Develop your expertise in managing and analyzing big data.
The mass amount of data being collected by industries, retailers, and organizations requires knowledgeable professionals who can manage, process, and analyze this information to identify and understand trends and to make meaningful 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.
The advanced certificate is also meant for students who would like a formal qualification in this area. The program allows professionals with a bachelor's degree to enhance their career opportunities and professional knowledge with targeted graduate course work in a focused area without making a commitment to an MS program.
The curriculum consists of two required courses and two elective courses selected by the student in topic areas related to 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 is 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:
Hold a baccalaureate degree (or equivalent) from an accredited university or college in science, computing, engineering, or a related major.
Applicants with undergraduate degrees from foreign colleges and universities are required to submit GRE scores. GRE scores from other students may be requested.
Submit a personal statement of educational objectives outlining the applicant’s research/project interests, career goals, and suitability to the program.
Submit a current resume or curriculum vitae.
Submit two letters of recommendation from academic or professional sources.
Submit official transcripts (in English) of all previously completed undergraduate and graduate course work.
Have a minimum cumulative GPA of 3.0 (or equivalent)
Have acceptable 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 scores from the TOEFL, IELTS, or PTE. A minimum TOEFL score of 88 (internet-based) is required. A minimum IELTS score of 6.5 is required. The English language test score requirement is waived for native speakers of English or for those submitting transcripts from degrees earned at American institutions.
This certificate is intended for part-time study; therefore, RIT cannot issue I-20 paperwork.