Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
1BBM467DATA INTENSIVE APPLICATIONS3+0+03606.09.2024

 
Course Details
Language of Instruction English
Level of Course Unit Bachelor's Degree
Department / Program COMPUTER ENGINEERING
Type of Program Formal Education
Type of Course Unit Elective
Course Delivery Method Face To Face
Objectives of the Course To teach students fundamentals of big data management and analytics. They will gain experience with some key technologies, platforms, tools and systems used by big data scientists and engineers.
Course Content Big Data, Distributed Computing, Cluster Computing, Scalable Machine Learning, Cloud Computing and Virtualization, Graph Analytics, Data and Ethics.
Course Methods and Techniques Lecture, Discussion, Question and Answer, Problem Solving
Prerequisites and co-requisities ( BBM469 ) and ( BBM104 ) and ( BBM102 )
Course Coordinator None
Name of Lecturers Asist Prof.Dr. Fuat Akal
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources ?Mining of Massive Datasets (2nd Edition)?, Jure Leskovec (Author), Anand Rajaraman (Author), Jeffrey David Ullman (Author), Cambridge University Press, 2014. ?Learning Spark: Lightning-Fast Big Data Analysis?, Holden Karau and Andy Konwinski, O'Reilly, 2015.
Course Notes “Mining of Massive Datasets (2nd Edition)”, Jure Leskovec (Author), Anand Rajaraman (Author), Jeffrey David Ullman (Author), Cambridge University Press, 2014.

“Learning Spark: Lightning-Fast Big Data Analysis”, Holden Karau and Andy Konwinski, O'Reilly, 2015.

“Architecting Modern Data Platforms: A Guide to Enterprise Hadoop at Scale”, Jan Kunigk, Ian Buss, Paul Wilkinson and Lars George, O'Reilly, 2019.

“Cloud Computing: A Practical Approach”, Robert C. Elsenpeter and Toby Velte, McGraw-hill, 2009.


Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria
In-Term Studies Quantity Percentage
Midterm Exam 2 % 50
Attendance 1 % 10
Final examination 1 % 40
Total
4
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Course Duration 14 3 42
Hours for off-the-c.r.stud 14 6 84
Preparation for Midterm Exam 2 15 30
General Exam Preparation 1 24 24
Total Work Load   Number of ECTS Credits 6 180

 
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 1. Students will be able to describe the Big Data and identify what are and what are not big data problems.
2 2. Students will be able to understand the architectural components and programming models used for scalable big data analysis.
3 3. Students will be able to learn how to make sense of huge volumes of data.
4  
5  
6  
7  
8  

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 What is Big Data?
2 Introduction to Data Science
3 Foundations for Big Data Systems: Hadoop
4 Cloud Computing and Virtualization
5 Semi-structured Data
6 NOSQL Data Stores
7 Midterm Exam
8 Visualization
9 Big Data Integration
10 Processing Big Data
11 Scalable Machine Learning
12 Midterm Exam
13 Graph Analytics
14 Data and Ethics
15 Preparation to Final Exam
16 Final exam

 
Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12
All 4 4 3 4 4 1 1 3 3 2 2
C1
C2
C3
C4
C5
C6
C7
C8

  Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant

  
  https://bilsis.hacettepe.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=2687546&lang=en