Master of Science in Computer Information Systems concentration in Data Analytics
The 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.
Admission & Prerequisite Information
MET prioritizes the review and admission of applications submitted earlier in the rolling admission process. You are encouraged to submit your application as soon as possible and no later than the priority application deadlines for each term.
Applicants must have an earned bachelor’s degree, in any field of study, from a regionally accredited college/university (or the international equivalent) prior to enrollment at Metropolitan College. The following materials are required for a complete application:
- Completed Application for Graduate Admission and application fee
- All college transcripts
- Personal statement
- Three letters of recommendation
- Official English proficiency exam results (International students)
Applicants are not required to have a degree in computer science for entry to a program within the Department of Computer Science. Upon review of your application, the department will determine if the completion of prerequisite coursework will be required, based on your academic and professional background. The following prerequisite courses may be required:
MET CS 200 Introduction to Computer Information Systems
This course is a technically-oriented introductory survey of information technology. Students learn about basic computer information, different types of business systems and basic systems analysis, design and development. Students also study basic mathematics, software development and create simple Java programs. [ 4 cr. ]Fall 2020
|A1||IND||Staff||CAS 228||T||6:00 pm – 8:45 pm|
A maximum of two graduate-level courses (8 credits) taken at Metropolitan College before acceptance into the program may be applied towards the degree.
Degree Requirements—On Campus
A total of 40 credits is required. Students must complete both the Core Curriculum and the Concentration Requirements. A minimum passing grade for a course in the graduate program is a C (2.0) but an average grade of B (3.0) must be maintained to be in good academic standing and to be eligible to graduate.
(Five courses/20 credits)
MET CS 625 Business Data Communication and Networks
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. ]Fall 2020
|A1||IND||Arena||STH 113||T||12:30 pm – 3:15 pm|
|A2||IND||Arena||PSY B53||T||6:00 pm – 8:45 pm|
MET CS 669 Database Design and Implementation for Business
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: 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. ]Fall 2020
|A1||IND||Maiewski||COM 215||W||6:00 pm – 8:45 pm|
|A2||IND||Russo||STH 113||R||6:00 pm – 8:45 pm|
|A3||IND||Matthews||T||6:00 pm – 8:45 pm|
|E1||IND||Matthews||T||6:00 pm – 8:45 pm|
MET CS 682 Information Systems Analysis and Design
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. ]Fall 2020
|A1||IND||Guadagno||CAS 222||T||6:00 pm – 8:45 pm|
|A2||IND||Guadagno||FLR 152||R||6:00 pm – 8:45 pm|
|E1||IND||Guadagno||FLR 152||R||6:00 pm – 8:45 pm|
MET CS 782 IT Strategy and Management
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. ]Fall 2020
|A1||IND||Arakelian||CAS 227||R||6:00 pm – 8:45 pm|
And one of the following:
MET CS 520 Information Structures with Java
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. For undergraduate students: This course may not be taken in conjunction with METCS232. Only one of these courses can be counted towards degree requirements. [ 4 cr. ]Fall 2020
|A1||IND||Donald||MCS B37||M||6:00 pm – 8:45 pm|
|E1||IND||Donald||MCS B37||M||6:00 pm – 8:45 pm|
MET CS 521 Information Structures with Python
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 300, or instructor's consent. [ 4 cr. ]Fall 2020
|A1||IND||Lu||STH 113||M||6:00 pm – 8:45 pm|
|A2||IND||Palmer||W||6:00 pm – 8:45 pm|
|A3||IND||Pinsky||R||12:30 pm – 3:15 pm|
|A4||IND||Aleksandrov||CAS 201||R||6:00 pm – 8:45 pm|
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.
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)
MET 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. ]Fall 2020
|A1||IND||Alizadehshab||M||6:00 pm – 8:45 pm|
|A3||IND||Kalathur||CAS 227||T||6:00 pm – 8:45 pm|
MET CS 546 Introduction to Probability and Statistics
The goal of this course is to provide students with the mathematical fundamentals required for successful quantitative analysis of problems. 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. For undergraduate students: This course may not be taken in conjunction with MET MA 213, only one of these courses will count toward degree program requirements. Students who have taken MET MA 113 as well as MET MA 123 will also not be allowed to count MET CS 546 towards degree requirements. [ 4 cr. ]Fall 2020
|A1||IND||Gorlin||SHA 201||M||6:00 pm – 8:45 pm|
|A2||IND||Gorlin||CGS 527||T||6:00 pm – 8:45 pm|
|E1||IND||Gorlin||CGS 527||T||6:00 pm – 8:45 pm|
MET 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. ]Fall 2020
|A1||IND||Alaghemandi||W||6:00 pm – 8:45 pm|
|A2||IND||Teymourian||CAS 226||R||6:00 pm – 8:45 pm|
|A3||IND||Zhang||HAR 408||R||12:30 pm – 3:15 pm|
MET CS 688 Web Analytics and Mining
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. 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 will be extensively investigated. Laboratory Course. Prerequisites: MET CS 544, or MET CS 555 or equivalent knowledge, or instructor's consent. [ 4 cr. ]Fall 2020
|A1||IND||Rawassizadeh||T||12:30 pm – 3:15 pm|
|A2||IND||Rawassizadeh||SHA 206||R||6:00 pm – 8:45 pm|
MET CS 699 Data Mining
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: CS 546 and either CS 579 or CS 669. Or instructor's consent. [ 4 cr. ]Fall 2020
|A1||IND||Lee||CAS 201||T||6:00 pm – 8:45 pm|
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.