A big data certificate that will develop your expertise in managing, analyzing big data.
The mass amount of data collected by industries, retailers, and organizations requires knowledgeable professionals who can collect, mine, and analyze as well as store, retrieve, and manage data. These professionals also guide the analysis, preparation, and visualization of data to aid in understanding trends, patterns, and behaviors, all of which help impact business decisions.
Big data is noted for its volume, varieties of data types, and rapid accumulation. Big data has become a catchphrase to describe data collections that are so large they are not amenable to processing or analysis using traditional database and software techniques. The advanced certificate in big data analytics is a multidisciplinary program intended for professionals with BS degrees in computing or other diverse fields–such as finance, retail, science, engineering, or manufacturing–where knowledge in data analysis is in demand.
Big Data Analytics Courses
The big data certificate features courses in the practical techniques used in exploratory data analysis and mining, as well as the approaches used to store, retrieve, and manage data in the real world. The certificate is meant for students who would like a formal qualification in big data analytics. It also allows professionals with a bachelor's degree to enhance their career opportunities and professional knowledge with targeted graduate course work in big data.
What is a Graduate Certificate?
A graduate certificate, also called an advanced certificate, is a selection of up to five graduate level courses in a particular area of study. It can serve as a stand-alone credential that provides expertise in a specific topic that enhances your professional knowledge base, or it can serve as the entry point to a master's degree. Some students complete an advanced certificate and apply those credit hours later toward a master's degree.
Big Data Analytics, advanced certificate, typical course sequence
Introduction to Big Data
This course provides a broad introduction to the exploration and management of large datasets being generated and used in the modern world. First, practical techniques used in exploratory data analysis and mining are introduced; topics include data preparation, visualization, statistics for understanding data, and grouping and prediction techniques. Second, approaches used to store, retrieve, and manage data in the real world are presented; topics include traditional database systems, query languages, and data integrity and quality. Case studies will examine issues in data capture, organization, storage, retrieval, visualization, and analysis in diverse settings such as urban crime, drug research, census data, social networking, and space exploration. Big data exploration and management projects, a term paper and a presentation are required. Sufficient background in database systems and statistics is recommended. (Prerequisite: CSCI-603 or CSCI-605 with a grade of B or better or (CSCI-320 or SWEN-344). May not take and receive credit for CSCI-620 and CSCI-420. If earned credit for/or currently enrolled in CSCI-420 you will not be permitted to enroll in CSCI-620.) Lecture 3 (Fall, Spring, Summer).
Big Data Analytics
This course provides a graduate-level introduction to the concepts and techniques used in data mining. Topics include the knowledge discovery process; prototype development and building data mining models; current issues and application domains for data mining; and legal and ethical issues involved in collecting and mining data. Both algorithmic and application issues are emphasized to permit students to gain the knowledge needed to conduct research in data mining and apply data mining techniques in practical applications. Data mining projects, a term paper, and presentations are required. (Prerequisites: CSCI-620 or (CSCI-420 and CSCI-320) or (4003-485 and 4003-487) or equivalent course.) Lecture 3 (Fall, Spring).
Total Credit Hours
To be considered for admission to the advanced certificate in big data analytics, candidates must fulfill the following requirements:
Have college level credit or practical experience in probability and statistics, computer programming in a high-level language, and database systems.
International applicants whose native language is not English must submit official test scores from the TOEFL, IELTS, or PTE. Students below the minimum requirement may be considered for conditional admission. Refer to Graduate Admission Deadlines and Requirements for additional information on English language requirements. International applicants may be considered for an English test requirement waiver. Refer to the English Language Test Scores section within Graduate Application Materials to review waiver eligibility.
This certificate is intended for part-time study; therefore, RIT cannot issue I-20 paperwork.