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Columbia Certification of Professional Achievement in Data Sciences
Columbia University Data Science Institute
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Tags: algorithms / data analysis / data science / data visualization / machine learning / probability / statistics
“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. Join us from anywhere in the world as this program is now also offered online.”
- Algorithms for Data Science: 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.
- Probability and Statistics: 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.
- Machine Learning for Data Science: 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.
- Exploratory Data Analysis and Visualization: Fundamentals of data visualization, layered grammar of graphics, perception of discrete and continuous variables, introduction to Mondran, mosaic pots, parallel coordinate plots, introduction to ggobi, linked plots, brushing, dynamic graphics, model visualization, clustering and classification.
Recommended Prerequisites: Undergraduate degree, prior quantitative coursework (calculus, linear algebra, etc.), prior introduction to computer programming coursework.