MS in Computer Information Systems, Data Analytics Concentration

Prerequisite: MET LB 102 or consent of instructor. Not recommended for students without a programming background. Explore the concepts of object-oriented approach to software design and development using the Java programming language. You will engage in a detailed discussion of programming concepts starting with the fundamentals of data types, control structures methods, classes, applets, arrays and strings, and proceeding to advanced topics such as inheritance and polymorphism, interfaces, creating user interfaces, exceptions, and streams. Upon completion of this course, you will be able to apply software engineering criteria to design and implement Java applications that are secure, robust, and scalable.  [ 4 cr. ]

This course covers the concepts of the object-oriented approach to software design and development using Python. It includes a detailed discussion of programming concepts starting with the fundamentals of data types, control structures methods, classes, arrays and strings, and proceeding to advanced topics such as inheritance and polymorphism, creating user interfaces, exceptions and streams. Upon completion of this course students will be able to apply software engineering principles to design and implement Python applications that can be used in with analytics and big data. Effective Fall 2021, this course fulfills a single unit in each of the following BU Hub areas: Quantitative Reasoning II, Creativity/Innovation, Critical Thinking. Prerequisite: Programming experience in any language. Or Instructor's consent.  [ 4 cr. ]

Prerequisites: MET LB 102 or consent of instructor. - This course presents the foundations of data communications and takes a bottom-up approach to computer networks. The course concludes with an overview of basic network security and management concepts. Restrictions: This course may not be taken in conjunction with MET CS 425 (undergraduate) or MET CS 535. Only one of these courses can be counted toward degree requirements.  [ 4 cr. ]

Learn the latest relational and object-relational tools and techniques for persistent data and object modeling and management. You will gain extensive hands-on experience using Oracle or Microsoft SQL Server as you learn the Structured Query Language (SQL) and design and implement databases. You will design and implement a database system as a term project. Restrictions: This course may not be taken in conjunction with MET CS 469 (undergraduate) or MET CS 579. Only one of these courses can be counted towards degree requirements.  [ 4 cr. ]

Prerequisites: Basic programming knowledge or consent of instructor. - Object-oriented methods of information systems analysis and design for organizations with data- processing needs. System feasibility; requirements analysis; database utilization; Unified Modeling Language; software system architecture, design, and implementation, management; project control; and systems-level testing.  [ 4 cr. ]

Prerequisites: MET LB 103, MET LB 104, and (METCS 520 or METCS 521), or equivalent knowledge, or consent of instructor. 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 methods. Data populations using discrete, continuous, and multivariate distributions are explored. Sampling methods and errors during measurements and computations are analyzed in the course. String manipulations and data wrangling methods are examined in detail. The concepts covered in the course are demonstrated using R. Laboratory Course. Restrictions: This course may not be taken in conjunction with MET CS 550.  [ 4 cr. ]

Prerequisites: Basic knowledge of Python or R; or consent of instructor. - Mathematics is fundamental to data science and machine learning. In this course, you will review essential mathematical concepts and fundamental procedures illustrated by Python and/or R code and visualizations. Computational methods for data science presented through accessible, self-contained examples, intuitive explanations, and visualization will be discussed. Equal emphasis will be placed on both mathematics and computational methods that are at the heart of many algorithms for data analysis and machine learning. You will also advance your mathematical proficiency, enabling you to effectively apply your skills to data analytics and machine learning. Restrictions: This course may not be taken in conjunction with MET CS 544.  [ 4 cr. ]

Prerequisites: MET CS 544 or MET CS 550 or consent of instructor. Learn the foundations of machine learning, regression, and classification. Topics include how to describe data, statistical inference, 1 and 2 sample tests of means and proportions, simple linear regression, multiple linear regression, multinomial regression, logistic regression, analysis of variance, and regression diagnostics. These topics are explored using the statistical package R, with a focus on understanding how to use these methods and interpret their outputs and how to visualize the 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 in order to help you learn when and how to deploy different methods.  [ 4 cr. ]

Prerequisite: (MET CS 521) or equivalent or instructor's consent. 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.  [ 4 cr. ]

Prerequisites: MET CS 544, or MET CS 555 or equivalent knowledge, or instructor's consent. - 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, link analysis and community detection based on graphs. Laboratory Course.  [ 4 cr. ]

Prerequisites: MET CS 521, MET LB 103 and MET LB 104; and either MET CS 579 or MET CS 669; or consent of instructor. - Study basic concepts and techniques of data mining. Topics include data preparation, classification, performance evaluation, association rule mining, regression and clustering. You will learn underlying theories of data mining algorithms in the class and practice those algorithms through assignments and a semester-long class project using R. After finishing this course, you will be able to independently perform data mining tasks to solve real-world problems.  [ 4 cr. ]

Prerequisites: MET CS 521 and at least one of MET CS 577, MET CS 622, MET CS 673 or MET CS 682; or consent of instructor. Theories and methods for learning from data. The course covers a variety of approaches, including Supervised and Unsupervised Learning, Regression, k-means, KNN's, Neural Nets and Deep Learning, Transformers, Recurrent Neural Nets, Adversarial Learning, Bayesian Learning, and Genetic Algorithms. The underpinnings are covered: perceptron's, backpropagation, attention, and transformers. Each student creates a term project.  [ 4 cr. ]

Prerequisite: (MET CS 521 & MET CS 544 & MET CS 555) or MET CS 577 or consent of instructor. An introduction to large-scale data analytics, focusing on both the foundational concepts and practical tools used in the field. Big Data analytics involves extracting meaningful, non-trivial insights from vast and complex datasets. You will explore key software tools and programming techniques commonly used by data scientists working with distributed systems. You will also learn core technologies for storing and processing large volumes of data, with a particular emphasis on cluster computing frameworks that follow the MapReduce paradigm, including Hadoop MapReduce and Apache Spark. Through hands-on assignments and projects, you will gain practical experience by implementing data processing algorithms and running them on real-world cloud platforms such as Amazon Web Services (AWS) and Google Cloud, utilizing educational credits and accounts provided for the course.  [ 4 cr. ]