MSAN 501 – Computation for Analytics (1 credit) An intense review of
the elementary aspects of computer programming using both R and Python, and an
introduction to a variety of numerical and computational problems. Topics
include functions, recursion, loops, list comprehensions, reading and writing
files, importing web sites, generating random numbers, the method of inverse
transformations, acceptance/rejection sampling, gradient descent, bootstrapping
techniques, matrix and vector operations, and graphics.
MSAN 502 – Review of Linear Algebra (1 credit) An intense review of linear algebra. Topics include matrix operations, special matrices, linear systems of equations, the inverse matrix, and determinants; vectors, subspaces, linear independence, basis and dimension, row space, column space, rank, and the rank-nullity theorem; eigenvectors, eigenvalues, computational methods for finding eigenvectors and eigenvalues, and diagonalization of matrices; the LU, spectral, and singular value decompositions.
MSAN 504 – Review of Probability and Statistics (1 credit) An intense review of
elementary probability and statistics. Topics include random variables,
probability mass functions, density functions, the cumulative distribution
function, moments, maximum likelihood estimation, and the method of moments;
one- and two-sample hypothesis tests and confidence intervals involving
proportions, means, and correlation coefficients; the axioms of Kolmogorov,
independence, the law of total probability, and Bayes’ Theorem; and
multivariate distributions, indicator random variables, and conditional
MSAN 593 – Exploratory Data Analysis (1 credit) Before we can begin to apply rigorous statistical tools to data, we often need to approach our data intuitively, and look for meaningful associations and surprising patterns, detect outliers and anomalies, formulate hypotheses. This practice is commonly referred to as Exploratory Data Analysis (EDA). Successful exploratory data analysis depends on the ability to manipulate and visualize data. This class introduces various concepts in EDA with an emphasis on data manipulation in R.
MSAN 601 – Linear Regression Analysis (2 credits) This course is an
intensive introduction to linear models, with a focus on both principles and
practice. Examples from finance, business, marketing and economics are
emphasized. Large data sets are used frequently. Topics include simple and
multiple linear regression; weighted, generalized, and outlier-resistant least
squares regression; interaction terms; transformations; regression
diagnostics and addressing violations of regression assumptions; variable
selection techniques like backward elimination and forward selection, and
logit/probit models. Statistical packages include R and SAS. Prerequisites: MSAN 502, MSAN 504, MSAN 593.
MSAN 603 – Business Strategies for Big Data (2 credits) In this course,
students will read case studies and hear from guest speakers about challenges
and opportunities generated by the advent of “big data.” Students will
make group presentations and write critical response papers related to
these case studies. Students will consider some of the traditional business
frameworks (e.g., SWOT analysis) for evaluating the strategic
opportunities available to a company in the “big data” space. Prerequisites: MSAN 593, 691, 697.
MSAN 604 – Time Series Analysis for Business and Finance (2 credits) A survey of the theory and application of time series models, with a particular emphasis on financial and business applications (e.g., exchange rates, sales data, Value-at-Risk, etc.). Tools for model identification, estimation, and assessment are developed in depth. Smoothing methods and trend/seasonal decomposition methods are covered as well, including moving average, exponential, Holt-Winters, and Lowess smoothing techniques. Finally, volatility clustering is modeled through ARCH, GARCH, EGARCH, and GARCH-in-mean specifications. Statistical packages include R and SAS. Prerequisite: MSAN 601.
605 – Practicum I (1 credit) Student teams are placed with a client as part of a module-long analytics project with weekly deliverables and meetings. The course provides both skills and experience in working with clients and opportunities to practice the professional skills required by business. The course features significant one-on-one mentoring and integration of topics presented in the program's courses. Prerequisites: MSAN 501, 593, 601, 692.
610 – Business Communications for Analytics (2 credits) In this course, students will learn essential concepts related to business communication and, in particular, the communication of technical material both spoken and written. Students will learn how to competently create, organize, and support ideas in their business presentations. They will deliver both planned and extemporaneous public presentations on topics related to data analysis and business, both individually and in groups. This course will emphasize the creation of presentation slides and other supporting materials, the correct presentation and organization of data analysis results, and listening to and critically evaluating presentations made by other students. Prerequisite: MSAN 593.
621 – Introduction to Machine Learning (2 credits) 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, 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. Prerequisites: MSAN 501, 502, 504.
MSAN 622 – Data Visualization (2 credits) This course will address basic data visualization techniques and design principles. Students will use R with the ggplot2 and shiny packages to prototype visualizations. Students will obtain practical experience with the visualization of complex data, including multivariate data, geospatial data, textual data, time series, and network data. Prerequisites: MSAN 501, 593, 692.
MSAN 623 –
Multivariate Statistical Analysis (2 credits) This course trains students in the use of multivariate statistical methods other than multiple linear regression, which is covered in MSAN 601. Applications to finance, social science, and marketing data are emphasized (e.g., dimension reduction for Treasury yield curves and consumer microdata). Topics include principal components analysis, factor regression, linear and quadratic discriminant analysis, ANOVA and MANOVA, repeated measures ANOVA, and various clustering techniques (k-means, hierarchical, spectral, total variation, etc.). Statistical packages include R and SAS. Prerequisites: MSAN 601, 604.
625 – Practicum II (2 credits for
the semester) Continuation of Practicum. Student teams extend their existing analytics project or are reassigned to new projects with a client as part of a semester-long project with weekly deliverables and meetings. Continued one-on-one mentoring and development of professional business skills are also provided, with an emphasis on "soft skills" training in creating their CV, interviewing and networking. Over the course of the semester, student teams present their Practicum I projects to the other students. Prerequisites: MSAN 604, 605, 621.
MSAN 630 – Advanced Machine Learning (2 credits) A deeper exploration of the specific properties and algorithms used in machine learning and clustering, with special attention to cutting-edge extensions of the more basic techniques learned in MSAN 621. Examples include k means ++, regularized logistic regression, topic modeling, stratified k-fold sampling, partitioning around medoids, deep learning, etc. Using industry standard machine learning and natural language processing packages, students will learn how to efficiently implement advanced applications set in high-dimensional feature spaces, including text mining and image classification. Prerequisite: MSAN 621.
MSAN 631 – Special Topics in Analytics (2 credits) Topics will be selected from geographic information systems (GIS), political analytics, sports analytics, supply chain analytics, optimization and simulation, and marketing analytics. Prerequisites: MSAN 603, 623, 630.
632 – Practicum III (1 credit) Continuation of Practicum. Student teams extend their existing analytics project or are reassigned to new projects with a client as part of a module-long project with weekly deliverables and meetings. Selected student teams present their Practicum II projects over the course of the module to other students. Students also learn about the start-up process and the venture capital industry. Prerequisites: MSAN 603, 622, 623, 625, 630.
MSAN 690 –
Introduction to Programming in SAS (2 credits) In this course,
students receive a brief, intense, and focused review of programming in SAS
Enterprise Guide. This review will augment the SAS training that students
receive in other analytics courses, yet specifically prepare students to
take the SAS Base Programming examination. Prerequisite: MSAN 501.
MSAN 691 – Relational Databases (1 credit) Students study relational database management systems, with an introduction to the theory of normal forms. Students are introduced to mySQL as well as the Python mySQL API. Databases are provisioned and maintained on Linux servers as well as AWS-RDS and AWS-EMR. The goal of the course is to achieve proficiency in data retrieval and reduction with SQL. Mastery of command line tools is encouraged. Prerequisite: MSAN 501.
MSAN 692 – Data Acquisition (1 credit) Analysts spend the
majority of their time just collecting data and contorting it into an
appropriate or convenient form for analysis. In this course, students write
programs to scrape data from websites such as Yahoo finance and you use REST
APIs to extract data from Twitter. Topics also include log file
filtering, table merging, data cleaning, and data reorganization. Prerequisite: MSAN 593.
694 – Distributed Computing (1 credit) Students learn the MapReduce technique of distributed computing. The fundamental principals are first learned with the Python multiprocessing library, in which students build their own concurrent MapReduce framework. Considerable time is spent exploring practical application of mapping and reducing for various types of real world data. Distributed statistical and machine learning approaches are explored. Finally, Hadoop streaming MapReduce jobs (in Python) are launched on AWS-EMR. Prerequisite: MSAN 501.
695 – Web Analytics (1 credit) The study of website
traffic analysis for the purpose of understanding how visitors use a site or
services. Topics include Google Analytics, A/B testing, and the analysis
of incoming traffic characteristics such as client browser, language, computer
attributes, and geolocation. Prerequisite: MSAN 501.
696 – Interview Skills (1 credit) In this class, students learn how to prepare for technical interviews. We review varied information on how you can be successful in an interview through research, practical application of coursework, practice, interviewing tips and plenty of sample questions. Students work on their communication, presentation and technical solving problems skills in homework and mock interviews. Prerequisite: MSAN 610.
MSAN 697 – NoSQL Databases (1 credit) Students study key-value store through NoSQL with a focus on using MongoDB (including, possibly, pymongo, the Python Mongo API). Applications are used to motivate a disciplined approach to database programming with MongoDB, including the construction of indices. Prerequisite: MSAN 691.