Master of Business Statistics

Computer Programming for Business Statistics I
This course is an introduction to the principles and techniques of computer programming using R, and an introduction to a variety of numerical and computational problems. Topics include functions, recursion, loops, list comprehensions, reading and writing files. Students will also learn how to program in R as well as how to use R for effective data analysis. The course covers practical issues in statistical computing which includes programming in R, reading data into R, and accessing R packages. Topics in statistical data analysis will provide working examples.

Statistical Modeling
This course is an introduction of statistical methods and multiple linear regression. Applications to finance, social science, and marketing data are emphasized. Topics include principal components analysis, factor regression, linear and quadratic discriminant analysis, ANOVA and MANOVA, and various clustering techniques (k-means, hierarchical, spectral, total variation, etc.). Statistical packages may include R and SAS.

Predictive Analytics
The Predictive Analytics course is fundamentally about decision support. In this course, students will learn to apply tools and techniques such as regression analysis, logistic regression, decision trees, and time series analysis to problems which are strategically critical to most businesses. Students will gain experience predicting future events in the areas of marketing, finance, and operations management. By the end of the course they will be positioned as indispensable business partners to executive decision-makers across a variety of contexts.

Data Strategy & Visualization
We are living in an age of information overload. Large amount of data are generated by human, computers, and instruments, and much of the data are available on the internet. Today’s big challenge is not about generating data, but how to analyze them. While looking at numbers and texts can provide insights, the enormous data size often makes this task impractical. One solution is to use data visualization tools to convert the numbers and texts into pictures or interactive visual presentations. This course covers the basic theories of data visualization, such as data types, chart types, visual variables, visualization techniques, structure of data visualization, navigation in data visualization, color theory, and visualization evaluation. Students will discover strategies for simplifying data overload and learn visual representation methods and techniques that increase the understanding of complex data and models. Emphasis will be placed on the identification of patterns, trends and differences from data sets across categories, space, and time using tools such as Tableau and Google Charts.

Computer Programming for Business Statistics II
This course is an introduction to the principles and techniques of computer programming using Python, an object- oriented computer language that is an ideal combination of power and simplicity, and an introduction to a variety of numerical and computational problems. Topics include importing websites, generating random numbers, the method of inverse transformations, acceptance/ rejection sampling, gradient descent, bootstrapping techniques, matrix and vector operations, and graphics. In this class students will learn to learn Python in a hands-on way, through tutorials and weekly homeworks that challenge the student to break down problems into manageable units. The emphasis in this course is on practical implementation, not on computational aesthetics.

Optimization
This course will introduce the students to the theory, algorithms, and applications of optimization. The optimization methodologies include linear programming, network optimization, integer programming, and decision trees. This course will also cover modeling methodology (linear, network, integer, nonlinear programming, and heuristics), modeling tools (sensitivity analysis), software, and applications in production planning and scheduling, inventory planning, supply network optimization, project scheduling, telecommunications, and finance. Students will study the mathematics underlying the optimization methods, and at the same time, considerable attention will also be paid to the numerical aspect of the subject. Interesting and important applications from computer vision and machine learning will also be discussed in the course.

Machine Learning
This course focuses on the core theory and application of classification and clustering techniques, feature selection, and performance evaluation. Algorithms discussed include logistic regression, support vector machines (SVM), k-Nearest Neighbors (kNN), Naive Bayes, association rules (a priori algorithm), decision trees, neural networks, clustering, and ensemble methods. Using tools available in Python and R, this course also provides a broad introduction to machine learning, data-mining, and statistical pattern recognition. Topics include: supervised and unsupervised learning, as well as best practices in machine learning. The course will also draw from numerous case studies and applications, so that students will gain experience with application of the theory to key predictive and descriptive analytics problems in business intelligence. Special attention is drawn to practical issues such as class imbalance, noise, missing data, and computational complexity.

Data Mining
This course will introduce students to applications and issues in data mining, Online Analytical Processing (OLAP), data warehousing, association rules, classification, different approaches for classification, prediction, clustering, outlier analysis, mining spatial databases, temporal databases, mining time series and sequence data, and web mining. The course explores the concepts and techniques of data mining, a promising and flourishing frontier in database systems. The course covers data mining tasks like constructing decision trees, finding association rules, classification, and clustering. The course is designed to provide students with a broad understanding in the design and use of data mining algorithms and provide a holistic view of data mining.

Applied Data Analytics
This course will be project-oriented and hands-on. It focuses on data analytics for information security and decision-making in distributed and parallel processing environments. Through the course, students will be better prepared to apply their new knowledge into real-life, data-intensive, research situations with problems drawn from marketing, finance, and operations applications.

Data Analysis Project
The Data Analysis Project is an applied capstone course of the program. Students will grapple with a strategically critical problem, and they will need to determine a data strategy, select appropriate tools for analyses, and interpret and communicate results in order to be successful. Students will work in teams under the guidance of faculty coaches and outside business challengers and stakeholders. By the end of the course students will have tested themselves as independent problem solvers ready to grapple with real-world problems in a practical way.

Storytelling Through Data
Impactful analysis begins with mastery of data analytics, but it is not complete until meaning is communicated clearly, intelligently, and persuasively. This course will give students strategies, tips, tools and techniques to translate analysis into coordinated action through effective storytelling. Students will learn to combine analytic, visualization, and presentation approaches in powerful ways and apply their skills to real-world management problems and situations.

Business Intelligence
This course will introduce the students to deep examination of business analytics methods of visualization, data mining, text mining and web mining using various analytical tools. This course will shed light on how business intelligence systems combine operational data with analytical tools to present complex and competitive information to planners and decision-makers. Students will learn how to improve the timeliness and quality of inputs to the decision process, as well as understand the capabilities available in the firm; the future directions in the markets, the technologies, and the regulatory environment in which the firm competes; they will also learn to focus on competitors and the implications of these actions.

Data Science & Ethics
This course will introduce students to the history and fundamentals of data science with an emphasis on the possibilities and limitations of data scientific approaches to human problems. Important questions will be asked alongside a broad overview of data science tools and applications. Who owns the data? How is privacy valued and protected? What disclosures are data users owed? Can algorithms be trusted, and, to the extent they are fallible, what is the responsibility to improve their accurate interpretability?