ACCT.6600 Accounting Data Analytics (3cr)
Topics to be covered in this course include managing and leaning data, building and evaluating models, visualizing the results of data analyses, and drawing conclusions from the analytics. A series of accounting topics with data analytics application will be discussed, such as fraud and earnings management detection, and financial statement analyses. Students should leave this course with skills necessary to understand data and manage data, to translate accounting and business problems into actionable proposals, and to present data/results to managers and data scientists.
ECON.2110 Statistics for Business and Economics I (3cr)
Presents descriptive statistics, sophisticated counting techniques and other components of probability, simple random variables and their distribution, bivariate functions, sampling theory properties of estimators.
Requirements:
MATH 1210 pre-req
FINA.6350 Programming for Finance (3cr)
This course introduces Python programming using examples for finance. Required financial knowledge is introduced as necessary, and the course does not require prior knowledge of programming. Exercises will include creating algorithms for financial models for valuing stock and bonds and evaluating the risk and return characteristics of individual assets and portfolios.
Requirements:
FINA.5010 Business Financial Analysis, or permission of graduate program coordinator.
FINA.6410 Cryptocurrency (3cr)
This course introduces students to the landscape of cryptocurrency. Students learn the core topics of mining, blockchain technology to allow them to better understand how this technology can change the way business operates.
MATH.2830 Introduction to Statistics (3cr)
An introduction to descriptive statistics, graphing and data analysis, probability laws, discrete and continuous probability distributions, correlation and regression, inferential statistics. No credit in Sciences (except Biology and EEAS) or Engineering. Meets Core Curriculum Essential Learning Outcome for Quantitative Literacy (QL).
Notes:
MATH.1115 or equivalent; MA; Previously 92.183
MGMT.6500 Workforce Analytics (3cr)
Workforce analytics is the use of empirical data to improve the management of an organization's human resources. The goal is for students to develop analytical literacy that will enable them to understand and apply fundamental analytic techniques, engage knowledgeably with data scientists in the application of more complex forms of analysis, interpret the analytical reporting of others with greater sophistication, and apply empirical evidence to employee-related decisions. The course emphasizes the link between workforce analytics and strategic decision making at all levels of leadership that will guide strategic performance management, talent development, and optimal investment in human capital. It is thus a high value leadership tool central to the achievement of organizational goals.
MIST.CAPSTO Non-Credit Capstone Review (0cr)
This is a non-credit (0credits) pre-requisite for the MIST.6490 capstone course. The focus of the course is on preparing students for their capstone projects in the MS Business Analytics program. The course covers background information on data privacy, non-disclosure agreement, project management best practices, data mining project methodology, team formation, and soft skills development to work with company sponsors.
MIST.2010 Business Information Systems (3cr)
The course familiarizes students with key components and principles of information systems and information technology. Students will learn about the role of IS/IT in businesses for improving organizational performance, competing globally, and gaining competitive advantage. The course covers basic principles and technologies pertaining to information management, business intelligence, and business analytics for improving decision-making and managing knowledge. The basic role of enterprise systems in businesses for enabling operational excellence is also discussed. Social and ethical issues associated with the use of information systems are also discussed. Students will utilize IS technologies (e.g., spreadsheet and database software) in a hands-on manner for business problem-solving.
Requirements:
COM Filter courses,or BU minor
MIST.6010 Management Information Systems (3cr)
Examines computer technologies, database management, and data communications as vehicle to improve and/or restructure business processes and decision making effectiveness to create competitive advantage.
Notes:
If not currently matriculated in a Manning School of Business program, please contact the MBA staff at MBA@uml.edu or call 978-934-2848 for permission to take courses.
Requirements:
MBA, MSA, MS ITE or MSF.
MIST.6030 Database Management (3cr)
This course provides students with in-depth knowledge for modeling, designing, implementing, and managing database systems for operational and decision support purposes. Topics covered include relational database model, entity-relationship modeling, normalization, SQL language, data warehousing, data quality and integration, data and database administration, and object-oriented database.
Requirements:
MIST.6010 Management information systems, and
Matriculated MS Business Analytics, or Business
Analytics Certificate, or Permission of Program
Coordinator.
MIST.6060 Business Intelligence and Data Mining (3cr)
This Course introduces the concepts and technologies of business intelligence and data mining. The course studies how data-oriented business intelligence techniques can be used by organizations to gain competitive advantages, as well as how to design and develop these techniques. Topics include classification, clustering, association analysis, prediction, and text and web mining. Data-mining related ethical issues will also be discussed.
Requirements:
MIST.6010 Management information systems, and
Matriculated MS Business Analytics, or Business
Analytics Certificate, or Permission of Program
Coordinator.
MIST.6070 Electronic Business (3cr)
This course provides a foundation on digital commerce and e-business for MBA students. It will cover both technological and managerial aspects of managing e-business operations in either a traditional or pure "dot.com" organization. Issues covered include interactive marketing and market-spaces, agent-based commerce and intelligent markets, electronic shopping carts, user interface issues, EDI transaction via Extranets, database interfaces, personalization and targeted communications, security, encryption, and payment systems, privacy and intellectual property.
Requirements:
MIST.6010 Management Information Systems, or permission of graduate program coordinator.
MIST.6080 Enterprise Systems and Analytics (3cr)
This course focuses on enterprise systems such as Enterprise Resource Planning (ERP) and Customer Relationship Management (CRM) systems to unify information across organizational functions. Students will learn business process design and execution through ERP systems. It covers essential ERP concepts, the pros and cons of ERP implementations, and provides experience with the commercial ERP package. Additionally, the course delves into descriptive and predictive analytics and its role in enterprises ,with Cloud Analytics software, which combines business intelligence, planning, predictive, and augmented analytics into the cloud environment to support business processes. Students also explore AI technologies and in-memory databases as part of the Analytics Cloud software.
Notes:
If not currently matriculated in a Manning School of Business program, please contact the MBA staff at MBA@uml.edu or call 978-934-2848 for permission to take courses.
Requirements:
MBA, Found Core, MIST.6010 or MSA
MIST.6140 AI Driven Social Media Analytics (3cr)
This course introduces the intersection of artificial intelligence and social media analytics, equipping students with the skills to extract insights from vast social media data. Topics include graph theory, social influence, community detection, information diffusion, social network analysis, sentiment analysis, and AI-driven recommendations and feature selections. Upon successful completion of this course, students will learn ho AI-powered techniques, including machine learning, natural language processing, and network analysis, are used to analyze social media interactions, detect trends, and drive decision-making, and have hands-on experience applying AI techniques to social media data, enabling them to address real-world challenges in marketing, business intelligence, and strategic decision-making.
Requirements:
MIST.6010 Management Information Systems, or Matriculated MS Business Analytics or permission of program coordinator.
MIST.6150 Data Quality for Business Analytics (3cr)
This course provides students with knowledge and skills to process data for business analytics. Topics include data quality requirement and data preparation for business analytics, impact of data quality on analytics, and methods for assessing and improving data quality in the context of business analytics.
Requirements:
MIST.6010 Management information systems, and Matriculated MS Business Analytics, or Business Analytics Certificate, or Permission of Program Coordinator.
MIST.6160 Advanced Data Mining (3cr)
The course will cover advanced data mining techniques with applications in different business domains. Students will be introduced to advanced analytic solutions aimed at addressing issues related to big data including volume, variety, and velocity. Topics will focus on performing descriptive and predictive analytics through programmatic analytic platforms as well as text analytics techniques for unstructured or semi-structured data. Concepts will be introduced through a hands-on approach using state-of-the-art analytic platforms and tools.
Notes:
If not currently matriculated in a Manning School of Business program, please contact the MBA staff at MBA@uml.edu or call 978-934-2848 for permission to take courses.
MIST.6170 Advanced Machine Learning and AI (3cr)
This is an advanced course on machine learning, data science, and AI for business. In this course, students learn how to analyze, design and develop machine learning and AI techniques and tools for business analytics. Applications to both strategic and operational problems in today's data-driven ecosystem will be discussed. Topics include supervised learning, unsupervised learning, statistical learning, ensemble learning, model and performance evaluation, text feature learning, text analytics, neural networks, deep learning, attention mechanism and transformers, large language models (LLMs), generative AI, and machine-learning and AI related ethical and social issues. The course will be taught using the Python programing language, but no prior experience with Python is required.
MIST.6180 Causality and Modern Methods for Analytics (3cr)
The course focuses on modern analytics methods and casual inference techniques with applications in business and social sciences. Topics include model interpretation, model uncertainties, methods for causal inference, text analytics, deep learning, and recent development in artificial intelligence. Drawing from economics, statistics, and machine learning, this course will elevate students' ability in deriving business insights using various types of data and appropriate modern analytics methods.
Requirements:
Pre-req- MIST.6060 Business Intelligence and Data Mining, and POMS.6120 Statistics for Predictive Analytics, or Permission by Instructor or Program Coordinator.
MIST.6450 Information Technology Project Management (3cr)
Application and integration of the project management body of knowledge (PMBOK) areas to managing information technology (IT) projects. Focuses on project management tools and techniques for defining and managing the project's goal, scope, schedule, and budget. Other topics include quality management, risk management, change management, and knowledge management as they are related to IT projects.
Notes:
If not currently matriculated in a Manning School of Business program, please contact the MBA staff at MBA@uml.edu or call 978-934-2848 for permission to take courses.
Requirements:
CSCE Graduate Restrictions
MIST.6490 Business Analytics Capstone Project (3cr)
Students will be guided through the process of developing their soft (communications) and hard (Technical) skills while delivering a business analytics project to support decision making in organizations. In this culminating project, students draw on the breadth and depth of the curriculum to address an industry supplied problem in small teams. The capstone project will involve application of industry accepted methodologies and analytical tools to solve real-world problems in R&D, marketing, supply chain, healthcare, finance and/or other disciplines. Students who cannot participate in university provided projects, with the permission of the program coordinator, are provided with two alternative project options: a) conduct a real-world business analytics project individually in a similar manner as above with an organization of their choice; or b) conduct a data analytics project individually as part of a research project under the guidance of an OIS Department faculty member.
Requirements:
MIST.6030 Business Database Management, and MIST.6060 Business Intelligence and Data Mining, and POMS.6120 Stat. for Predictive Analytics, and POMS.6220 Decision Analytics, or Permission of MS Business Analytics Program Coordinator.
MIST.6890 Internship in Business Analytics (3cr)
The Internship in Business Analytics provides three academic credits that count as an MSBA core course for working in an anaytics-related position that integrates more than one analytics discipline with a minimum of 11 hours per week for a single semester. After developing a proposal in cooperation with their employer, students obtain the permission of the internship coordinator to enroll in the course. Students then perform their designated work duties during the semester and write a reflective term paper which describes their work experience and relates it to their academic work in the other course taken at UMass Lowell.
MKTG.6350 Marketing Analytics (3cr)
Marketing Analytics will cover commonly used methods in the Marketing area, such as regression analysis and t-tests. Students will work with actual sales and customer data to determine appropriate strategic actions. Students will also learn how to use relevant analysis software, such as Excel and SPSS.
POMS.6120 Statistics for Predictive Analytics (3cr)
This course introduces statistical methods and techniques for predictive analytics. This is part of the business-analytics umbrella of courses. The main focus of this course is on regression, a powerful and widely used predictive method. Topics covered include simple linear regression, multiple regression, variable selection, model diagnostics, and systems of regression equations. The course also covers classification techniques using statistical methods such as linear discriminant function and logistic regression. Spreadsheet software, such as MS Excel, and statistical software, such as SAS and R, will be heavily utilized.
Requirements:
POMS.6010 Operations Management, or
Matriculated MS Business Analytics, or Business
Analytics Certificate, or matriculated MS
Engineering Management, or permission of
program coordinator.
POMS.6210 Advanced Statistics for Business Analytics (3cr)
This course introduces important statistical techniques in business analytics such as time series analyses, multivariate analyses, and fundamental concepts in casual inferences. This course is practice-oriented with a focus on business contexts such as housing finance, e-commerce and online marketing.
Requirements:
POMS.6120 Stat. for Predictive Analytics, or MIST.6060 Business Intelligence & Data Mining, or Permission of Program Coordinator.
POMS.6220 Decision Analytics (3cr)
This course covers the three main facets of business analytics: descriptive, predictive, and prescriptive analytics. Students will gain the knowledge of managerial decision-making (commonly referred to as data analytics, decision support systems-DSS, data mining). Some of the business analytic topics covered include neural networks, decision trees, support vector machines, k-means, association rule mining, Analytical Hierarchy Process, Data Envelopment Analysis, expert systems, optimization, and simulation.
Requirements:
POMS.6010 Operations Management, or
Matriculated MS Business Analytics, or Business
Analytics Certificate, or matriculated MS
Engineering Management, or permission of
program coordinator.
POMS.6240 Analytical Decision Making Tools (3cr)
This course covers principles and techniques of applied mathematical modeling for managerial decision making. Emphasis is on the methods of prescriptive analytics, including optimization models, decision analysis, simulation modeling, and risk analysis. Problems studied will include applications in finance, health care, marketing, operations, and management. Cases studies will be used extensively to demonstrate the practical use of models to improve managerial decision making. In addition to developing and applying models, emphasis will be placed on explaining the models and interpreting their results.
Requirements:
POMS.6010 Operations Management, or
Matriculated MS Business Analytics, or Business
Analytics Certificate, or matriculated MS
Engineering Management, or permission of
program coordinator.
PUBH.5060 Quantitative Methods in Health Management (3cr)
This course explores analytic methods that can be used to improve the decision making of management, clinicians and others within the healthcare industry. Students learn the conceptual foundations of quantitative analysis and common methods used in supporting decision-making; developing evidence-based practices; analyzing data and testing hypotheses. Students also learn how to use industry-standard data analysis software applications, statistical packages and common applications for the development and reporting of analytic findings.
Requirements:
CSCE Graduate Restrictions
PUBH.5150 Applied Health Economics (3cr)
Students explore the economic dimensions of healthcare by considering the input, output, production and costs of producing quality healthcare which meets demand and evaluates the behavior of supply. Students consider provider payer systems and aspects relative to private and public health insurance in determining market power and competitive markets. Common economic evaluation methods are introduced to measure health service feasibility, and promote value judgment in the realm of healthcare reform and regulatory compliance.
PUBH.5270 Business Strategies for Health Organizations (3cr)
This course explores the important aspects of planning and implementation of business strategies in a health service organization. Students learn about the multi-step process of creating and managing a successful business plan, as well as strategies and solutions for analyzing business situations while utilizing popular tools of the industry.
PUBH.5310 Health Informatics (3cr)
This course introduces healthcare professionals to the power of data and the importance of analysis. Students learn how population informatics, consumer health informatics, translational bioinformatics, and clinical research informatics are essential components in selecting the techniques and systems used for transforming clinical data into information, knowledge and improved decision-making. The past, current and future role of healthcare IT is also discussed.
PUBH.6070 Healthcare Information Systems (3cr)
This course provides a broad-range overview of the healthcare information systems industry, its history, recent developments and continuing challenges, as well as a practical understanding of healthcare information systems acquisition and implementation. Topics include EMR, Data, CMS Quality Programs, Clinical Integration and health information exchange.
PUBH.6390 Electronic Health Record Systems (3cr)
The course addresses Electronic Health Records (EHR) integration with patient care flow, clinical decision making and patient engagement, as well as patient registries and clinical quality reporting. Students also learn core EHR functions, strategies for EHR optimization, and how the EHR can be leveraged for population health management. The course uses industry-leading EHR software as a learning tool to demonstrate how electronic health record technologies are used in a healthcare setting.