an understanding of how government agencies operate and create laws that regulate business including antitrust regulation, financial regulation and insurance.
Students will learn the law of negotiable instruments (checks, drafts, notes etc.) and their role in the financing of operations.
Students will learn the role of bankruptcy law and its impact on the law of property and ownership.

This course will provide the student with a knowledge of a variety of fraud schemes, how to prevent them, how to investigate them, and how to document their findings. We will consider the auditors’ role in investigating scams, employee fraud schemes, and fraudulent financial reporting. Specifically, the course will give the students hands-on experience with a simulated case study of a fraud.

Course Learning Objectives

Upon completion of this course, students should be able to:

Identify common scams, fraudulent reporting methods and employee fraud schemes.
Understand methods of prevention and detection of fraudulent financial reporting and employee fraud.
Have a working knowledge of documenting and reporting fraud and the legal rules of evidence.

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.

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.

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 2018 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 2018 Best Regional College
Transitioning Athletics to Division 1 and joining the Northeast Conference in 2019-20

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testimonial

“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.”

WILL LINDSEY, Manager of Analytics, Blue Cross and Blue Shield of North Carolina