Master of Science in Computer Information Systems concentration in Data Analytics

CNSS - LogoThe Master of Science in Computer Information Systems concentration in Data Analytics will provide professionals with the skills required to compete for data analysis jobs amid rising global demand. The concentration will explore the intricacies of data analytics and expose students to various topics related to data processing, analysis, and visualization. Along with probability theory and statistical analysis methods and tools, students will learn how to generate relevant visual presentations of data and will examine concepts and techniques for data mining, text mining, and web mining. In addition to the broad background in the theory and practice of information technology gained from the Computer Information Systems core courses, individuals who complete this program will have a solid knowledge of data analytics practices accompanied by exposure to the methods and tools for data mining and knowledge discovery.

Students who complete the Computer Information Systems master’s degree concentration in Data Analytics will be able to demonstrate:

  • Familiarity with applied probability and statistics, and their relevance in day-to-day data analysis
  • The ability to explore the various data visualization techniques and their applications using real-world data sets
  • An understanding of web analytics and metrics; how to procure and process unstructured text; and hidden patterns
  • Skills in facilitating knowledge discovery using data mining techniques over vast amounts of data

A total of 40 credits is required. Students must complete both the Core Curriculum and the Concentration Requirements.

Core Curriculum

(Five courses/20 credits)

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. Prereq: MET CS 200, or instructor's consent. 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 towards degree requirements.  [ 4 cr. ]

Sum1 2017
Section Type Instructor Location Days Times
SC1 IND Arena HAR 326 T 6:00 pm – 9:30 pm
SEL IND Arena HAR 326 T 6:00 pm – 9:30 pm
SO1 IND Chitkushev ARR
Fall 2017
Section Type Instructor Location Days Times
B1 IND Arena FLR 109 T 6:00 pm – 8:45 pm
E1 IND Arena FLR 109 T 6:00 pm – 8:45 pm
O1 IND Rizinski ARR
O2 IND Mansur ARR

Students learn the latest relational and object-relational tools and techniques for persistent data and object modeling and management. Students gain extensive hands- on experience using Oracle or Microsoft SQL Server as they learn the Structured Query Language (SQL) and design and implement databases. Students design and implement a database system as a term project. Restrictions: Only for MS CIS. 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. ]

Sum1 2017
Section Type Instructor Location Days Times
BCL IND Parrott U 8:00 am – 3:30 pm
SC1 IND Matthews FLR 109 W 6:00 pm – 9:30 pm
SEL IND Matthews FLR 109 W 6:00 pm – 9:30 pm
SO1 IND Mansur ARR
Fall 2017
Section Type Instructor Location Days Times
C1 IND Maiewski CAS 222 W 6:00 pm – 8:45 pm
D1 IND Matthews FLR 266 R 6:00 pm – 8:45 pm
E1 IND Matthews FLR 266 R 6:00 pm – 8:45 pm
O1 IND Mansur ARR
O2 IND Farr ARR

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. ]

Sum1 2017
Section Type Instructor Location Days Times
SC1 IND Guadagno FLR 264 R 6:00 pm – 9:30 pm
SEL IND Guadagno FLR 264 R 6:00 pm – 9:30 pm
SO1 IND Polnar ARR
Fall 2017
Section Type Instructor Location Days Times
D1 IND Guadagno FLR 109 R 6:00 pm – 8:45 pm
E1 IND Guadagno FLR 109 R 6:00 pm – 8:45 pm
O1 IND Braude ARR
O2 IND Polnar ARR

This course describes and compares contemporary and emerging information technology and its management. Students learn how to identify information technologies of strategic value to their organizations and how to manage their implementation. The course highlights the application of I.T. to business needs. CS 782 is at the advanced Masters (700) level, and it assumes that students understand IT systems at the level of CS 682 Systems Analysis and Design. Students who haven't completed CS 682 should contact their instructor to determine if they are adequately prepared. Prereq: MET CS 682, or instructor's consent.   [ 4 cr. ]

Sum1 2017
Section Type Instructor Location Days Times
SC1 IND Arakelian FLR 267 M 6:00 pm – 9:30 pm
SEL IND Arakelian FLR 267 M 6:00 pm – 9:30 pm
SO1 IND Williams ARR
Fall 2017
Section Type Instructor Location Days Times
D1 IND Arakelian CAS 204A R 6:00 pm – 8:45 pm
O1 IND Williams ARR
O2 IND Arakelian ARR
BHA IND Staff W 6:00 pm – 8:45 pm

And one of the following:

This course covers the concepts of object-oriented approach to software design and development using the Java programming language. It includes 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 the students will be able to apply software engineering criteria to design and implement Java applications that are secure, robust, and scalable. Prereq: MET CS 200 or MET CS 300 or Instructor's Consent. Not recommended for students without a programming background.   [ 4 cr. ]

Sum1 2017
Section Type Instructor Location Days Times
BCL IND Kieffer S 8:30 am – 4:00 pm
Fall 2017
Section Type Instructor Location Days Times
A1 IND Donald FLR 109 M 6:00 pm – 8:45 pm
E1 IND Donald FLR 109 M 6:00 pm – 8:45 pm
O1 IND Guardino ARR

This course covers the concepts of the object-oriented approach to software design and development using the Python programming language. 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 capable of applying software engineering principles to design and implement Python applications that can be used in conjunction with analytics and big data. Prerequisite: MET CS 200 Fundamentals of Information Technology or MET CS 300 Foundations of Modern Computing or instructor's Consent. Not recommended for students without a programming background.   [ 4 cr. ]

Sum1 2017
Section Type Instructor Location Days Times
SC1 IND Aleksandrov MCS B23 T 6:00 pm – 9:30 pm
SO1 IND Ultrino ARR
Fall 2017
Section Type Instructor Location Days Times
A1 IND Aleksandrov CAS 428 M 6:00 pm – 8:45 pm
C1 IND Lu STH B20 W 6:00 pm – 8:45 pm
O1 IND Ultrino ARR
O2 IND Ultrino ARR

Students who have completed courses on core curriculum subjects as part of their undergraduate degree program or have relevant work-related experience may request permission from the Department of Computer Science to replace the corresponding core courses with graduate-level computer information systems electives. Please refer to the MET CS Academic Policies Manual for further details.

Concentration Requirements

In addition to the MS in Computer Information Systems Core Curriculum (20 credits), students pursuing a concentration in Data Analytics must also take the following concentration requirements and electives:

Required Data Analytics Courses

(Five courses/20 credits)

The goal of this course is to provide students with the mathematical and practical background required in the field of data analytics. Starting with an introduction to probability and statistics, the R tool is introduced 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 CS 546 or equivalent knowledge, or instructor's consent.  [ 4 cr. ]

Sum1 2017
Section Type Instructor Location Days Times
SC1 IND Liu CAS B25A M 6:00 pm – 9:30 pm
SO1 IND Kalathur ARR
Fall 2017
Section Type Instructor Location Days Times
A1 IND Kalathur COM 217 M 6:00 pm – 8:45 pm
O1 IND Kalathur ARR

The goal of this course is to provide Computer Information Systems students with the mathematical fundamentals required for successful quantitative analysis of problems in the field of business computing. The first part of the course introduces the mathematical prerequisites for understanding probability and statistics. Topics include combinatorial mathematics, functions, and the fundamentals of differentiation and integration. The second part of the course concentrates on the study of elementary probability theory, discrete and continuous distributions. Prereq: Academic background that includes the material covered in a standard course on college algebra or instructor's consent.  [ 4 cr. ]

Sum1 2017
Section Type Instructor Location Days Times
SC1 IND Gorlin FLR 109 M 6:00 pm – 9:30 pm
SEL IND Gorlin FLR 109 M 6:00 pm – 9:30 pm
SO1 IND Temkin ARR
Fall 2017
Section Type Instructor Location Days Times
A1 IND Gorlin FLR 133 M 6:00 pm – 8:45 pm
B1 IND Gorlin FLR 267 T 6:00 pm – 8:45 pm
E1 IND Gorlin FLR 267 T 6:00 pm – 8:45 pm
O1 IND Hicks ARR
O2 IND Temkin ARR

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. ]

Section Type Instructor Location Days Times
B1 IND Teymourian CAS 323A T 6:00 pm – 8:45 pm
D1 IND Zhang CAS 324 R 6:00 pm – 8:45 pm

The Web Analytics and Mining course covers the areas of web analytics, text mining, web mining, and practical application domains. The web analytics part of the course studies the metrics of web sites, their content, user behavior, and reporting. Google analytics tool is used for collection of web site data and doing the analysis. The text mining module covers the analysis of text including content extraction, string matching, clustering, classification, and recommendation systems. The web mining module studies how web crawlers process and index the content of web sites, how search works, and how results are ranked. Application areas mining the social web and game metrics will be extensively investigated. Laboratory Course. Prerequisites: MET CS 544, or MET CS 555 or equivalent knowledge, or instructor's consent.  [ 4 cr. ]

Sum1 2017
Section Type Instructor Location Days Times
SC1 IND Vasilkoski CAS 208 R 6:00 pm – 9:30 pm
Fall 2017
Section Type Instructor Location Days Times
C1 IND Vasilkoski STH B22 W 6:00 pm – 8:45 pm
O1 IND Vasilkoski ARR

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 SQL Server or Oracle. Prereq: MS CS Prerequisites: MET CS 579; or instructor's consent. MS CIS Prerequisites: MET CS 669 and MET CS 546; or instructor's consent.  [ 4 cr. ]

Sum1 2017
Section Type Instructor Location Days Times
SO1 IND Lee ARR
Fall 2017
Section Type Instructor Location Days Times
D1 IND Lee FLR 267 R 6:00 pm – 8:45 pm
E1 IND Lee FLR 267 R 6:00 pm – 8:45 pm
O1 IND Lee ARR

Degree requirements for the online MS in Computer Information Systems concentration in Data Analytics can be viewed here.

View all Computer Science & IT graduate courses.