Prerequisites: STAT GU5204 and STAT GU5205
Open to MA students in Statistics only
Introduction to programming in the R statistical package: functions, objects, data structures, flow control, input and output, debugging, logical design, and abstraction. Writing code for numerical and graphical statistical analyses. Writing maintainable code and testing, stochastic simulations, paralleizing data analyses, and working with large data sets. Examples from data science will be used for demonstration.
Prerequisites: STAT GR5206 or the equivalent.
Open to MA students in Statistics only
The course will provide an introduction to Machine Learning and its core models and algorithms. The aim of the course is to provide students of statistics with detailed knowledge of how Machine Learning methods work and how statistical models can be brought to bear in computer systems - not only to analyze large data sets, but to let computers perform tasks that traditional methods of computer science are unable to address. Examples range from speech recognition and text analysis through bioinformatics and medical diagnosis. This course provides a first introduction to the statistical methods and mathematical concepts which make such technologies possible.
Prerequisites: STAT GR5204 or the equivalent. STAT GR5205 is recommended.
Open to MA students in Statistics only
A fast-paced introduction to statistical methods used in quantitative finance. Financial applications and statistical methodologies are intertwined in all lectures. Topics include regression analysis and applications to the Capital Asset Pricing Model and multifactor pricing models, principal components and multivariate analysis, smoothing techniques and estimation of yield curves statistical methods for financial time series, value at risk, term structure models and fixed income research, and estimation and modeling of volatilities. Hands-on experience with financial data.
Prerequisites: the M.A. program adviser's permission.
For unpaid internships only. Students seeking academic credit for unpaid internships should enroll no later than the second week of the semester. Students must confer with the MA Program Advisor at the time of enrollment to determine the requirements for successfull completion. The Department does not assist students to obtain unpaid internships.
In this course students learn the principles of management as they relate to enterprise-wide information and knowledge services. Attention is given to the philosophy and history of information and knowledge services, specifically as this background affects students’ future performance as managers and leaders in the workplace. The focus is on management and leadership skills, knowledge sharing, and the role of knowledge strategy in strengthening the corporate knowledge culture.
Prerequisites: all 6 MAFN core courses, at least 6 credits of approved electives, and the instructor's permission. See the MAFN website for details.
This course provides an opportunity for MAFN students to engage in off-campus internships for academic credit that counts towards the degree. Graded by letter grade. Students need to secure an internship and get it approved by the instructor.