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Courses - Summer 2024
Data Science and Analytics
Open Seats as of
06/15/2024 at 10:30 AM
Elementary Statistics and Probability
Credits: 3
Grad Meth: Reg, P-F, Aud
Prerequisite: MATH110, MATH112, MATH113, or MATH115; or permission of CMNS-Mathematics department; or must have math eligibility of STAT100 or higher and math eligibility is based on the Math Placement Exam or the successful completion of Math 003 with appropriate eligibility.
Restriction: Must not have completed MATH111; or must not have completed any STAT course with a prerequisite of MATH141.
Cross-listed with: STAT100.
Credit only granted for: DATA100 or STAT100.
Simplest tests of statistical hypotheses; applications to before-and-after and matched pair studies. Events, probability, combinations, independence. Binomial probabilities, confidence limits. Random variables, expected values, median, variance. Tests based on ranks. Law of large numbers, normal approximation. Estimates of mean and variance.
Applications of R for Data Science
Credits: 1
Grad Meth: Reg, P-F, Aud
Prerequisite: DATA100, STAT100, or MATH135; or any 400-level STAT course.
Cross-listed with: STAT110.
Credit only granted for: STAT110 or DATA110.
Intended to prepare students for subsequent courses requiring computation with R, providing powerful and easy to use tools for statistical data analysis. Covers basics of R and R Studio including file handling, data simulation, graphical displays, vector and function operations, probability distributions, and inferential techniques for data analysis.
Introduction to Data Science
Credits: 3
Grad Meth: Reg
Prerequisite: DATA110 or DATA120; and DATA200 and DATA250; or by permission of the DATA Program Director.
Restriction: Must not be a Computer Science major.
Jointly offered with: CMSC320.
Credit only granted for: CMSC320 or DATA320.
An introduction to data science i.e., the end-to-end process of going from unstructured, messy data to knowledge and actionable insights. Provides a broad overview of several topics including statistical data analysis, basic data mining and machine learning algorithms, large-scale data management, cloud computing, and information visualization.
Applied Probability and Statistics I
Credits: 3
Grad Meth: Reg, P-F, Aud
Prerequisite: 1 course with a minimum grade of C- from (MATH131, MATH141); or students who have taken courses with comparable content may contact the department.
Cross-listed with: STAT400.
Credit only granted for: DATA400, ENEE324, or STAT400.
Additional information: Not acceptable toward graduate degrees in MATH/STAT/AMSC.
Random variables, standard distributions, moments, law of large numbers and central limit theorem. Sampling methods, estimation of parameters, testing of hypotheses.
Credits: 3
Grad Meth: Reg
Prerequisite: DATA603 or MSML603.
Cross-listed with: MSML612.
Credit only granted for: DATA612 or MSML612.
Provides an introduction to the construction and use of deep neural networks: models that are composed of several layers of nonlinear processing. The class will focus on the main features in deep neural nets structures. Specific topics include backpropagation and its importance to reduce the computational cost of the training of the neural nets, various coding tools available and how they use parallelization, and convolutional neural networks. Additional topics may include autoencoders, variational autoencoders, convolutional neural networks, recurrent and recursive neural networks, generative adversarial networks, and attention-based models. The concepts introduced will be illustrated by examples of applications chosen among various classification/clustering questions, computer vision, natural language processing.