Big Data Analytics (CMPE543) Course Detail

Course Name Course Code Season Lecture Hours Application Hours Lab Hours Credit ECTS
Big Data Analytics CMPE543 Area Elective 3 0 0 3 5
Pre-requisite Course(s)
N/A
Course Language English
Course Type Computer Engineering Elective Courses
Course Level Ph.D.
Mode of Delivery Face To Face
Learning and Teaching Strategies Lecture.
Course Coordinator
Course Lecturer(s)
Course Assistants
Course Objectives The objective of this course is to present methods and technologies for sharing, visualizing, classifying, and analyzing big data.
Course Learning Outcomes The students who succeeded in this course;
  • Choose the right data format and service model to host and share big data.
  • Develop a noSQL-based web application.
  • Create queries by using Hadoop, Hive, and Shark.
  • Evaluate visualization strategies for exploring large datasets.
  • Build data processing pipelines by using MapReduce model and data transformation workflows
  • Analyze big data by using R
Course Content Infrastructure as a Service(IaaS), Hadoop framework, hive infrastrucure, data visualization, MapReduce model, NoSQL databases, large-scale data workflows, clustering, using R.

Weekly Subjects and Releated Preparation Studies

Week Subjects Preparation
1 Introduction Chapter 1 (Text Book)
2 Hosting and Sharing Big Data Chapter 2 (Text Book)
3 Non-relational databases Chapter 3 (Text Book)
4 Processing with Big Data Chapter 4 (Text Book)
5 Using Hadoop Chapter 5 (Text Book)
6 Building a Data Dashboard Chapter 6 (Text Book)
7 Visualization Big Data Chapter 7 (Text Book)
8 Map Reduce Model Chapter 8 (Text Book)
9 Map Reduce Model Chapter 8 (Text Book)
10 Data Transformation Workflows Chapter 9 (Text Book)
11 Data Classification with Mahout Chapter 10 (Text Book)
12 Statistical Analysis with R Chapter 11 (Text Book)
13 Building Analytics Workflows Chapter 12 (Text Book)
14 Building Analytics Workflows Chapter 12 (Text Book)
15 Review
16 Review

Sources

Course Book 1. Data Just Right: Introduction to Large-Scale Data & Analytics”, M. Manoochehri, Addison-Wesley, 2013
Other Sources 2. “Mining of Massive Datasets”, A. Rajaraman & J. D: Ullman, Cambridge University Press, 2011.
3. Apache Hadoop Project, available at http://hadoop.apache.org/

Evaluation System

Requirements Number Percentage of Grade
Attendance/Participation - -
Laboratory - -
Application - -
Field Work - -
Special Course Internship - -
Quizzes/Studio Critics - -
Homework Assignments - -
Presentation - -
Project 3 30
Report - -
Seminar - -
Midterms Exams/Midterms Jury 1 35
Final Exam/Final Jury 1 35
Toplam 5 100
Percentage of Semester Work 65
Percentage of Final Work 35
Total 100

Course Category

Core Courses
Major Area Courses
Supportive Courses X
Media and Managment Skills Courses
Transferable Skill Courses

The Relation Between Course Learning Competencies and Program Qualifications

# Program Qualifications / Competencies Level of Contribution
1 2 3 4 5
1 To become familiar with the state-of-the art and the literature in the software engineering research domain X
2 An ability to conduct world-class research in software engineering and publish scholarly articles in top conferences and journals in the area
3 Be able to conduct quantitative and qualitative studies in software engineering X
4 Acquire skills needed to bridge software engineering academia and industry and to develop and apply scientific software engineering approaches to solve real-world problems X
5 An ability to access information in order to follow recent developments in science and technology and to perform scientific research or implement a project in the software engineering domain. X
6 An understanding of professional, legal, ethical and social issues and responsibilities related to Software Engineering.
7 Skills in project and risk management, awareness about importance of entrepreneurship, innovation and long-term development, and recognition of international standards of excellence for software engineering practices standards and methodologies. X
8 An understanding about the impact of Software Engineering solutions in a global, environmental, societal and legal context while making decisions.
9 Promote the development, adoption and sustained use of standards of excellence for software engineering practices. X

ECTS/Workload Table

Activities Number Duration (Hours) Total Workload
Course Hours (Including Exam Week: 16 x Total Hours) 16 3 48
Laboratory
Application
Special Course Internship
Field Work
Study Hours Out of Class 16 2 32
Presentation/Seminar Prepration
Project
Report
Homework Assignments 3 5 15
Quizzes/Studio Critics
Prepration of Midterm Exams/Midterm Jury 1 10 10
Prepration of Final Exams/Final Jury 1 20 20
Total Workload 125