CERT

Data Science Certification

Online Program Overview

Degree Level
Certificates
Total Credits
12
Delivery
Fully Online
Contact Us
Minimum GPA
3
Qualifying Exam
GRE Not Required

The Certification of Professional Achievement in Data Sciences prepares students to expand their career prospects or change career paths by developing foundational data science skills.

Individuals looking to strengthen their career prospects or make a career change by developing in-depth expertise in data science would benefit from this program.

Admissions Requirements

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

Applicants to the Certification of Professional Achievement Program must submit official transcripts, personal-professional statement, 3 letters of recommendation, resume, and the $150 application fee.

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.

Description

  • CSOR W4246 ALGORITHMS FOR DATA SCIENCE

  • STAT GR5701 PROBABLITY AND STATISTICS FOR DATA SCIENCE

  • COMS W4721 MACHINE LEARNING FOR DATA SCIENCE

  • STAT GR5702 EXPLORATORY DATA ANALYSIS AND VISUALIZATION

Course List

CSOR W4246 ALGORITHMS FOR DATA SCIENCE

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 GR5701 PROBABLITY AND STATISTICS FOR DATA SCIENCE

Prerequisites: 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

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 GR5702 EXPLORATORY DATA ANALYSIS AND VISUALIZATION
Prerequisites: STAT GR5205 and STAT GR5206 at the discretion of the instructor (students should have basic knowledge of R). This course covers visual approaches to exploratory data analysis, with a focus on graphical techniques for finding patterns in high dimensional datasets. We consider data from a variety of fields, which may be continuous, categorical, hierarchical, temporal, and/or spatial in nature. Building on material from STAT GR5205, STAT GR5206 and other applied courses, we cover visual approaches to selecting, interpreting, and evaluating models/algorithms such as linear regression, time series analysis, clustering, and classification.

Tuition & Fees

2017 - 2018 Tuition & Fees

Please note that all tuition and fees are in U.S. dollars and are estimated. Tuition and most fees are prescribed by statute, and are subject to change at the discretion of the Trustees.

CVN Credit Tuition: $1,936 per point (Credit Hour)
CVN Fee: $395 non-refundable fee per course
Transcript Fee: $105 non-refundable one-time fee

CVN Audit Tuition: $834 per point (Credit Hour)
Graduate Admission Application Fee: $150 non-refundable one-time fee
CVN Fee: $395 non-refundable fee per course

Certification Program Application Fee: $150 non-refundable one-time fee

Late Registration Fee: $100 non-refundable fee

CVN Withdrawal Fee: $75, plus prorated tuition and all non-refundable fees

For example: A three credit course would be $5,808 + transcript fee $105 (one-time) + CVN fee $395 = $6,308

 

Payment should be mailed to:

Columbia Video Network
540 S.W. MUDD Building, MC4719
500 West 120th Street
New York, NY 10027

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