Certification of Professional Achievement in Data Sciences

The inaugural class for the Certification of Professional Achievement in Data Sciences began in Fall 2013 as 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.

ELIGIBILITY REQUIREMENTS

  • Undergraduate degree
  • Prior quantitative coursework (calculus, linear algebra, etc...)
  • Prior introductory to computer programming coursework

APPLICATION REQUIREMENTS

  • Online application
  • Uploaded transcripts from every post-secondary institution attended
  • Three recommendation letters
  • Personal statement
  • Curriculum vitae / resumé
  • $85 non-refundable application fee

To learn more about the admissions application requirements, please visit the Office of Graduate Student Affairs.

DEADLINE

Applications are currently accepted for fall admission only. The priority deadline for Fall 2015 application submissions is February 15th. [Apply Here]

CURRICULUM

Candidates for the Certification of Professional Achievement in Data Sciences, a non-degree part-time program, are required to complete a minimum of 12 credits, including four required courses:

CSOR W4246 ALGORITHMS FOR DATA SCIENCE
3 pts. Professor Eleni Drinea.
Prerequisites: basic knowledge in programming (e.g., at the level of COMS W1007), a basic grounding in calculus and linear algebra.
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.

STAT W4700 PROBABLITY AND STATISTICS
3 pts. Professor John Cunningham.
Prerequisite: Calculus.
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.

COMS W4721 MACHINE LEARNING FOR DATA SCIENCE
3 pts. Professor John Paisley.
Prerequisites: Background in linear algebra and probability and statistics.
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. Part of the course will be focused on methods and problems relevant to big data problems.

STAT W4701 EXPLORATORY DATA ANALYSIS AND VISUALIZATION
3 pts. Professor Michael Malecki.
Prerequisite: programming.
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.

QUESTIONS

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 refer to our Frequently Asked Questions or sign up for one of our regularly scheduled online information sessions.

 


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