Curriculum Overview

**In order to keep up with industry, classes are subject to change/update.

Course Descriptions:

BUAD 679 Leadership Development

The student will learn:
1. Teamwork and team effectiveness
2. Understanding Big Data
3. Ethics
4. Project Management

ANLY 605: Understanding and Visualizing Data with Modern Tools

Predictive analytics involves the use of data, visualization of data, statistical algorithms developed based on data, and meaningful insights derived from models and analysis for effective managerial decision making. The first step in business decision making is the ability to visualize data from every possible direction such that selection of tools and models to be used for analysis and understanding of data becomes easy. As such, to harness the power of data, this course combines lectures, interactive exercises, business case examples, and student participations in a holistic manner to develop the necessary skills for predictive modeling and to enhance the learning experience. This course provides a handson, practical approach to implementing predictive analytics as a tool to gain competitive advantage. Tableau and Power BI The primary approach will entail ‘learningbydoing’ with the use of the stateoftheart software such as Tableau and Power BI. The course is practically oriented with a primary focus on visualization of data and its application to answer critical business problems. The course will use charts and graphs to visualize a large amount of complex data and will show how to convey concepts and analyze decisions in a universal manner with different scenarios by making slight adjustments. The course will identify the areas that need improvement in a business environment, explore factors influencing business decisions (e.g., sourcing, pricing, locations, product type, financial status, resources), help
understand which products to place where and allow to predict business performance based on visualization. Overall, this course demonstrates how to leverage business data to design, develop and implement visualization techniques to enhance decisionmaking throughout an organization.
RProgramming In the second half of the course, students will learn to convey ideas and concepts from statistics, data mining, and data modeling, with the goal of extracting knowledge and actionable insights for business problems using R. They will develop data driven thinking to discover new knowledge from data, drive evidencebased solutions and make informed business decisions by extracting insights from data models. Students will have handson practice in R to apply data modeling concepts using real business datasets and data cases. The course will emphasize business decisionmaking based on data modeling, exploration and insights from data. The course will cover fundamental principles of predictive modeling and assessment criteria such as model fit, interpretation of model outcome, multicollinearity and variable selection.

ANLY 620:  Special Topics in Corporate Strategy, Negotiations, and Persuasion with data analytics

1. Develop your ability to think strategically.

2. Acquire familiarity with the central concepts, frameworks, and techniques of strategic management and negotiations.

3. Gain expertise in applying management concepts, frameworks and techniques in order to understand the reasons for positive and/or negative firm performance, generate strategic options for a firm, assess those options under conditions of imperfect information, and select and implement the most appropriate options or strategies.

4. Develop a greater understanding of the factors that facilitate and those that hinder effective negotiation and persuasion.

5. Improve analytical abilities in understanding the needs, concerns, motivations, and desires of other negotiators.

ANLY 615: Business Database Systems:

The primary objective of the course is to familiarize students with general database concepts, database design methodologies, and database implementation. In addition, the course introduces students to SQL and to Python as a computer coding language used to obtain data from social media.

MKTG 625 Marketing Engineering

This course will introduce students to a variety of datasets and teach them (hands on usage of) SAS to implement various quantitative techniques. This is an applied course that involves extensive use of data and PC-based analysis using JMP/SAS, a popular statistical software. The course will cover a number of quantitative analyses, explanatory and predictive models pertinent to marketing, such as customer segmentation, customer choice models, customer lifetime value, conjoint models, and market response models.

Course Goals
Understand how the “first principles” of marketing strategy helps firms organize the analytics opportunity and challenge in today’s data era, and use and execute data analytic techniques, and case studies to understand how to solve marketing analytics problems in a scientific and process-driven manner. Using statistical software to estimate various marketing models. Apply your learning through real database/marketing engineering cases and data.

Topics covered:
1. 4 Marketing Analytics Principles
2. JMP Orientation
3. Segmentation Concept Demo and Case
4. Targeting Concept
5. Choice Models Demo and Case
6. Customer Lifetime Value
7. Conjoint Concept Demo and Case
8. Satisfaction Analytics Concept and Case
9. Response Models Concept and Case

1. Introduction to relational databases and SQL
2. How to retrieve data from two or more tables
3. How to code summary queries
4. How to insert, update, and delete data
5. How to work with data types
6. How to work with functions
7. How to work with the views
8. Introduction to coding and coding standards and Python
9. Core objects, variables, input, and output in Python
10. Structures that control flow in Python
11. Functions in Python
12.Processing data in Python
13. Miscellaneous topics in Python

ANLY 608 Regression Analysis:

Prepare data for model fitting, fit appropriate regression models to business data from a wide variety of settings, interpret the output from regression models, identify weaknesses in models and formulate ways to overcome them, make valid inferences and draw business conclusions on the basis of the fitted models, and recommend business actions on the basis of these conclusions.

Topics covered in STAT 608
1. Regression methods based on least squares

2. Diagnostic methods including marginal model plots

3. Transformations and weighted least squares

4. Shrinkage and model selection techniques including lasso

5. Linear regression splines including MARS

6. Logistic regression

7. Poisson regression

8. Regression models with serially correlated errors

9. Bayesian approaches to linear and logistic regression

ANLY 626 Time Series Analysis

Prepare time series data for model fitting. Identify whether a time series exhibits the following properties: stationarity vs trend and/or seasonality. Fit appropriate models to time series data from a wide variety of business settings.

At the completion of the course, students will be able to:
1. Prepare time series data for model fitting
2. Identify whether a time series exhibits the
following properties:
a) Stationarity vs trend and/or
b) Outliers and/or level shifts
3. Fit appropriate models to time series data from
a wide variety of business settings
6. Interpret the output from models for time series data
7. Identify weaknesses in models for time series data
and formulate ways to overcome them
8. Make valid predictions of future values and draw
business conclusions on the basis of the fitted
models for time series data
9. Recommend business actions on the basis of
these conclusions

Topic list:
1. Introduction – Autocorrelation and stationarity vs trend
and/or seasonality
2. Autoregressive (AR) models
3. Moving average (MA) models
4. Autoregressive moving average (ARMA) models
5. Autoregressive integrated moving average (ARIMA)
6. Exponential smoothing
7. Regression models with autocorrelated errors
8. SAS Forecast Studio
9. Models for more than one time series
i. Transfer function models
ii. Multivariate time series models

ANLY 656 Applied Analytics (Machine Learning)

This course is an introduction to the general concepts and methodologies associated with Data Mining, Neural Networks, Machine Learning, and Analytics Modeling. Data Mining is the modeling and analysis of data, usually very large datasets, for decision making. Although several software packages used for Data Mining will be reviewed and compared, the primary concepts will be illustrated using SAS Enterprise Miner. Models discussed include neural networks; multiple and logistic regression; decision trees; and clustering algorithms.

1. The data mining process
2. Introduction to SAS Enterprise Miner
3. Data collection, exploration and pre-processing
4. Linear and logistic regression in Enterprise Miner
5. High performance Enterprise Miner
6. Comparing and evaluating big data models
7. Machine Learning: Decision Trees
8. Machine Learning: Random Forests
9. Ensemble modeling
10. Introduction to text analytics
11. Document classification & sentiment analysis
12. Topic analysis
13. Machine Learning: Neural networks & Random Forests.

ANLY 610: Deploy Enterprise Data Models & Optimization

This first section of this course focuses on model deployment in the enterprise. It will focus on how to deploy models both onpremises and in the clouds. Students will learn how to use models built in Python for prediction. We will cover Modern DevOps techniques and methodologies including Agile/Scrum, CI/CD pipelines and Git used by teams to build and deploy models in the enterprise. Students will learn how to build Docker containers and deploy models into Docker containers and how to deploy the containers into the enterprise and cloud. Students will learn how to deploy models in the Azure cloud ecosystems using Azure Functions, Azure Container Services, Kubernetes. We will see how to build and deploy models at scale in Azure Databricks which is built on Apache Spark. The second section focuses on formulating and solving mathematical optimization models for business problems. Students will learn how to build optimization models using the SAS/OR procedure OPTMODEL, which is an algebraic modeling language with control flow similar to the SAS DATA step. Students will write PROC OPTMODEL programs that read data from SAS data sets, build and solve an associated optimization model, and produce formatted output and data sets. We will see how linear, mixed integer, and nonlinear optimization models can be used to solve a variety of logistics and resource allocation problems. At the conclusion of the course students will be knowledgeable in building and deploying models in the enterprise and cloud, about optimization methodology and the use of the advanced SAS optimization capabilities.

• Gain insight into general capabilities of the software Learn how to predict using Python Learn how to develop and deploy models in the Enterprise Learn Agile/Scrum methodologies and DevOps including Git and CI/CD pipelines Learn how to build Docker Containers Learn how to deploy Python models into Docker Containers Learn how to deploy models in the cloud in Azure Learn how to build and deploy models at scale in Azure Databricks Learn capabilities of the SAS Data Step data handling capabilities. Understand the use of optimization modelling in a business context Use PROC OPTMODEL to perform and report the results of optimization studies

Past  Electives:

FINC 649 Financial Modeling

Learn how to build sophisticated financial models that analyze the impact of proposed corporate projects, investments, and other strategic decisions on shareholder value. Master the basic finance theory that underlies valuation models.

Throughout the class students will:
1. Fundamental principles underlying all financial
models, including the concept of free cash flow,
the time value of money and net present value.
2. The fundamentals of capital structure and
financing policy
3. The weighted average cost of capital and adjusted
present value methods
4. Building deterministic models for forecasting
financial statements and free cash flows
5. Incorporating uncertainty and simulation techniques
into financial statement and free cash flow
forecasting models
6. Fitting distributions for simulation of financial
time series and other stochastic processes
7. Estimation of the cost of equity and equity capital,
as well as the weighted average cost of capital
8. Incorporating real options into financial models
9. Applications

ISTM 601: Fundamentals of Business Programming

The ISTM 601 course provides an opportunity to build upon the introductory Python coding experience gained in the first semester (ISTM 615). Students will explore various publically available Python code libraries that are useful to data science (e.g., Pandas, NumPy, SciKit-Learn, Seaborn, etc.) and learn how to apply them in their analytics work. Other potential topics include web scraping and machine learning. Students will complete a data analysis project by writing Pyton code.

ANLY 610: Analytics for Financial Reporting

This course has a dual purpose. The first purpose is to gain a working knowledge of the context, concepts, and problems of using data to analyze financial reporting information from an internal and an external perspective. The second purpose is to develop skills in R that can be used for these problems. We will begin the course by discussing academic research that has analyzed external financial information (including accounting usefulness, earnings management, textual analysis, and trading anomalies) and at the same time will cover R basics and some useful packages (data importing and structuring, textual data basics, SQL in R, and simple dashboards) that will form the foundation for the course. For the rest of the course, we will use small case studies to apply different techniques to common data focused questions related to the information from financial systems and reports (correlations, clustering, linear and non-linear models, textual analysis).

  • Have a working knowledge of R including key packages (flexdashboard, dplyr, sqldf, stringr, ggplot2) and methods (clustering, linear models, regression trees, neural networks, textual analysis)
  • Understand the what, how, and why of data analysis with and related to financial reports.