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
decomposition and singular value decomposition.
MSAN 503 –
Introduction to Data-Driven Business Strategies (1 credit) An introduction to
the history of “big data” and four ideas driving the revolution in data
analytics: volume, velocity, variety, and veracity. Students will read current
newspaper and journal articles, listen to guest speakers, and complete case
studies. After finishing this gateway course, students should understand how
businesses, governments, and not-for-profit institutions are creating
stakeholder value by more effectively capturing, curating, storing, searching,
sharing, analyzing, and visualizing data.
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 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.
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.
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 of 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.
605 – Practicum I (1 credit) Provides both skills and experience in working with clients
and opportunities to practice the professional skills required by business. The
course features frequent presentations by program partners about real
analytical problems and how they are addressed. The course features significant
one-on-one mentoring and integration of topics presented in program’s courses.
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.
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, business, and
economics. This course will particularly emphasize the creation of presentation
slides and other supporting materials, the correct use of data visualization
techniques, and learning how to listen to and critically evaluate presentations
made by other students.
621 – Machine Learning (2 credits) Algorithms to
classify unknown data and make predictions. Support Vector
Machines, kNN, Naive Bayes, association rules (a priori
algorithm), decision trees, feature selection, classifier
accuracy measures, Neural networks.
MSAN 622 – Data and Information Visualization (2 credits) This course will
address basic information and data visualization techniques, as well as design
principles. Students nwill primarily use R with the ggplot2 and shiny packages
to prototype visualizations. Students will obtain practical experience
with the presentation of complex visual data, including multivariate data,
geospatial data, textual data, and networks and data.
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 factor analysis,
linear and nonlinear discriminant analysis, ANOVA and MANOVA, regression
with longitudinal data, repeated measures ANOVA, and both hierarchical and
k-means cluster analysis. Statistical packages include R and SAS.
MSAN 624 – Marketing
Analytics (2 credits) In this course,
students will learn how companies harness their digital marketing data to drive
insights that convert into better customer experiences. Topics may include
survival analysis, longitudinal data analysis, heat maps, geographic
information systems, fraud detection, and market basket analysis. Areas of
application may include customer targeting, election management, and
625 – Practicum II (2 credits for
the semester) Students are placed with a client as part of a
semester-long project with weekly deliverables and meetings. Continued
mentoring and development of professional business skills are also provided.
MSAN 630 – Text Mining (2 credits) Deriving information
such as sentiment from unstructured text like tweets or web documents. Distance
measures for documents and email messages. Application of clustering and
classification algorithms to high-dimension feature spaces from text documents.
631 – Analytics for Social Networks (2 credits) This course introduces the fundamental concepts and methods underlying the field of social network analysis including network centrality, cohesive subgroups, structural and role equivalence, visualization and hypothesis testing. Emphasis is on students learning from analyzing data and answering empirical questions using routines written in R.
632 – Practicum III (1 credit) Continuation of
Practicum. Students also receive “soft skills” training in creating their CV,
interviewing and networking, and study of the venture capital and startup
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.
MSAN 691 –
Distributed Databases (1 credit) Students create a
distributed MongoDB cluster study partitioning strategies such as sharding and
horizontal partitioning. Topics include SQL and NoSQL queries and data
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.
MSAN 693 –
Exploratory Data Analysis (1 credit) In this introductory
course, students will learn to perform basic data exploration techniques in
both R and Python, as well as manipulate unstructured text in these two
environments. Students will learn elementary techniques for visualizing
and exploring patterns in data while practicing basic presentation skills.
Furthermore, students will understand basic text classification
techniques, implement algorithms for sentiment analysis, and evaluate
and compare classification algorithms.
694 – Distributed Computing (1 credit) Big data does not fit
on a single machine and analysts must resort to clusters of machines
cooperating to compute results. This course introduces students to map-reduce
systems such as Hadoop and domain specific languages such as PIG. Students
learn to re-express programs as map-reduce jobs and present them to environment
such as Amazon's "Elastic Map-Reduce."
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.
696 – Interview Skills (1 credit) Students learn how to
prepare for an interview, successfully answer questions in interviews, and how
to present themselves. Labs include interviews and answering technical
questions quickly and accurately.