BIG DATA ANALYTICS Courses

BDA 411/511. Introduction to Machine Learning. 3 Credits.

An introductory course on machine learning. The course gives an elementary summary of modern machine learning tools. Topics include regression, decision trees, artificial neural networks, genetic algorithms, clustering, dimension-reduction, learning sets of rules, support vector machines, hidden Markov models, and Bayesian learning. The course will also discuss applications of machine learning that include data mining, bioinformatics, speech recognition, and text and web data processing. Students enrolled are expected to have some ability to write computer programs, some knowledge of probability, statistics and linear algebra. Prerequisites: MATH 312, MATH 316, and STAT 331.

BDA 413/513. Modern Statistical Methods for Big Data Analysis. 3 Credits.

Due to the rapid development of technology, vast amount of data is being generated in social media, genomics, financial industry, business and health care. We rely increasingly on statistical tools to analyze this big data. From the vast array of tools available, in this course students will study the most relevant ones. Topics that will be covered are Logistic Regression, Lasso and Related Methods, Support Vector and Kernel Methodology, Principal Components (SVD) and Variations, Multidimensional Scaling, Boosting, Random Forests, Graphical Models, False Discovery Rates and Permutation Tests. Prerequisites: A grade of C or better in STAT 331 and STAT 405.

BDA 431/531. Data Mining. 3 Credits.

Data mining is the science of discovering structure and making predictions in data sets. Data Mining is a dynamic and fast growing field at the interface of Statistics and Computer Science. The statistical perspective of data mining is emphasized for the majority of the course. Both applied aspects (programming, problem solving, and data analysis) and theoretical concepts (learning, understanding, and evaluating methodologies) of data mining will be covered. Topics may include data preparation, data cleaning, exploratory data analysis, statistical estimation and prediction, regression modeling, clustering, classification and regression trees. An elementary knowledge of concepts in probability and statistics and basic computing proficiency are required. Prerequisites: A grade of C or better in STAT 331 and STAT 405.

BDA 432/532. Introduction to Optimization and Inverse Problems. 3 Credits.

Topics considered include unconstrained and constrained optimization problems, Lagrange multiplier methods, inequality constraints, Kuhn-Tucker conditions, and applications. Also considered are the linear and nonlinear inverse problems, regularization of ill-posed problem including singular value decomposition and Tikhonov regularization methods, inverse eigenvalue problems, and applications. Prerequisites: MATH 307, MATH 312 and MATH 316.

BDA 450. Senior Project in Big Data Analytics I. 3 Credits.

This course introduces students to practical applications of big data analytics. Lecture topics include an overview of the various topics in business, engineering, and government currently using big data analytics. Students will choose a project involving a real world application to explore techniques learned during other course work. Course involves written and oral presentations for students to improve communication and teamwork skills. Prerequisites: A grade of C or better in STAT 331 and STAT 405. Pre- or corequisite: BDA 431.

BDA 451. Senior Project in Big Data Analytics II. 3 Credits.

This course allows the student to pursue an in-depth exploration of a project initiated in BDA 450. The course involves written and oral presentations for students to improve communication and teamwork skills. Prerequisites: BDA 450 and permission of instructor.

BDA 511. Introduction to Machine Learning. 3 Credits.

An introductory course on machine learning. The course gives an elementary summary of modern machine learning tools. Topics include regression, decision trees, artificial neural networks, genetic algorithms, clustering, dimension-reduction, learning sets of rules, support vector machines, hidden Markov models, and Bayesian learning. The course will also discuss applications of machine learning that include data mining, bioinformatics, speech recognition, and text and web data processing. Students enrolled are expected to have some ability to write computer programs, some knowledge of probability, statistics and linear algebra. Prerequisites: MATH 312, MATH 316, and STAT 331.

BDA 513. Modern Statistical Methods for Big Data Analysis. 3 Credits.

Due to the rapid development of technology, vast amount of data is being generated in social media, genomics, financial industry, business and health care. We rely increasingly on statistical tools to analyze this big data. From the vast array of tools available, in this course students will study the most relevant ones. Topics that will be covered are Logistic Regression, Lasso and Related Methods, Support Vector and Kernel Methodology, Principal Components (SVD) and Variations, Multidimensional Scaling, Boosting, Random Forests, Graphical Models, False Discovery Rates and Permutation Tests. Prerequisites: A grade of C or better in STAT 331 and STAT 405.

BDA 531. Data Mining. 3 Credits.

Data mining is the science of discovering structure and making predictions in data sets. Data Mining is a dynamic and fast growing field at the interface of Statistics and Computer Science. The statistical perspective of data mining is emphasized for the majority of the course. Both applied aspects (programming, problem solving, and data analysis) and theoretical concepts (learning, understanding, and evaluating methodologies) of data mining will be covered. Topics may include data preparation, data cleaning, exploratory data analysis, statistical estimation and prediction, regression modeling, clustering, classification and regression trees. An elementary knowledge of concepts in probability and statistics and basic computing proficiency are required. Prerequisites: A grade of C or better in STAT 331 and STAT 405.

BDA 532. Introduction to Optimization and Inverse Problems. 3 Credits.

Topics considered include unconstrained and constrained optimization problems, Lagrange multiplier methods, inequality constraints, Kuhn-Tucker conditions, and applications. Also considered are the linear and nonlinear inverse problems, regularization of ill-posed problem including singular value decomposition and Tikhonov regularization methods, inverse eigenvalue problems, and applications. Prerequisites: MATH 307, MATH 312 and MATH 316.