University of Connecticut

Course Descriptions

Business Analytics (12 credits)

Project Management (12 credits)

Business Analytics

Business Process Modeling and Data Management (3 credits)

Managing and improving a business process adds to the bottom line, and data is a core business asset derived from multiple business processes. The need to manage both efficiently and use them effectively has assumed paramount importance. In all business domains –financial, marketing, operations, etc., pertinent and available data is a bedrock for actionable business intelligence, predictive modeling and other data mining techniques, which is a key element of business productivity and growth. This course introduces market-leading techniques that help to identify and manage key data from business processes. It provides the essential tools required for data mining and business process re-engineering. It combines lecture, class discussion and hands-on computer work in a business-oriented environment. The course covers the following:

Model business processes; How to manage data for various business applications; Show how to retrieve data and create reports in the form you need; Implement a database using a DBMS tool (Oracle or MS Access); Learn how to lead data management, business intelligence and business process engineering projects.

Course includes: Assessment and Analysis; Data Storage, Planning, and Design; Process Modeling

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Predictive Modeling (3 credits)

Technology advancements now allow companies to capture and store large amount of data (or facts) in databases and data warehouses. With so much raw data, organizations urgently need tools that allow them to effectively sift through these enormous datasets and extract actionable information and knowledge (meaningful patterns, trends, and anomalies) from such data sets to help them optimize businesses. Predictive modeling is the process of developing models to better predict future outcomes for an event of interest by exploring its relationships with explanatory variables from historical data. It is used extensively in businesses to identify risks and opportunities associated with a set of conditions.

The course introduces the techniques of predictive modeling and analytics in a data‐rich business environment. It covers the process of formulating business objectives, data selection, preparation, and partition to successfully design, build, evaluate and implement predictive models for a variety of practical business applications (such as direct marketing, cross selling, customer retention, delinquency and collection analytics, fraud detection, machine failure detection, insurance underwriting). Predictive models such as classification and decision trees, neural networks, regressions, association analysis, link analysis, and others will be studied.  It is practically oriented with a focus of applying data analytic tools to help companies answer business questions such as who is likely to respond to a new advertisement, what customers are most likely to be default on a loan/payment, what transactions are most likely to be fraudulent, and what combinations of products are customers most likely to purchase at the same time.

The primary approach will entail ‘learning-by-doing’ with the use of the state-of-the-art software such as SAS JMP®, SAS Enterprise Miner®, and a variety of open source software.

Course includes: Data Visualization; Predictive Models; Model Assessment, Scoring and Implementation

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Business Decision Modeling (3 credits)

Spreadsheets are ubiquitous elements of software packages available on most personal and networked microcomputer systems. They are enormously flexible and powerful tools with primary application in business/financial. They have the ability to hold large numerical datasets and perform complex calculations, including statistical analyses.

This course serves as a guide to developing high quality spreadsheets that ensure that the objectives of the model are clear, defining the calculations, good design practice, testing and understanding and presenting the results from spreadsheet models.

Course includes: Introduction to Modeling and Decision Analysis; Optimization and Linear Programming; Modeling and Solving Problems in a Spreadsheet; Sensitivity Analysis; Introduction to Simulation; Network modeling

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Data Mining and Business Intelligence (3 credits)

This course is a follow-up of the predictive modeling course. It aims at business managers, information professionals, financial analysts, insurance analysts, as well as general audience who are interested in how to turn data into business intelligence and eventually use data and analytics to create business value. It is generally assumed that students who take the course will have basic understanding about statistics and simple predictive modeling techniques (such as data visualization, simple linear regression, data partition, etc).

The course will discuss the principles and ideas underlying the current practice of data mining, as well as introduce a broad collection of useful data analytics tools (such as clustering, logistic regression, latent class analysis, survival models, association and event analysis, text mining, etc). However,it emphasizes hands-on learning with a focus on dealing with real business problems from industry-strength projects. In that regard, the course will take a case-study approach. It will not just describe/explain the end results, but also discuss the process of formulating/refining business objectives, data selection, data preparation, model selection and evaluation that lead to the results. The course will use analytics software SAS (including SAS Base, SAS Enterprise Miner, SAS Text Analytics) for hands-on experimentation with various data mining techniques.

Course includes: Data Mining Overview; Clustering Analysis; Latent Class Analysis, Survival Models; Text Mining; Social Network Analysis

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Additional optional course:

Real-time Enterprise Data Integration and Audit (3 credits)

Companies store and collect large amounts of data during day-to-day transactions. To analyze long-term trends and patterns in the data and provide actionable intelligence to managers, this data needs to be consolidated in a data warehouse. A data warehouse is “a repository of subject-oriented, time-variant data from multiple sources, used for information retrieval and decision support”. It provides a single consolidated interface to the entire corporate data. Data analysis for enterprise-wide business intelligence can then be performed on such consolidated data.

This course material will cover various aspects of the data-warehousing environment, followed by data mining techniques for business intelligence (BI). It is a combination of lecture, class discussion and hands-on computer work, and will:

  • Help you understand how to design and implement a data warehouse for various business applications.
  • Perform OLAP and data mining operations
  • See an integrated view of data warehousing and BI.

Course includes: Setting up the Warehouse Environment; Building the Warehouse; Deploying and Maintaining the Warehouse

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Enterprise Security, Governance and Audit(3 credits)

This course discusses the business risks arising from the digital information processing and identifies ways to prevent, detect, and mitigate negative consequences of information security breaches. First, students will be introduced to the basic principles of information security, its role in reducing information risk exposure, and tools and solutions that can be used to prevent information loss or costly business interruptions. Second, students will explore the role of information technology governance in business organizations, discuss important relevant laws (for example, Sarbanes-Oxley Act of 2002), reporting requirements, and industry standards for IT Governance (for example, COBIT). Third, students will study the process of information systems audit, IT audit tools, and audit procedures to help in detection and prevention of fraud.

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Data Analytics using R (3 credits)

Data analytics has emerged as one of the most important new areas with high demand in industry. R, an open source domain specific language (DSL) focused on data analytics, has grown in importance and usage in corporations, because it is free to use, and is being constantly improved to include the latest statistical techniques. Organizations using R span a wide range of industries and include companies such as Google, Facebook, Bing, The New York Times, Orbitz, etc. R code is always at the cutting edge, because the source code is open source, and it receives frequent new contributions and improvements from experts around the world. This course helps students develop proficiency in data analytics using R for statistical inference, regression, predictive analytics, and data mining. It combines lectures, hands-on exercises, business case discussions, and student presentations in a professional environment. As a student of data analytics, you will benefit from learning R because (a) it is a core skill in high demand, and (b) because doing data analytics using R will enhance your understanding of analytics.Specifically, we will cover the following topics: R basics — data frames, packages, etc., Formal Inference – standard errors; t-distribution; confidence intervals; Multiple Regression – assumptions and diagnostics; model fitting; comparing models; interpreting coefficients; multicollinearity; Generalized linear models – Logistic regression; Poisson and quasi-poisson regression; ordinal regression models; survival analysis; Time series analysis – graphical exploration; autoregressive (AR) and autoregressive moving average (ARMA) models; Data mining: clustering, association rules; Text mining: analyzing twitter and social network data.

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Adaptive Business Intelligence (3 credits)

Adaptive Business Intelligence (ABI) is a discipline that combines predictions, optimization, and adaptability to create decision support systems in process management. The goal is to answer several fundamental management questions: What is likely to happen in the future? What is the best decision right now? How to adapt to change? The study of these systems is an important area of research in information technology because of their wide range of applications to situations where the number of possible solutions is so large that it precludes a complete search for the best answer, decisions have to be made in a time-changing environment, the problems are heavily constrained, and there are many (possibly conflicting) objectives

The goal of the course is to teach the use of techniques from statistics, optimization, and evolutionary algorithms to implement Adaptive Business Intelligence (ABI) systems. The course will introduce students to the fundamentals of decision support systems, genetic algorithms, ABI systems, Bayesian analysis, and their applications to diverse management contexts. Applications in the class will be developed using the R programming language.

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Big Data Analytics with Hadoop (3 credits)
In this course, students will learn cutting edge technologies and concepts related to analysis of big data – data that is too large to process in the main memory of one computer. The course is organized into two parts. In the first part, students will build upon their understanding of RDBMS and SQL, and explore the use of SQL-like queries in a big data environment (Hadoop distributed file system – HDFS), using tools such as Sqoop, Pig and Hive. Students will identify typical situations that warrant large data analysis, move data between relational databases and Hadoop using Sqoop, manage data in HDFS, and use Pig and Hive to run distributed queries on data.

In the second part of the course, students will build upon their understanding of data mining techniques and learn to apply them to analyze large datasets. Students will use Apache Mahout software in the Hadoop ecosystem to explore item-based collaborative filtering, non-distributed recommenders, frequent itemset mining, clustering, and some text mining algorithms, including Naïve Bayesian classifier.

Course includes: SQL-like querying in big data cluster; systems, classifiers, clustering; Hadoop ecosystem overview; and deep dive into Hadoop Pig, Hive and Mahout.

 

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Project Management Courses

Introduction to Project Management (3 credits)

Business objectives are increasingly solved by projects. Many projects fail to produce the expected results, are over budget, or not completed on time. Good project management significantly improves the likelihood of a successful project. This course will examine the project management process and the management of a portfolio of projects, with focus on techniques to overcome the pitfalls and obstacles that frequently occur during a typical project. It is designed for business leaders responsible for implementing projects, as well as beginning and intermediate project managers.

Course includes: Project management principles and practices; Portfolio management; tools such as Charters, FMEA, Gantt Charts, PERT Charts, Work Breakdown Structures, Critical Path Analysis, Budget Simulation, Timeline Simulation, Project Crashing, and Project Plans, in order to increase the likelihood of project success. Topic coverage prepares students towards Project Management Institute’s (PMI) Certified Associate in Project Management (CAPM).

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Project Leadership and Communications (3 credits)

This course explores the issues that managers face when attempting to get work accomplished through other individuals or teams, while emphasizing innovation in their products, services, and internal processes. The relationship between individuals, organizations and the larger social context is studied. Focus topics include: motivation, incentive systems, team and work flow management. You will review management techniques, effective verbal and nonverbal communication methods and negotiation skills as they pertain to the different aspects of managing individuals or teams. You will study how successful managers have developed the ability to understand the nature of conflict and its resolution through persuasion, collaboration, and negotiation. You will learn theories of interpersonal and organizational conflict and its resolution as applied in personal, corporate, historical, and political contexts.

Course includes: Leadership Type; Behavioral Strengths and Motivators; Managing Personal Growth, Time and Accountability; Managing Team Conflict; Improving Team Productivity; Planning and Problem-Solving Using Collaboration; Relationships with Your Stakeholders

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Project Risk and Cost Management (3 credits)

This course introduces the art and science of project risk as well as continuity management and cost management. Managing the risk of a project as it relates to a three-part systematic process of identifying, analyzing, and responding is examined through actual case studies. You will learn how to manage risk to ensure a project is completed through both general and severe business disruptions on local, national and international levels. You will learn the process of cost management, early cost estimation, detailed cost estimation, and cost control using the earned value method. You will review issues related to project procurement management and the different types of contracts for various scope scenarios.

Course includes: Project Planning; Cost Management; Managing Budget and Progress; Risk Management; Portfolio Management; ROI

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Advanced Business Analytics and Project Management (3 credits)

This capstone course will involve a live data analytics project, where students will need to integrate their knowledge of data analytics and project management. Using the skill sets of predictive modeling, data management, process models, and data mining techniques, students will investigate a real problem through data analytics, and will use their project  management skills to complete the project within time and budget constraints.

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Additional optional course:

Ethical and Legal Issues in Project Management (3 credits)

The primary aims of the course are to introduce ethical concepts that are relevant to resolving legal and moral issues in business; to impart the reasoning and analytical skills needed to apply ethical concepts to legal and business decisions; to identify the moral issues involved in the management of specific legal and ethical problem areas in business; to provide an understanding of the social and natural environments within which legal and moral issues in business arise; and to analyze case studies of actual moral dilemmas faced by businesses.

Course includes: Breach of Contract, Judicial Remedies, and Alternative Dispute Resolution Mechanisms

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Managing International Development Projects (3 credits)

Organizations of all kinds around the world today, whether private or public, large or small, for profit or not for profit, achieve their strategic and operational goals by carrying out projects. The emphasis of this course is on the application of project management knowledge, tools, and techniques to the planning, organization, and delivery of international development projects and programs. Funded by institutions (e.g., multilateral or regional development banks, United Nations associated agencies, bilateral government agencies, non-governmental organizations, global funds), these projects/programs cover a wide range of sectors and focus on poverty reduction/alleviation and improving living standards of people in developing and emerging countries, assistance to victims of natural or people caused disasters, capacity building and development of basic physical and social infrastructures, and on promoting environmentally sound development and basic human rights protection.

The format of the course consists of a mixture of lectures, case studies, class discussions, and presentations to peers. The material is organized to enable both technical and non-technical participants to appreciate the value offered by a variety of management practices and planning tools as means for administering, directing, and coordinating international development projects.

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