C531: Predictive Analytics/Data Mining (3 credit hours)

This course on data mining and predictive analytics provides students with both the conceptual underpinnings of a broad variety of data mining models as well as experience with analyzing real data sets.

• Topics include: data preparation, cleaning, and exploratory analysis using data visualization and descriptive statistics; applications of multiple regression for numeric prediction; building predictive models using logistic regression, k-nearest neighbors, Naive Bayes, classification and regression trees, neural nets, discriminant analysis, advanced predictive techniques based on ensembles of predictions such as bagging and boosting, and selected time series forecasting methods; finding patterns in data using unsupervised models including principle components and cluster analysis; evaluating the performance of predictive models using training, validation, and testing data subsets as well as k-fold cross-validation; evaluating the performance of predictive and classification models using Receiver Operating Characteristic (ROC), lift charts and statistics on confusion matrices.
• The class sessions provide overview of the theory behind each model as well as demonstrations using Excel, Power BI, JMP Pro from SAS, and other tools.

C533: Data Warehousing & Visualization (3 credit hours)

High quality information is the key to successful management of businesses. Despite the large quantity of data that is collected by organizations, managers struggle to obtain information that would help them in decision making.

• This course covers a variety of fundamental technologies that enhance the management of data within organizations: relational models, SQL, NoSQL, data warehouse architecture and design, visualization, master data management, data profiling, and data integration and preparation.
• Those topics are bookended by discussions of a model for enterprise data management and data governance in the firm.
• Students will significantly enhance their understanding of both managerial and technical issues related to organizational data and its utilization.

C534: Simulation and Optimization for Business Analytics (3 credit hours)

This course focuses on building mathematical models for applied business situations using primarily optimization and simulation. The emphasis is on practical solution methods while gaining managerial insight.

• Topics include: math programming for linear, integer, and non-linear models where finding an optimal solution can be found for reasonably sized models; models for finding good solutions for combinatorial problems; simulation techniques to evaluate the risk of solutions over a range of scenarios; and methods for modeling time varying demand by identifying elements like trend and seasonality, and ultimately creating a practical forecast.
• Students will learn to create generic models, but use Microsoft’s Excel as the solution tool.
• By the end of the course, students should be able to build complete models from a written explanation including the data set, enter the model into Excel, solve the model, and then be able to offer insights and make managerial decision recommendations from their model.
   

C535: Developing Value through Business Analytics Applications (3 credit hours)

This course focuses on the application of analytical techniques to specific business applications and on application-specific interpretation of analytical models.

• Analytical techniques include Monte Carlo simulation, linear programming, nonlinear optimization, discrete optimization, linear regression, logistic regression, and data envelopment analysis.
• These models are constructed using a combination of Excel tools and Python.
• At the conclusion of the course, students will know how to structure spreadsheet models and basic programming models to complete analysis of business applications, as well as how to utilize Python libraries to analyze larger datasets.
  
   

After you complete the Kelley application, you’ll receive an email with instructions on how to apply through the Graduate CAS Application. During this Part 2 application, you will upload the following materials to complete your application.

• Resume—Provide a copy of your resume, summarizing your professional experiences and accomplishments.
• Personal Statement—Tell us what you want to achieve in this program in 500 words or less.
• Letter of Recommendation—Ask someone who knows your professional career to write a letter of recommendation for you. Provide the contact information of your recommender within the application. An email prompt will be sent to their email address, and they will be able to fill out a form and upload additional material. If you need alternative arrangements, please contact edp@@indiana.edu.
• Post-Secondary Transcripts—Provide copies of your transcripts from all of the post-secondary institutions you’ve attended. These copies can be sent to the program manager, or uploaded into the Part 2 Application.