MATH-443 Mathematical Concepts in Data Analytics
Fall for 2016-2017
Description: This is a projects-based and directed-reading course for advanced undergraduate and beginning graduate students in the mathematical sciences. The classroom environment will not always follow the conventional lecture format, and some out-of-class meetings, either physical or electronic, may take place. Student work will be evaluated on the basis of participation, responsiveness and project quality. The topics covered are all concerned with analytical modeling and computation involving data sets which may be large. The term analytics will refer to modeling and computation within specific mathematical frameworks such as large matrices and other definite file types, and mathematical formulas or prescriptions that enable measuring, parsing, understanding and visualizing the data. The overarching goal is to discover the taxonomy, shape or primary characteristics of data. Examples include large matrices, such as adjacency matrices corresponding to networks, n-dimensional data sets and non-numerical data such as text files. Data sets and their analyses will involve uncertainty, so that familiarity with probability will be essential. Additional topics such as optimization will be encountered, one example being the minimization of objective functions.
Prerequisites: Math 150 (linear algebra), the equivalent of Math 140 (Intro to Math Stat) and skill in a programming language such as Matlab, R or Python, preferably Python. Students having taken Math 501 and Math 510 will be adequately prepared. Students who are unfamiliar with either probability or programming will not have satisfactory background to take the course.
Textbook: No specific text required. Learning resources will include desktop published lecture notes and web-based materials.
Instructor: W Graham Mueller
Restrictions: Enrollment requires instructor approval. In addition the course is restricted to Georgetown students who are either undergraduate mathematics majors or graduate students in the MS degree program in mathematics and statistics. Other qualified students may be permitted to enroll on a case-by-case basis. Math 442 and 443 cannot both be taken for credit by either undergraduate or graduate students.
Prerequisites: Math 150 (linear algebra), the equivalent of Math 140 (Intro to Math Stat) and skill in a programming language such as Matlab, R or Python, preferably Python.
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