Analytics & Cybersecurity Electives

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Presentation & Customer focus (Business Intelligence): 

16:137:553 Business Intelligence with Visual Analytics

17:610:554 Information Visualization

16:137:531 Introduction to UXD

16:137:537 Mobile App Development

Data Science: 

17:610:594 Data Curation

17:610:582 Information Policy

Programming & Software: 

16:137:562​ Applied AI from Concept to Market (using TensorFlow)

16:332:573 Data Structures & Algorithms

16:332:567 Software Engineering

16:137:541 Enterprise Software Architecture

16:137:552 Python Methodologies

​16:198:518 Operating Systems Design

16:198:520 Introduction to Artificial Intelligence

16:198:536 Machine Learning

Business Requirements & Development: 

16:137:560 Systems Engineering

16:332:567 Software Engineering (Any)

Statistics & Modeling:

16:960:586 Interpretation of Data

16:960:590 Design of Experiments

16:960:588 Data Mining

Project Based: 

16:137:608/609/610 Professional Internship


16:137:650/651/652 (F, S, SUM) Systems Security Certified Practitioner (SSCP) (Voucher Included)

Analytics Courses:

16:137:562​: Applied AI from Concept to Market (using TensorFlow)
This course provides an overview of the processes for specifying, designing, and launching AI/Deep Learning products. AI/Deep learning applications are predicted to bring the next wave of disruptive innovation to many industries. In addition to the highly visible applications such as Autonomous Cars, Grand Master Chess winning Apps, Industrial Robots, Customer service Chatbots, AI software is already changing work processes in many professions such as law, Journalism, finance and medicine. Many platforms have become available to assuage the growing appetite for new AI/deep learning applications. The course will give an overview of the software platforms available with detailed examination of one of the major tools. The course includes a team project where the business and technical methods presented are used to launch a virtual AI/deep learning product.  Students interested in registering for this course should be familiar with the programming language, Python.

16:137:560: Fundamentals of Systems Engineering for Engineering Management
This course focuses on two important aspects - engineering management and systems engineering. It covers functional, non-functional and other aspects, such as managing technical features, quality and performance, and staffing, budgets and outsourcing, respectively, types of management models, such as hierarchical or matrix, and differing approaches for low vs. hi-tech, strategic vs. tactical and other situations encountered in Engineering management. Students also learn how to develop an architecture and design for any system, based on stakeholders’ needs.

16:137:537 Mobile App Development

This course will provide a foundation of the key concepts, tools, and techniques that are required to succeed in today’s mobile market from development through deployment. Taught by a blended team of lively instructors and guest lecturers from industry, business, and academic backgrounds, this boot camp style course will be feature focused on learning experiences and case studies centered on vital subject areas in this emerging market.   Topics will offer a mix of theory and practice in technology and business strategies necessary for today’s professionals and is immediately transferable to your career.

16:960:588 Data Mining
Databases and data warehousing; exploratory data analysis and visualization; an overview of data mining algorithms; modeling for data mining; descriptive modeling; predictive modeling; pattern and rule discovery; text mining; Bayesian data mining; and observational studies. Prerequisites: 16:960:567 and 587.

26:198:644 Data Mining
The key objectives of this course are two-fold: (1) to teach the fundamental concepts of data mining and (2) to provide extensive hands-on experience in applying the concepts to real world applications. The core topics to be covered in this course include classification, clustering, association analysis, and anomaly/novelty detection. This course consists of about 13 weeks of lecture, followed by 2 weeks of project presentations by students who will be responsible for developing and/or applying data mining techniques to applications such as intrusion detection, Web usage analysis, financial data analysis, text mining, bioinformatics, systems management, Earth Science, and other scientific and engineering areas. At the end of this course, students are expected to possess the fundamental skills needed to conduct their own research in data mining or to apply data mining techniques to their own research fields.

17:610:561 Data Analytics for Information Professionals
Data analytics linked to storage, curation, management, and mining with attention to alternative methodological approaches.  The course will demonstrate various methods to explore how big data might be analyzed, stored, and retrieved.

16:220:507 Econometrics I
Focus on measurement of economic parameters. Statistical estimation and inference of regression equation models. Properties of OLS, GLS, JGLS, 2SLS, 3SLS, and Maximum Likelihood Estimators. Introduction to time-series analysis and quantitative-response models. Use of linear algebra and statistical packages. Emphasis is on theory. Prerequisite: 16:220:506 or equivalent.

Computing and Computer Science Courses

16:332:569 (F) Database System Engineering
Relational data model, relational database management system, relational query languages, parallel database systems, database computers, and distributed database systems.

16:198:541 (S) Database Systems
Relational data model. Relational query languages and their expressiveness. Dependency theory and relational normalization. Physical database design. Deductive databases and object-oriented databases. Optimization of relational queries.  Prerequisites: 01:198:336 or equivalent; 16:198:513. Recommended: 16:198:509 or equivalent.

16:332:503 Programming Methodologies for Numerical Computing and Computational Finance (3)
Fundamentals of object-oriented programming and C++ with an emphasis on numerical computing and computational finance. Design oriented. Topics include C++ basics, object-oriented concepts, data structures, algorithm analysis, and applications.    

16:332:566 Introduction to Parallel and Distributed Computing
Systems, architectures, algorithms, programming models, languages and software tools. Topics covered include parallelization and distribution models; parallel architectures; cluster and networked meta-computing systems; parallel/distributed programming; parallel/distributed algorithms, data-structures and programming methodologies; applications; and performance analysis. Programming assignments and a final project. Prerequisites: a data structures course and knowledge of computer architecture.

16:332:572 Parallel and Distributed Computing
(Data-Intensive Computing, Cloud Computing, Scalable Data-Analytics, Accelerators, etc.) Advanced topics in parallel computing including current and emerging architectures, programming models application development frameworks, runtime management, load balancing, and scheduling, as well as emerging areas such as autonomic computing, Grid computing, pervasive computing, and sensor-based systems. Prerequisites: 16:332:566.

16:332:573 Data Structures and Algorithms
The objective is to take graduate students in all graduate School of Engineering fields with a good undergraduate data structures and programming background and make them expert in programming the common algorithm and data structures, using the C and C++ programming languages. The students will perform laboratory exercises in programming the commonplace algorithms. The students will also be exposed to computation models and computational complexity.

56:198:562 Big Data Algorithms
This course provides an introduction to algorithms and techniques for processing very large data sets, including those that may not be suitable or even available for offline processing. A big data algorithm usually tries to solve a problem in data mining (if one is trying to obtain an appropriate statistical model, or summary features for the data), machine learning (if one is trying to use data samples or data repositories as training sets for discovering models), or online processing of large datasets or data streaming.

56:137:500 Essentials of Computer Science (Camden)

This course targets as its main learning goals, the acquisition of essential computational skills, viz. being able to program and work with models for scientific data, and to organize and visualize such data on computing systems.

The course will provide a detailed introduction to programming in Python (as a representative language widely used by scientists and engineers). Specific programming topics that we will cover during the course will include: control statements; arrays and lists; classes, objects and methods; inheritance; polymorphism; exception handling; file streams and serialization; recursion; searching and sorting.

26:198:641 Advanced Database Systems
The purpose of this course is to present advanced topics in database systems and delve into research in these areas. The topics include distributed, object-oriented, active, deductive and temporal databases, as well as advanced application domains that influence database research such as semantic web, internet, workflow systems, etc

56:198:500 Introduction to Programming for Computational Scientists
This course introduces the basics of modern computer programming to beginning graduate students without a background in computer science. Topics covered are: control statements; arrays and lists; classes, objects and methods; inheritance; polymorphism; exception handling; file streams and serialization; recursion; searching and sorting. Students are required to use an up-to-date integrated development environment (IDE) to complete a number of programming assignments.

16:332:567 Software Engineering
This is a graduate course in software engineering. The key objective of this course is to learn how to design and document complex software using symbolic representations, i.e., UML diagrams. The course covers software life-cycle models and different phases of the software development process.
The course focuses on hands-on development of demonstrable software, which requires a great deal of programming. However, this is not a programming course in the sense that it does not teach any programming language. We are assuming that the student has a solid programming knowledge and is ready to learn best practices and ideas about software development. An ideal background knowledge includes a traditional programming language, such as Java, C++, or C#, and Web programming languages, such as PHP and JavaScript, as well as relational database programming (using SQL).
The key characteristic is having teams of about five students work on a semester-long project. The grading is competitive, with the highest rated project receiving the highest grade and the others being rated relatively to the highest one.

16:198:518 Operating Systems Design
The course covers the basic operating systems concepts (interrupt and system call handling, processes, threads, virtual memory, I/O and file systems), with an emphasis OS design. Each week, an OS concept will be presented, followed by a study of the OS kernel code (from MIT xv6, written in C) that implements that concept, in the subsequent week. There will be reading assignments for each lecture. Five project assignments are meant to deepen the understanding of the OS concepts and their implementation through independent work requiring kernel-level C programming. Students are expected to be comfortable with the use of the C programming language in systems-level programming (equivalent to CS 214) and have undergraduate-level computer-architecture background (equivalent to CS 211).

Data Sciences Courses

16:137:531 Introduction to User Experience Design (3)

An introduction to the field of User Experience Design (UXD) describing the process by which computer interfaces are specified, designed and tested so that they are easy to learn, understand and use by their intended user population. The course covers how such processes fit within industry and current software practices, how they are managed and what specific competitive advantages can be gleaned from them in addition to and introduction to the practitioners' field, including conferences, blogs and recognized gurus of UXD.

17:610:554 Information Visualization and Presentation

The Design of presentations using texts, graphics, images and sounds. User interpretation, navigation an interaction with visualizations. Visualization in information retrieval and interfaces in library and information processes. Effective display and presentation of information in orgamizational contexts, using various formats both print and electronic.

17:610:560 Fundamentals of Big Data Curation and Management
This course introduces students to the use of large data sets and prepares them for work in either a corporate data analytics world or in a major scientific archive.  Students will learn the beginnings of how to design, manage, and exploit large textual, graphical, and numeric data collections.

17:610:558 Digital Library Technology
Organizational, technical, and logistical issues concerning the design and implementation of electronic collections, documents, and services. Students learn in the context of building their own prototype digital library.

16:960:590 Design of Experiments
The course covers fundamental principles of experimental design, completely randomized variance component designs, randomized blocks, latin squares, incomplete blocks, partially hierarchic mixed-model experiments, factorial experiments, fractional factorials, response surface exploration.

16:960:586 Interpretation of Data
Modern methods of data analysis with an emphasis on statistical computing: univariate statistics, data visualization, robust statistics, nonlinear models, logistic regression, generalized linear models (GLM), and smooth regression (including GAM models). Expect to use statistical software packages, such as SAS (or SPSS) and Splus (or R) in data analysis

Project Based

16:137:608/609/610 Professional Internship

Students in the MBS degree program must demonstrate relevant work experience to be eligible for graduation. Part-time students who are working in a relevant industry automatically satisfy this requirement. For all others, students have the option of registering and completing an internship through the MBS program.