Font Size

SCREEN

Cpanel

Big Data Management and Security

Graduate Certificate Program

Big Data Management and Security

Program Description
Significant data growth leads to challenges in efficiently and securely sharing, accessing, and analyzing big data. Proficiency in big data management and security requires knowledge in interdisciplinary areas including computer science, business information technology, mathematics and statistics, and electrical and computer engineering. Currently many colleges and universities worldwide are establishing programs in big data analytics. LCIT faculty have the expertise to provide a unique specialized graduate certificate program to teach practicing computing professionals and graduate students the skills that are necessary for the use and development of big data securely and efficiently.

Curriculum*
The Big Data Management and Security Certificate program consists of four courses. Students will be responsible for prerequisite knowledge as determined by course instructors and listed in the Graduate Catalog. With the approval of the department, appropriate courses may be substituted for a certificate course if that course is not available.

Select one of the following courses:

COMP SCI 6304: Cloud Computing and Big Data Management
IST 5420: Introduction to Big Data Analytics

Select one of the follow courses:

COMP SCI 5402: Data Mining & Machine Learning
COMP SCI 6301: Web Data Management and XML
COMP SCI 6302: Heterogeneous and Mobile Databases
COMP ENG 6330: Clustering Algorithms (Co-listed with ELEC ENG 439, SYS ENG 6214, COMP SCI 6405 and STAT 6239)
STAT 5814: Applied Time Series Analysis

Select two of the following courses:

COMP SCI 6601: Privacy Preserving Data Integration and Analysis
COMP SCI 6600: Computer Security
COMP SCI 6605: Advanced Network Security

Note: There is overlap between the course offerings for this graduate certificate and the Big Data and Management and Analytics certificate. No course can be used to satisfy the requirements for more than one certificate.

Course Descriptions

COMP SCI 6304: Cloud Computing and Big Data Management
Covers facets of cloud computing and big data management, including the study of the architecture of the cloud computing model with respect to virtualization, multitenancy, privacy, security, cloud data management and indexing, scheming and cost analysis; it also includes programming models such as Hadoop and MapReduce, crowd sourcing, and data provenance. Prerequisites: a “C” or better in both Comp Sci 5800 and either 5300 or Comp Sci 5001- Introduction to Data Mining

IST 5420: Introduction to Big Data Analytics
This course addresses the foundations of using predictive statistics on big data sets to impact decision-making. Focus is applied examples using realistic data. Models implemented include regression (parametric/nonparametric), classification, decision trees, and clustering with analytical estimation accomplished using popular software. Prerequisite: Calculus and statistics knowledge

COMP SCI 5402: Data Mining & Machine Learning
Classical and modern data mining and machine learning algorithms; data preprocessing/warehousing, mining association rules, classification/prediction methods, clustering techniques, Bayesian networks; unsupervised/supervised/reinforcement learning, learning decision trees, artificial neural networks, support vector machines, and ensemble learning. Prerequisites: A "C" or better in both Comp Sci 2300 and one of Stat 3111, Stat 3113, Stat 3115, Stat 3117. Field Trip

COMP SCI 6301: Web Data Management and XML
Management of semi-structured data models and XML, query languages such as XQuery, XML indexing, and mapping of XML data to other data models and vice-versa, XML views and schema management, advanced topics include change-detection, web mining and security of XML data. Prerequisite: A "C" or better grade in COMP SCI 5300

COMP SCI 6302: Heterogeneous and Mobile Databases
This course extensively discusses multidatabase systems (MDBS) and mobile data access systems (MDAS). Moreover, it will study traditional distributed database issues within the framework of MDBSs and MDASs. Prerequisite: A "C" or better grade in COMP SCI 5300

COMP ENG 6330: Clustering Algorithms (Co-listed with Elec Eng 6340, Sys Eng 6214, Comp Sci 6405 and Stat 6239)
An introduction to cluster analysis and clustering algorithms rooted in computational intelligence, computer science and statistics. Clustering in sequential data, massive data and high dimensional data. Students will be evaluated by individual or group research projects and research presentations. Prerequisite: At least one graduate course in statistics, data mining, algorithms, computational intelligence, or neural networks, consistent with student's degree program.

STAT 5814: Applied Time Series Analysis 
Introduction to time series modeling of empirical data observed over time. Topics include stationary processes, auto-covariance functions, moving average, autoregressive, ARIMA, and GARCH models, spectral analysis, confidence intervals, forecasting, and forecast error. Prerequisite: One of STAT 3113 OR 3115 OR 3117 OR 5643 and one of MATH 3103, 3108, or 5108

COMP SCI 6601: Privacy Preserving Data Integration and Analysis
This course covers basic tools, in statistics and cryptography, commonly used to design privacy-preserving and secure protocols in a distributed environment as well as recent advances in the field of privacy preserving data analysis, data sanitization and information retrieval. Prerequisite: A "C" or better grade in both COMP SCI 5300 and COMP SCI 3600

COMP SCI 6600: Computer Security
The course presents various vulnerabilities and threats to information in cyberspace and the principles and techniques for preventing and detecting threats, and recovering from attacks. The course deals with various aspects and layers of security: data-level, network-level, system-level, and application-level security. Prerequisite: A "C" or better grade in both COMP SCI 3600 and COMP SCI 5200

 COMP SCI 6605: Advanced Network Security
Topics covered include network security issues such as authentication, anonymity, traceback, denial of service, confidentiality, forensics, etc. in wired and wireless networks. Students will have a clear, in-depth understanding of state of the art network security attacks and defenses. Prerequisite: A "C" or better grade in either COMP ENG 5420 or COMP SCI 4600

 

 * Curriculum is subject to change. Please contact the department for up-to-date information on courses. Other courses approved by the department may be substituted for any of the above listed courses on a case-by-case basis. The administrative coordinators must approve the substitution prior to enrolling in the course.

 

Admission Requirements

The graduate certificate program is open to all individuals holding a BS degree in computer science, engineering, or a scientific discipline, and who have a minimum of two years of professional experience, or are currently accepted into a graduate degree program at LCIT. The only additional requirement for students entering a graduate certificate program is that they satisfy the prerequisites for any course they take in the program.

The certificate program consists of four courses chosen from three areas. In order to receive a Graduate Certificate, the student must have an sverage graduate cumulative grade point average of 3.0 or better in the certificate courses taken.

Students admitted to the certificate program will have non-degree graduate status, but will earn graduate credit for the courses they complete. If the four course sequence approved by the graduate advisor is completed with a grade of B or better in each of the courses taken, the student will upon application be admitted to the MS program in Computer Science as long as they have a BS in Computer Science, Electrical Engineering, or Computer Engineering, and as long as they meet the minimum undergraduate GPA requirements and core computer science course requirements. All Computer Science certificate courses and up to one non-Computer Science certificate course taken by the students admitted to the program will count towards their computer science MS degree. Once admitted to the program, a student will be given three years to complete the program as long as a B average is maintained in the courses taken.

Contact us

Here is our Contact info :

  •     Phone: +442035198338
  •     Fax: +442035198338

Social Networks

Follow us & get in touch.
 

Newsletter

Make sure you dont miss interesting happenings by joining our newsletter program.