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Big Data Management and Analytics

Graduate Certificate Program

Big Data Management and Analytics

Program Description
As the size and availability of datasets increase, so too do the challenges in efficiently and effectively sharing, analyzing, and visualizing information. Proficiency in big data analytics requires knowledge in interdisciplinary areas including computer science, business information technology, mathematics and statistics, and electrical and computer engineering. This is a 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 management, big data analytics, data mining, cloud computing, and business intelligence.

Curriculum*
The Big Data Management and Analytics 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.

 The following courses are required:

  • COMP SCI 5402: Data Mining & Machine Learning
  • COMP SCI 6304: Cloud Computing and Big Data Management

One of the following courses is required:

  • IST 5420: Introduction to Big Data Analytics
  • COMP ENG 4330 / ELEC ENG 6340 / SYS ENG 6214 /COMP SCI 6300 /STAT 6239: Clustering Algorithms
  • ERP 5410: Use of Business Intelligence
  • COMP SCI 6301: Web Data Management and XML
  • COMP SCI 6302: Heterogeneous and Mobile Databases

One of the following courses is required:

  • COMP SCI 5300: Database Systems
  • IST 6444: Essentials of Data Warehouses
  • COMP SCI 6402: Advanced Topics in Data Mining
  • STAT 5814: Applied Time Series Analysis

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

Course Descriptions

COMP SCI 5402: Data Mining & Machine Learning
Data mining and knowledge discovery utilizes both classical and new algorithms, such as machine learning and neural networks, to discover previously unknown relationships in data. The topics covered will include data preprocessing, mining association rules, classification and prediction methods, and clustering techniques. Prerequisite COMP SCI 2300 and one of STAT 3113 or 3115 or 3117 or 5643.

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 ENG 6330/ ELEC ENG 6340/ SYS ENG 6214 /COMP SCI 6300/ STAT 6239: Clustering Algorithms
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.

ERP 5410: Use of Business Intelligence
This course introduces data-oriented techniques for business intelligence. Topics include Business Intelligence architecture, Business Analytics and Enterprise Reporting. SAP Business Information Warehouse, Business Objects, or similar tools will be used to access and present data, generate reports, and perform analysis. Prerequisite: IST 3423 or equivalent; ERP 2100 or preceded or accompanied by ERP 5110

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 Mobil 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 SCI 5300: Database Systems
This course introduces the advanced database concepts of normalization and functional dependencies, transaction models, concurrency and locking, time stamping, serializability, recovery techniques, and query planning and optimization. Students will participate in programming projects. Prerequisite: A "C" or better grade in both COMP SCI 1200 and COMP SCI 2300

IST 6444: Essentials of Data Warehouses
This course presents the topic of data warehouses and the value to the organization. It takes the student from the database platform to structuring a data warehouse environment. Focus is placed on simplicity and addressing the user community needs. Prerequisite: IST 3423 or equivalent relational database experience. (Co-listed with ERP 6444)

COMP SCI 6402: Advanced Topics in Data Mining
Advanced topics of current interest in the field of data mining. This course involves reading seminal and state-of-the-art papers as well as conducting topical research projects including design, implementation, experimentation, analysis, and written and oral reporting components. Prerequisite: A "C" or better grade in COMP SCI 6402. (Co-listed with COMP ENG 6302 and SYS ENG 6216)

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, 3115, 3117, 5643 and one of MATH 3103, 3108, or 5108

 * 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: two are core courses and two are elective courses. In order to receive a Graduate Certificate, the student must have an average 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.

 

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