Data Analytics Graduate Certificate
The rapid pace at which digital data is being generated has resulted in very large amounts of data, usually referred to as “Big Data,” which require new techniques for processing and analysis. Data analytics is needed in nearly every industry to guide decision-making processes through the collection and analysis of available data—yet, according to a McKinsey Global Institute assessment, the shortage of personnel with the requisite deep analytical skills could reach 140,000 to 190,000 in the United States alone by 2018.
The new Graduate Certificate in Data Analytics will provide professionals with the skills required to compete for data analysis jobs amid rising global demand. The certificate program 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. 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 Graduate Certificate 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
Graduate Certificate in Data Analytics Program Options
Available on campus and in the following formats:
Prerequisite courses or evidence of proficiency in these areas must accompany the application to the program. If college-level credit courses are not in evidence, the department will determine what prerequisite courses must be completed in addition to the graduate certificate requirements. Students claiming equivalent proficiency in prerequisite courses from non-academic sources must take an examination to demonstrate such proficiency.
Official transcripts of previous academic work, three letters of recommendation, personal statement, and résumé are required as part of the application.
A maximum of two graduate-level courses (8 credits) taken at Metropolitan College prior to acceptance into the program may be applied toward the certificate.
Minimum passing grade for a course in the graduate certificate program is C (2.0), but an average grade of B (3.0) must be maintained to be in good academic standing and satisfy the certificate requirements.
Applicants to the program are required to have a bachelor’s degree from a regionally accredited institution, in addition to the equivalent of MET CS 546. Some courses may have additional prerequisites.
(Four courses/16 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. 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 CS546 and (MET CS520 or MET CS521), or equivalent knowledge, or instructor's consent. [ 4 cr. ]Fall 2018
|A1||IND||Zhang||STH 113||M||6:00 pm – 8:45 pm|
|C1||IND||Zhang||CAS B06A||W||6:00 pm – 8:45 pm|
|SA1||IND||Zhang||FLR 267||MW||6:00 pm – 9:30 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 2018
|B1||IND||Teymourian||CAS 315||T||6:00 pm – 8:45 pm|
|D1||IND||Teymourian||MCS B23||R||6:00 pm – 8:45 pm|
|SB1||IND||Teymourian||FLR 267||MW||6:00 pm – 9:30 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. 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. ]Fall 2018
|A1||IND||Vasilkoski||PSY B53||M||6:00 pm – 8:45 pm|
|A2||IND||Staff||EPC 204||M||6:00 pm – 8:45 pm|
|SC1||IND||Vasilkoski||HAR 228||R||6:00 pm – 9:30 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: 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. ]Fall 2018
|C1||IND||Lee||EPC 205||W||6:00 pm – 8:45 pm|
View all Computer Science & IT graduate courses.