Big Data Analytics (CMPE543) Course Detail

Course Name Course Code Season Lecture Hours Application Hours Lab Hours Credit ECTS
Big Data Analytics CMPE543 Elective Courses 3 0 0 3 5
Pre-requisite Course(s)
N/A
Course Language English
Course Type Elective Courses
Course Level Natural & Applied Sciences Master's Degree
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 X
Supportive Courses
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 An ability to apply knowledge of mathematics, science, and engineering. X
2 An ability to design and conduct experiments, as well as to analyse and interpret data. X
3 An ability to design a system, component, or process to meet desired needs. X
4 An ability to function on multi-disciplinary domains. X
5 An ability to identify, formulate, and solve engineering problems. X
6 An understanding of professional and ethical responsibility. X
7 An ability to communicate effectively. X
8 Recognition of the need for, and an ability to engage in life-long learning. X
9 A knowledge of contemporary issues. X
10 An ability to use the techniques, skills, and modern engineering tools necessary for engineering practice. X
11 Skills in project management and recognition of international standards and methodologies X
12 An ability to produce engineering products or prototypes that solve real-life problems. X
13 Skills that contribute to professional knowledge. X
14 An ability to make methodological scientific research. X
15 An ability to produce, report and present an original or known scientific body of knowledge. X
16 An ability to defend an originally produced idea. 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