QTM6100 - BIG DATA AND DATA ANALYTICS
BIG DATA AND DATA ANALYTICS
QTM6100 BIG DATA AND DATA ANALYTICS
Since the advent of ubiquitous, large-scale data collection, managers have become increasingly interested in data- driven decision making. In this course, we will explore a broad toolbox of data analytic techniques that are commonly used to investigate large data sets in industry. Students will gain firsthand experience in using industrial data analytic software on real world data sets. Special attention will be paid to communicating both the process and product of data analysis effectively to a business-oriented audience.
QTM6110 - DATA EXPLORATION (QUANTITATIVE METHODS)
DATA EXPLORATION (QUANTITATIVE METHODS)
QTM6110 Data Exploration (Quantitative Methods)
Data is valuable when it is used to make good decisions and avoid bad ones. We consider the value of data as a resource by studying how the variety of information available can be displayed, interpreted and communicated. Students will see the different approaches suggested by both traditional statistical methods and the recent advances in big data analytics. The course will emphasize the ways in which managers and entrepreneurs are both producers and consumers of data.
QTM6300 - MACHINE LEARNING FOR BUSINESS
MACHINE LEARNING FOR BUSINESS
QTM6300: Machine Learning for Business
(Previously titled: Data Exploration and Analytics)
3 blended credits
This course will examine the methods and challenges faced in turning data into insightful analytics in business. With data sizes significantly increasing in the last decade, extracting meaningful information to compete successfully is essential. You will accomplish this by learning techniques for data gathering, data analysis, and visualization as well as in discussion on companies currently trying to turn the information they gather into business opportunities. We will learn a variety of methods and software for finding patterns(such as regression, neural networks, association rules, CART, forecasting etc.), building models, and ultimately making decisions using large data sets. We will address questions such as:
Guest speakers who are executives and consultants in the field of analytics and visualization will discuss how they address these challenges in their companies. This is a hands-on course with in-class exercises and group projects to help students learn and apply data analysis techniques preparing them for the practical challenges analysts face in the real world.
- How does Amazon recommend products based on your past purchases?
- How to forecast energy consumption based on historical weather and consumption data?
- How do credit-card companies detect fraud?
- What challenges does Big Data pose to companies and how to handle these challenges?
QTM6600 - ANALYTICS FOR DECISION MAKERS
ANALYTICS FOR DECISION MAKERS
QTM6600: Analytics for Decision-Makers
1.5 Credits (MSAEL core)
Data exploration and data-driven decision making are integral in identifying and validating business opportunities. Depending on the nature of the problem and the institutional context, techniques ranging from classical statistical methods (descriptive and inferential statistics) to more recent advances in big data and tools (Excel, R, Tableau) might provide the greatest utility and deepest insights. In this course, we encounter selection of these techniques and develop our ability to formulate analytics problems in ambiguous contexts, quantify performance of various solutions, and articulate the key results of our analysis to a non-technical audience, including using visualization methods.
QTM7200 - DATA, MODELS AND DECISIONS
DATA, MODELS AND DECISIONS
QTM7200 Data, Models and Decisions
Data, Models and Decisions (DMD) - This course is concerned with identifying variation, measuring it, and managing it to make informed decisions. Topics include: numerical and graphical description of data, confidence intervals, hypothesis testing, regression, decision analysis, and simulation. Applications to Economics, Finance, Marketing, and Operations illustrate the use of these quantitative tools in applied contexts. The course utilizes spreadsheet, statistical, and simulation software.
QTM7540 - BUS FORECASTING WITH PREDICTIVE ANALYTIC
BUS FORECASTING WITH PREDICTIVE ANALYTIC
QTM7540 Business Forecasting with Predictive Analytics
1.5 credit graduate elective
Predictive analytics encompasses a variety of statistical techniques that analyze current and historical facts to make predictions about the future or otherwise unknown events. In business, predictive models exploit patterns found in historical and transactional data to identify possible risks and opportunities. Predictive models capture relationships among many factors to allow the assessment of risk or the potential associated with a particular set of conditions. This course introduces advanced forecasting methods in the context of real business analytics data and decision-making situations. The objectives of the course are: to provide experience in using time series data (e.g., sales, profits, financial and economic indicators, and industry sector indicators) to explain the impact of various internal and external factors and to predict future trends; to provide a framework for comparing alternative forecasting models for validity, accuracy, and feasibility; to enhance an appreciation for the limitations of forecasting models; to provide exposure and experience in using analytics software to develop forecasting models; and to develop skills at communicating statistical concepts, methods, results, and inferences effectively in a managerial context. Teamwork and professional presentation of analysis and recommendations will be required during this course. It emphasizes the use of the latest technological methods (Minitab and RStudio) for prediction.
QTM7571 - MACHINE LEARNING METHODS FOR BUS
MACHINE LEARNING METHODS FOR BUS
QTM7571: Machine Learning Methods for Business
(previously titled Business Intelligence, Analytics & Visualization)
This course introduces statistical and machine learning methods for business intelligence. Given the ease of data collection and storage, extracting meaningful information from data has become an essential trait for competitiveness, for companies large and small. In this course, you will learn a variety of statistical and machine learning methods that companies use to turn data into insights. You will get hands-on experience with the implementation of statistical and machine learning methods, from data pre-processing to generating predictions, and evaluating model accuracy. Your learnings will be in practical contexts, such as:
Predicting prices of homes as provided by the leading real estate database company realtor.com
Predicting the statuses of the loans for the peer-to-peer loan lending company LendingClub
Building a recommendation engine for the music website Last.fm
The various methods covered in this course will be implemented using the RStudio coding language. No prior knowledge of RStudio is required.
Prerequisite: QTM 7200 OR QTM7800
QTM7580 - INDEPENDENT RESEARCH
******Independent research is available for all academic divisions. Registration is manual for students through Graduate Eform approved by a faculty and Office of Graduate Academic Services******
Independent Research provides an opportunity to conduct in-depth research in areas of a student's own specific interest. Students may undertake Independent Research for academic credit with the approval of a student-selected faculty advisor, the appropriate division chair, and Graduate Academic Services. Please note that a student is responsible for recruiting a faculty advisor through the student's own initiative and obtain the advisor's prior consent/commitment before applying for an independent research project.
Authorization for such a project requires submission of a formal proposal written in accordance with standards set forth by the Graduate Academic Services. The research project normally carries 1.5 or 3 credits.
QTM7800 - BUSINESS ANALYTICS
QTM7800 Business Analytics
**If you have taken and passed QTM7200 you cannot register for QTM7800 as these two courses are equivalent**
In the BA stream of the course, regression models are used to understand dependence relations and thereby improve the accuracy of predictive modeling. Sensitivity analyses are used to determine which factors drive our decisions, and, thus, determine which factors need to be carefully managed. In the OIM stream of the paired course, strategic tradeoffs are discussed to understand the operations and information models for a variety of settings (e.g., startups, nascent or established organizations) and thereby improve any model by utilizing resources (e.g., physical assets, people, data, digital technologies, markets) and processes for the flow of goods, people and information.
QTM9510 - OPTIMIZATION METHODS AND APPLICATIONS
OPTIMIZATION METHODS AND APPLICATIONS
QTM9510: Optimization Methods and Applications
This course provides an introduction to optimization models and their applications to a variety of business decision problems. Linear, integer, and network models will be discussed. Emphasis will be placed on understanding the relevant applications for various models, the strengths and limitations associated with the models, using software tools to solve optimization problems, and interpreting and analyzing solution results. In-class analysis of problems and/or cases will be utilized to reinforce material covered in lecture. Teamwork outside the class will be required in order to complete assignments. Evaluation will be based on both individual and team performance.
QTM9515 - INTRODUCTION TO DATA SCIENCE
INTRODUCTION TO DATA SCIENCE
QTM9515: Introduction to Data Science
(Previously titled Introduction to Data Science and Business Analytics)
1.5 credit Intensive Elective
This course is an introduction to data science the science of iterative exploration of data that can be used to gain insights and optimize business processes. The course is set up as a journey through the data analytics lifecycle of a project based on an actual company, and introduces predictive analytics techniques in the context of real-world applications from diverse business areas. A map of applications and an overview is provided for advanced methods for data visualization, logistic regression, decision tree learning methods, clustering, and association rules. The course utilizes the advanced visualization software Tableau, the free open-source statistical modeling language R, and various other tools like cloud computing to gain insights from data. The case studies include data sets from a variety of industries and companies, including financial planning startups, online retailers, telecommunications companies, and healthcare organizations.
Prerequisites: QTM7200 or QTM7800