Practitioners Seminar
This upcoming fall, we are going to kick off the “Practitioners Seminar” course, where successful practitioners from various industry fields (tech, finance, insurance, pharmaceutical, etc..) will have a chance to meet our students and present the projects they work on, technologies they utilize to achieve their goals, solutions they came up with etc. In addition, guest speakers will share their career development path (what kind of obstacles they faced, what pitfalls to avoid, and in general give advice on career development in their fields). We will finish up the meeting with a Q&A session with students.
- F 4:10 p.m.-6:40 p.m.
- 0 points
MA Mentored Research
MA MENTORED RESEARCH
This course is intended to provide a mechanism to MA students in Statistics who undertake on-campus project work or research. The course may be signed up with a faculty member from the Department of Statistics for academic credit. Students seeking to enroll in the course should identify an on-campus project and a congenial faculty member whose research is appealing to them, and who are able to serve as their mentor. Students should then submit an application to enroll in this course, which will be reviewed and approved by the Faculty Director of the MA in Statistics program.
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Instructor:
Gabriel Young
CULPA: 7 Info - 0-3 points
Statistical Fieldwork
Prerequisites: GR5203; GR5204 &GR5205 and at least 4 approved electives This course is an elective course for students in the M.A. in Statistics program that counts towards the degree requirements. To receive a grade and academic credits for this course, students are expected to engage in approved off-campus internships that can be counted as an elective. Statistical Fieldwork should provide students an opportunity to apply their statistical skills and gain practical knowledge on how statistics can be applied to solve real-world challenges.
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Instructor:
Demissie Alemayehu
h-index: 14 Info - 1 points
STAT INFERENCE & MODELING
Prerequisites: (STAT GR5701) working knowledge of calculus and linear algebra (vectors and matrices), STAT GR5701 or equivalent, and familiarity with a programming language (e.g. R, Python) for statistical data analysis. In this course, we will systematically cover fundamentals of statistical inference and modeling, with special attention to models and methods that address practical data issues. The course will be focused on inference and modeling approaches such as the EM algorithm, MCMC methods and Bayesian modeling, linear regression models, generalized linear regression models, nonparametric regressions, and statistical computing. In addition, the course will provide introduction to statistical methods and modeling that addresses various practical issues such as design of experiments, analysis of time-dependent data, missing values, etc. Throughpout the course, real-data examples will be used in lecture discussion and homework problems. This course lays the statistical foundation for inference and modeling using data, preparing the MS in Data Science students, for other courses in machine learning, data mining and visualization.
- TR 4:10 p.m.-5:25 p.m.
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Instructor:
Dobrin Marchev
Info - 3 points
MA Research Specialization in Statistics
Research Specialization/S
Each year, approximately 10–15% of MA in Statistics students participate in on-campus academic research, contributing to advances in statistical methodology and applied areas. Some projects demonstrate exceptional promise and benefit from additional time and support for further development. The MA Research Specialization in Statistics allows qualified students to extend their MA program to a fourth semester to continue their research under the supervision of a faculty mentor. This competitive, merit-based program requires demonstrated research progress, a nomination from a faculty mentor, and an outstanding academic record. STAT GR5999 serves as the course through which students admitted to the MA Research Specialization fulfill their research requirements.
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Instructor:
Gabriel Young
CULPA: 7 Info - 6 points
APPLIED STATISTICS II
Prerequisites: STAT GR6101 Continuation of STAT GR6101.
- TR 10:10 a.m.-11:25 a.m.
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Instructor:
Bianca Dumitrascu
Info - 4 points
COMPUTATIONAL STATISTICS
- TR 11:40 a.m.-12:55 p.m.
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Instructor:
Christopher Harshaw
Info - 4 points
Statistical Consulting
Prerequisites: STAT GR6102 or instructor permission. The Deparatments doctoral student consulting practicum. Students undertake pro bono consulting activities for Columbia community researchers under the tutelage of a faculty mentor.
- F 12:10 p.m.-2:40 p.m.
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Instructor:
Tian Zheng
CULPA: 13
Wikipedia
h-index: 25
Info
- 3 points
STATISTICL INFERENCE THEORY II
Prerequisites: STAT GR6201 Continuation of STAT G6201
- MW 2:40 p.m.-3:55 p.m.
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Instructor:
Marco A Avella Medina
Info - 4 points
PROBABILITY THEORY II
Prerequisites: STAT GR6301. Conditional distributions and expectations. Martingales; inequalities, convergence and closure properties, optimal stopping theorems, Burkholder-Gundy inequalities, Doob-Meyer decomposition, stochastic integration, Itos rule. Brownian motion: construction, invariance principles and random walks, study of sample paths, martingale representation results Girsanov Theorem. The heat equation, Feynman-Kac formula. Dirichlet problem, connections with potential theory. Introduction to Markov processes: semigroups and infinitesimal generators, diffusions, stochastic differential equations.
- TR 4:10 p.m.-5:55 p.m.
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Instructor:
Ioannis Karatzas
CULPA: 7 h-index: 58 Info - 4 points
SEM-THEORETICAL STATISTIC
Departmental colloquium in statistics.
- M 4:10 p.m.-6:40 p.m.
- 3 points
SEMINAR PROBABILITY THEORY
Departmental colloquium in probability theory.
- F 2:10 p.m.-4:40 p.m.
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Instructor:
Ivan Corwin
CULPA: 1 Wikipedia h-index: 34 Info - 3 points
SEMINAR IN APPLIED PROB & RISK
A colloquiim in applied probability and risk.
- R 1:10 p.m.-3 p.m.
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Instructor:
Graeme Baker
Info - 1 points
SEM IN MATHEMATICAL FINANCE
A colloquium on topics in mathematical finance
- R 4:10 p.m.-6:40 p.m.
- 3 points