STAT GR5701 PROBABILITY ＆ STAT FOR DATA SC. 3.00 points.

This course covers the following topics: Fundamentals of probability theory and statistical inference used in data science; Probabilistic models, random variables, useful distributions, expectations, law of large numbers, central limit theorem; Statistical inference; point and confidence interval estimation, hypothesis tests, linear regression

STAT GR5702 EXPLORATORY DATA ANALYSIS/VISUAL. 3.00 points.

This course is covers the following topics: fundamentals of data visualization, layered grammer of graphics, perception of discrete and continuous variables, intreoduction to Mondran, mosaic pots, parallel coordinate plots, introduction to ggobi, linked pots, brushing, dynamic graphics, model visualization, clustering and classification

STAT GR5703 STAT INFERENCE ＆ MODELING. 3.00 points.

Prerequisites: (STAT GR5701) working knowledge of calculus and linear algebra (vectors and matrices), and STAT GR5203 or the equivalent.
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

CSOR W4246 ALGORITHMS FOR DATA SCIENCE. 3.00 points.

Prerequisites: COMS W1007 Basic knowledge in programming (e.g. at the level of COMS W1007), a basic grounding in calculus and linear algebra.
Corequisites: COMS W4121

Methods for organizing data, e.g. hashing, trees, queues, lists,priority queues. Streaming algorithms for computing statistics on the data. Sorting and searching. Basic graph models and algorithms for searching, shortest paths, and matching. Dynamic programming. Linear and convex programming. Floating point arithmetic, stability of numerical algorithms, Eigenvalues, singular values, PCA, gradient descent, stochastic gradient descent, and block coordinate descent. Conjugate gradient, Newton and quasi-Newton methods. Large scale applications from signal processing, collaborative filtering, recommendations systems, etc.

COMS W4121 COMPUTER SYSTEMS FOR DATA SCIENCE. 3.00 points.

Prerequisites: CSOR W4246 OR STAT W4203; or equivalent as approved by faculty advisor. background in Computer System Organization and good working knowledge of C/C++
Corequisites: CSOR W4246,STAT GU4203
An introduction to computer architecture and distributed systems with an emphasis on warehouse scale computing systems. Topics will include fundamental tradeoffs in computer systems, hardware and software techniques for exploiting instruction-level parallelism, data-level parallelism and task level parallelism, scheduling, caching, prefetching, network and memory architecture, latency and throughput optimizations, specialization, and an introduction to programming data center computers

COMS W4721 MACHINE LEARNING FOR DATA SCI. 3.00 points.

ENGI E4800 DATA SCIENCE CAPSTONE＆ETHICS. 3.00 points.

Not offered during 2024-2025 academic year.