Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
1BBM469DATA INTENSIVE APPLICATIONS LAB.0+2+01406.09.2024

 
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 some key technologies, platforms, tools and systems used in big data Big Data management and analysis.
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, Problem solving, Experiment
Prerequisites and co-requisities ( BBM102 ) and ( BBM104 ) and ( BBM467 )
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. ?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.
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
Assignment 5 % 50
Attendance 1 % 10
Final examination 1 % 40
Total
7
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Course Duration 14 1 14
Hours for off-the-c.r.stud 14 2 28
Assignments 5 12 60
General Exam Preparation 1 20 20
Total Work Load   Number of ECTS Credits 4,06666666666667 122

 
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 1. Students will gain experience with installation and deployment of Big Data management systems.
2 2. Students will gain practical experience about distributed programming.
3 3. Students will gain experience about programming for data science.
4  
5  
6  
7  
8  

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Understanding Big Data Platforms
2 Data Science by Using Python
3 Installing and Using Hadoop
4 Exploring Hadoop Ecosystem
5 Cloud Computing Models
6 Creating Virtual Machines
7 Packaging Applications in Containers
8 Building a structured peer-to-peer network
9 Big Data Integration and Processing
10 Big Data Analytics using Spark
11 Visualization of Data
12 Graph Analytics Techniques
13 Computing Platforms for Graph Analytics
14 Anonymization of Data
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 5 3 4 4 4 3 4 3 3 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=2687421&lang=en