Certification of Professional Achievement in Data Sciences

The inaugural class for the Certification program began the Fall 2013 term. This program is a great opportunity for individuals seeking continuing education to either strengthen their existing career prospects in environments where data science skills are valued, or as a means of embarking on a new career trajectory that takes advantage of the growing demand for a workforce with data science skills or knowledge

We currently accept applications for the fall term only. Applications for Fall 2015 admission will open in late September:  [Apply Here]

Application deadlines for our programs: February 15th. To learn more about the admissions application requirements, click here.

If you would like to learn more, or if you still have questions about the admissions application process, or the academic opportunities through the Data Science Institute, please sign up for one of our regularly scheduled online information sessions.

The tuition cost for the course(s) will default to the rate used by the School of Engineering. Please note this estimate is expected to change for each annual term. For more details on tuition and fees, click here.

Questions?  Please email admissions@idse.columbia.edu with any specific questions pertaining to the Admissions process.

Certification of Professional Achievement in Data Sciences

The Certification of Professional Achievement in Data Sciences program is jointly offered through The Fu Foundation School of Engineering and Applied Science and The Graduate School of Arts and Sciences at Columbia University. The Certification Program consists of the below four courses. Two courses are currently offered during the fall term and the remaining two courses are offered in the spring.  These courses are offered on campus and during weekday evenings.

The tuition cost for the course(s) will default to the Fall 2014-Spring 2015 tuition rate for the School of Engineering. Each credit will cost approximately $1,710.00 and the entire program (12 credits) will be $20,520.00.  Please note this estimate is expected to change for each annual term.  For more details on tuition and fees, click here.

Fall Course Offerings:

Algorithms for Data Science (3) CSOR W4246 - Introduction to the design and analysis of efficient algorithms, with an emphasis on data science. Topics include efficient sorting and searching, graph algorithms, dynamic programming, randomized algorithms, approximation algorithms, and NP completeness. In addition the course will cover material relevant to big data problems: for example models of parallelism, and hashing, sketching, and sublinear time algorithms.

Probability & Statistics (3) STAT W4700 - A calculus-based tour of the fundamentals of probability theory and statistical inference. Probability models, random variables, useful distributions, expectations, law of large numbers, central limit theorem, point and interval estimation, hypothesis tests, asymptotic ideas, non-parametrics, resampling, Bayesian inference, linear regression.

Spring Course Offerings:

Machine Learning for Data Science (3) COMS W4721 - An introduction to machine learning, with an emphasis on data science. Topics will include least squares methods, Gaussian distributions, linear classification, linear regression, maximum likelihood, exponential family distributions, Bayesian networks, Bayesian inference, mixture models, the EM algorithm, graphical models, hidden Markov models, support vector machines, and kernel methods. An emphasis of the course will be on methods and problems relevant to big data problems.

Exploratory Data Analysis and Visualization (3) STAT W4701 - This class introduces the algorithmic skills and design principles necessary to explore and present datasets computationally and visually. These include command line tools, the use of state-of-the art languages and software, an algorithmic understanding of how to work with a large datasets (including parallelism and the map-reduce framework), interactive visualizations, exploratory data analysis as a means to generate and test hypotheses, as well as basics of data exploration and visualization.


The Certification program may be completed in as little as two semesters of part-time study. This is a non-degree part-time program; admitted students requiring an F1/J1 visa would not be eligible to apply.


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