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Ion Nemteanu

Electives: Focus Your MSBA Experience

Our elective courses are a tool for you to focus your educational experience on your personal and professional goals. Rady's electives provide you with the knowledge and skills critical for working in innovative industries.

Elective Courses:

MGTA 402 - Data-Driven Communications (2)

This project-based course explores and develops applied communication skills needed for analytical professions to make the business case. Students work individually and in teams to develop persuasive messaging to communicate complex technical information and business insights to, and between, technical experts and business decision-makers.

MGTA 414 - Web Data Analytics (2)

This course focuses on collecting, drawing inference, and making business decisions based on data. It will cover tools — Python, APIs, and NLP — to collect, manipulate, and analyze data from the web and other sources, with the objective of making students data savvy and comfortable with deriving insights from real-world, large datasets. Students will be exposed to the power of clickstream analysis and the possibilities that can be unleashed from industry applications of web data analytics.

MGTA 415 - Analyzing Unstructured Data (4)

Unstructured data such as text, images and video are routinely collected by companies and other organizations. The complex nature and scale of data such as this requires specialized analytics frameworks. In this course we discuss and use tools that empower decision makers to extract actionable

MGTA 456 - Supply Chain Analytics (4)

This course focuses on improving the performance of production and service operations, as well as supply chains, through the combination of data and analytical tools including statistics, forecasting, and optimization. Students will learn to employ analytics in capacity and distribution facility planning and contracting; how to determine data collection requirements for dynamic management of inventory levels; and how to improve revenue management under demand learning. Prerequisites: MGTA 451, MGTA 452, MGTA 453, and restricted to master’s of business analytics students or with department and instructor approval.

MGTA 457 - Business Intelligence Systems (2)

In this course we will cover the principles of designing and building dashboards and data visualizations. We will leverage Tableau's library of resources to demonstrate best practices for data visualization and data storytelling.

MGTA 458 - Experiments in Firms (4)

Students will learn to design and run experiments to guide policy and business decisions. They will learn to distinguish between a correlation and a causal effect and also to critically evaluate causal claims based on nonexperimental data. Prerequisites: MGTA 451, MGTA 452, MGTA 453, and restricted to master’s of business analytics students or with department and instructor approval.

MGTA 459 - Managerial Judgment and Decision-Making (4)

Decision making is difficult with scarce resources, inexact information, and conflicting stakeholder agendas (i.e., most of the time). Students will use advanced behavioral research to gain a competitive advantage, by improving their decisions and their understanding of how others decide. Prerequisites: MGTA 451, MGTA 452, MGTA 453, and restricted to master’s of business analytics students or with department and instructor approval.

MGTA 460 - Business Analytics Project Management (2)

Many projects fail to produce valuable results, are over budget, or not finished on time. This course will teach students how to set and manage goals and expectations of the team and of the executive sponsors for business analytics projects. Prerequisites: MGTA 451, MGTA 452, MGTA 453, and restricted to master’s of business analytics students or with department and instructor approval.

MGTA 461 (CSE 258) - Web Mining and Recommender Systems (4)

This course is devoted to current methods for recommender systems, data mining, and predictive analytics. No previous background in machine learning is required, but all participants should be comfortable with programming (all example code will be in Python), and with basic optimization and linear algebra.

For additional information see:

MGTA 462A - Big Data Technology & Business Application (2)

Analyzing large-scale and complex data is an integral part of many business applications today. This course is designed to provide students with the skills of handling large volumes of data through database systems. The students will learn fundamental operations like data cleaning and the extract-transform-load (ETL) process that collects data from external sources into a database, advanced levels of SQL to perform complex queries as well as in-database analytics, and principles of data warehouse design for enterprise-level data analytics.

MGTA 462B - Big Data Technology & Business Application (2)

Analyzing large-scale and complex data is an integral part of many business applications today. This course is designed to provide students with skills and knowledge to perform analytics at scale. Students will learn the steps in the analytics process used to gather, prepare, process, and evaluate data. Skills and technologies to perform scalable analytics on big data using distributed computing and cloud-based tools will be the focus of the course. Hands-on experience will be provided with the Spark distributed platform and AWS cloud-based analytics. Through assignments and a project, students will apply the tools and techniques they learn in the course to leverage big data.

MGTA 463 - Fraud Analytics (4)

In this course you will learn everything you need to know to build statistical/machine learning models to find fraud in data events. We will cover the basics of building fraud models: understanding the problem dynamics, modes of fraud, data analysis and preparation, building expert variables/features, different algorithm choices, and measurements of model goodness. There will be five homework problems and two group projects, with group sizes typically four to six people. We will talk through in detail several examples of fraud problems and solutions, including tax fraud, application identity fraud, healthcare claims fraud, and credit card payment fraud. We will cover the topic of statistical modeling with a drill-down into all steps to building machine learning models. There will be a high-level discussion of a wide variety of statistical modeling methods, including linear/logistic regressions, clustering, neural nets, trees, SVM, random forests, deep learning, boosting and bagging. The overall fraud solution emphasis will be on creating expert variables/features which are the key to building successful fraud algorithms. For each of the two projects the teams will build the fraud algorithms and prepare a presentation and a proper business report, which can then be useful tools for future job interviews. Students are expected to have good backgrounds in math and statistics, and a working ability in any modern programming language that can interact with open source machine learning algorithms, such as R or Python.

MGTA 479 - Pricing Analytics (4)

This class covers the key drivers for making data-driven pricing decisions. Price setting is one of the most crucial of all tactical marketing decisions. It involves a detailed understanding of both supply-side (e.g. costs) and demand-side factors (e.g., consumer willingness to pay). In this course, we will approach the pricing decision as an intersection of economic, strategic, and behavioral considerations. We will cover the data required and analytical techniques to make optimal pricing decisions. We will cover tools and techniques for primary (e.g., survey) data as well as for secondary data (e.g., POS scanner data). The course will consist of lectures, where we will discuss the theory of pricing, and in-class labs, where we use data to determine the appropriate price to charge. The class will be heavily analytical and a good working knowledge of R, an understanding of regressions, and predictive modeling are required.

Electives in Addition to Business Analytic Courses (up to 16 units permitted)

CSE 250B - Principles of Artificial Intelligence: Learning Algorithms (4)

This course covers algorithms for supervised and unsupervised learning from data. Content may include maximum likelihood, log-linear models including logistic regression and conditional random fields, nearest neighbor methods, kernel methods, decision trees, ensemble methods, optimization algorithms, topic models, neural networks and backpropagation.

CSE 253 - Neural Networks for Pattern Recognition (4)

Neural networks have come back into fashion recently with the advent of deep networks, which are winning all of the most important computer vision contests, and have also been used in a number of other pattern-recognition and pattern transformation problems, and have become the method of choice in reinforcement learning. In this course, we begin with the fundamentals of neural networks: We introduce Perceptrons, linear and logistic regression, multilayer networks and back-propagation, convolutional neural networks, recurrent networks, and deep networks.

CSE 256 - Statistical Natural Language Processing (4)

Natural language processing (NLP) is a field of AI which aims to equip computers with the ability to intelligently process natural (human) language. This course will explore statistical techniques for the automatic analysis of natural language data. Specific topics covered include: probabilistic language models, which define probability distributions over text sequences; text classification; sequence models; parsing sentences into syntactic representations; machine translation, and machine reading.

MGT 451 - Technology and Innovation Strategy (4)

Outlines tools for formulating and evaluating technology strategy, including an introduction to the economics of technical change, models of technological evolution, and models of organizational dynamics and innovation. Provides an understanding of how technology firms gain and sustain competitive advantage. Students may not earn credit for both MGT 451 and MGT 271. They are course equivalents. Prerequisites: admission to MBA program or consent of instructor.

MGT 475 - Research for Marketing Decisions (4)

Marketing research is the process by which firms acquire and synthesize information about their customers and competitors. Managers use marketing research in the face of uncertain decisions so they can take actions that will lead to better outcomes. This course has a particular emphasis on information acquisition in the digital and automated age and statistics will feature prominently. Equally emphasized in this course is the development of skills to take in information and translate it to a sensible business strategy. Methods taught and applied to marketing research include hypothesis testing, clustering, factor analysis, multidimensional scaling, causal inference with panel data, A/B testing, and experimental design.

MGT 477 - Consumer Behavior (4)

The course identifies the factors that influence the selection and usage of products and services. Students will be introduced to problems/ decisions that include evaluating behavior; understanding the consumers’ decision process, and strategies to create desirable consumer behavior. Students may not earn credit for both MGT 477 and MGT 203. They are course equivalents. Prerequisites: Admission to MBA program or consent of instructor.

MGT 489 - E-commerce

In this class students will create prototypical e-commerce businesses. We proceed through market selection, sourcing, customer and market research, differentiation, positioning, branding, store design, user experience testing, pricing, social media, content marketing and digital advertising. Data-driven decision making is emphasized at every stage.

MGTF 405 - Business Forecasting (4)

Introduction to state-of-the-art forecasting methods in finance. Students will learn to estimate forecasting models based on past values of the predicted variable(s), surveys, market information, and other economic data. Participants will become critical consumers of forecasts reported in the media. Letter grades only. Prerequisites: Restricted to master of finance program, MBA program, or by consent of instructor.

MGTF 406 - Behavioral Finance (4)

Develop theories of behavior motivated by psychology to describe various features of financial markets. Examine how the insights from behavioral finance complement the traditional paradigm and shed light on investors’ trading patterns, the behavior of asset prices, and corporate finance. Letter grades only.