Earn an online certificate in business analytics

In this course, we enhance the students' basic statistical and mathematical modeling skills covering the following topics:

• Probabilistic decision-making
• Regression analysis
• Forecasting
• Simulation models
• Optimization modeling with the EXCEL Solver

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.

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.

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.

Fill out this online application first.

After you complete the Kelley application, you’ll receive an email with instructions on how to apply through the Graduate CAS Application. During this stage, 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 familiar with 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, allowing them to fill out a form and upload the letter. If you need alternative arrangements, please contact keep@iu.edu.

• Undergraduate transcripts
  Provide copies of your transcripts from the undergraduate institutions you attended. Your transcripts can be uploaded into the Part 2 Application.

We conceptualized this program as a career enhancer, more than a tool for career transition. Undoubtedly, this program will deepen and widen your skill set and increase your marketability. However, we would encourage you to seek out informational interviews with individuals in your desired job to identify all the variables that will go into a prospective employer’s assessment of your viability for such a position.

We believe that successfully completing our program will make you a stronger candidate for (and improve your job performance in) any position that requires outstanding quantitative skills. For example, if you work in a finance or accounting role, our program will teach you how to develop more sophisticated financial models. If you work in a marketing or sales role, our program will allow you to analyze your results in greater depth and forecast more accurately. If you work in an operations or supply chain role, our program will allow you to run simulations and predictive models to anticipate potential issues.

As such, if you desire a position where quantitative skills are important, this program will provide you with the tools you need. However, please see our answer to the question above regarding the many variables that contribute to a candidate’s qualifications for a new role/industry.

We use Tableau in our courses. However, no prior experience is needed.

Broadly speaking, people who are comfortable with quantitative methods will have an easier time in this program. For example, if you took a statistics class as an undergraduate and did well, the Business Analytics Graduate Certificate will likely be a comfortable next step. Ultimately, only you can decide what is best for you, based on your prior experience, existing skill set, learning style, and long-term goals.

We will use Python in multiple classes. There is no expectation that you will already be familiar with it, however, prior exposure to programming concepts will be of additional benefit to you.