MS in Applied Data Analytics

Formerly titled CS 544 Foundations of Analytics with R.
The goal of this course is to provide students with the mathematical and practical background required in the field of data analytics. Probability and statistics concepts will be reviewed as well as the R tool for statistical computing and graphics. Different types of data are investigated along with data summarization techniques and plotting. Data populations using discrete, continuous, and multivariate distributions are explored. Errors during measurements and computations are analyzed in the course. Confidence intervals and hypothesis testing topics are also examined. The concepts covered in the course are demonstrated using R. Laboratory Course. Prereq: MET CS546 and (MET CS520 or MET CS521), or equivalent knowledge, or instructor's consent.  [ 4 cr. ]

Formerly titled CS 555 Data Analysis and Visualization with R.
This course provides an overview of the statistical tools most commonly used to process, analyze, and visualize data. Topics include simple linear regression, multiple regression, logistic regression, analysis of variance, and survival analysis. These topics are explored using the statistical package R, with a focus on understanding how to use and interpret output from this software as well as how to visualize results. In each topic area, the methodology, including underlying assumptions and the mechanics of how it all works along with appropriate interpretation of the results, are discussed. Concepts are presented in context of real world examples. Recommended Prerequisite: MET CS 544 or equivalent knowledge, or instructor's consent.  [ 4 cr. ]

Discusses basic methods for designing and analyzing efficient algorithms emphasizing methods used in practice. Topics include sorting, searching, dynamic programming, greedy algorithms, advanced data structures, graph algorithms (shortest path, spanning trees, tree traversals), matrix operations, string matching, NP completeness. Prereq: MET CS248 and either MET CS341 or MET CS342. Or METCS 521 and METCS 526. Or instructor's consent.  [ 4 cr. ]

Students will learn major Python tools and techniques for data analysis. There are weekly assignments and mini projects on topics covered in class. These assignments will help build necessary statistical, visualization and other data science skills for effective use of data science in a variety of applications including finance, text processing, time series analysis and recommendation systems. In addition, students will choose a topic for a final project and present it on the last day of class. Prerequisite: MET CS 521 or equivalent. Or, instructor's consent.   [ 4 cr. ]

Formerly titled CS 688 Web Analytics and Mining.
The Web Mining and Graph Analytics course covers the areas of web mining, machine learning fundamentals, text mining, clustering, and graph analytics. This includes learning fundamentals of machine learning algorithms, how to evaluate algorithm performance, feature engineering, content extraction, sentiment analysis, distance metrics, fundamentals of clustering algorithms, how to evaluate clustering performance, and fundamentals of graph analysis algorithms. Laboratory Course. Prerequisites: MET CS 544, or MET CS 555 or equivalent knowledge, or instructor's consent.  [ 4 cr. ]

The goal of this course is to study basic concepts and techniques of data mining. The topics include data preparation, classification, performance evaluation, association rule mining, and clustering. We will discuss basic data mining algorithms in the class and students will practice data mining techniques using data mining software. Students will use Weka and JMP Pro. Prereq: CS 546 and either CS 579 or CS 669. Or instructor's consent.  [ 4 cr. ]

Mathematics is fundamental to data science and machine learning. This course reviews essential mathematical concepts and procedures which are fundamental. These concepts are illustrated by Python and/or R code and by many visualizations. This course discusses mathematical concepts and computational methods for data science using simple self-contained examples, intuition and visualization. These examples will help develop intuitive explanations behind mathematical concepts. Extensive visualizations will be used to illustrate core mathematical concepts. The emphasis is both on mathematics and computational algorithms that are at the heart of many algorithms for data analysis and machine learning. This course will advance students mathematical skills that can be used effectively in data analytics and machine learning. Prerequisite: Basic knowledge of Python or R. Or instructor's consent.  [ 4 cr. ]

This course surveys state-of-the art technologies in DW and Big Data. It describes logical, physical and semantic foundation of modern DW infrastructure. Students will create a cube using OLAP and implement decision support benchmarks on Hadoop/Spark vs Vertica database. Upon successful completion, students will be familiar with tradeoffs in DW design and architecture. Prereq: MET CS 579 or MET CS 669 and either MET CS 520 or MET CS 521. Or instructor's consent.   [ 4 cr. ]

Formerly titled CS767 Machine Learning
Theories and methods for learning from data. The course covers a variety of approaches, including Supervised and Unsupervised Learning, Neural Nets and Deep Learning, Adversarial Learning, Bayesian Learning, and Genetic Algorithms. Each student focuses on two of these approaches and creates a term project. Laboratory course. Prerequisite: MET CS 521 and either MET CS 622, MET CS 673 or MET CS 682. MET CS 677 is strongly recommended. Or, instructor's consent.  [ 4 cr. ]

This course is an introduction to large-scale data analytics. Big Data analytics is the study of how to extract actionable, non-trivial knowledge from massive amount of data sets. This class will focus both on the cluster computing software tools and programming techniques used by data scientists, as well as the important mathematical and statistical models that are used in learning from large-scale data processing. On the tools side, we will cover the basics systems and techniques to store large-volumes of data, as well as modern systems for cluster computing based on Map-Reduce pattern such as Hadoop MapReduce, Apache Spark and Flink. Students will implement data mining algorithms and execute them on real cloud systems like Amazon AWS, Google Cloud or Microsoft Azure by using educational accounts. On the data mining models side, this course will cover the main standard supervised and unsupervised models and will introduce improvement techniques on the model side.
Prerequisite: MET CS 521, MET CS 544 and MET CS 555. Or, MET CS 677. Or, Instructor's consent.   [ 4 cr. ]

This course covers advanced aspects of database management including normalization and denormalization, query optimization, distributed databases, data warehousing, and big data. There is extensive coverage and hands on work with SQL, and database instance tuning. Course covers various modern database architectures including relational, key value, object relational and document store models as well as various approaches to scale out, integrate and implement database systems through replication and cloud based instances. Students learn about unstructured "big data" architectures and databases, and gain hands-on experience with Spark and MongoDB. Students complete a term project exploring an advanced database technology of their choice. Prereq: MET CS 579 or MET CS 669; or instructor's consent.  [ 4 cr. ]

Interval estimation. Point estimation including sufficiency, Rao-Blackwell theorem, completeness, uniqueness, Rao-Cramer inequality, and maximum likelihood estimation. Tests of hypothesis: uniformly most powerful tests, uniformly most powerful unbiased tests, likelihood ratio test, chi-squared test, comparison of means and variances, ANOVA, regression, and some nonparametric tests.   [ 4 cr. ]