ELECTRIC-ELECTRONIC ENGINEERING (ENGLISH, NON-THESIS)
Master TR-NQF-HE: Level 7 QF-EHEA: Second Cycle EQF-LLL: Level 7

Course Introduction and Application Information

Course Code Course Name Semester Theoretical Practical Credit ECTS
BDA5011 Big Data and Analytics Fall 3 0 3 8
The course opens with the approval of the Department at the beginning of each semester

Basic information

Language of instruction: En
Type of course: Departmental Elective
Course Level:
Mode of Delivery: Face to face
Course Coordinator : Dr. Öğr. Üyesi SERKAN AYVAZ
Course Objectives: This course provides an overview of the fields of big data analytics and data science. Topics are covered in the context of data analytics include the terminology and the core concepts behind big data problems, applications, and systems. In this course, the students learn how to use Hadoop and related Big Data Processing tools that are used for scalable big data analysis and have made it easier and more accessible.

Learning Outputs

The students who have succeeded in this course;
• Get a broad understanding of this high trend Big Data and NoSQL concepts
• Learn big data analytics skills that are highly demanded on the market currently.
• Be able to implement and use MapReduce programs and NoSQL databases.

Course Content

In this course, the technologies associated with big data analytics including NoSQL databases, moving data into Hadoop, real-time data analysis using HBase, big data analytical tools such as Apache Hive and Pig will be covered.

Weekly Detailed Course Contents

Week Subject Related Preparation
1) Discussion of course contents, Overview of Hadoop ecosystem
2) Hadoop Architecture, Basic Linux Commands, Hadoop Installation
3) Data Transfer into and out from Hadoop (Hands on Exercise -SQOOP)
4) Overview of Apache Pig and Pig Latin Basics
5) Pig Latin Operators and Examples
6) Programming with Pig and Examples
7) Overview of Hive and Hive Architecture
8) Hands on HIVE Exercises in Configuration, Database and table operations
9) Hands on HIVE Exercises in Partitions, Buckets, Operators and Built in Functions, Loading data and Overview of Impala
10) Hands on HIVE Exercises in Views and Indexes, HIVEQL –Where, Order By and JOINS
11) Overview of Spark and Spark Architecture
12) Spark & Hands on Exercises
13) Spark & Hands on Exercises
14) Student Presentations of Group Assignments

Sources

Course Notes: Lecture notes will be provided.
References: Big Data Science & Analytics: A Hands-On Approach. Bahga, A. and Madisetti, V., 2016.

Evaluation System

Semester Requirements Number of Activities Level of Contribution
Attendance % 0
Laboratory % 0
Application % 0
Field Work % 0
Special Course Internship (Work Placement) % 0
Quizzes % 0
Homework Assignments % 0
Presentation % 0
Project 1 % 20
Seminar % 0
Midterms 1 % 30
Preliminary Jury % 0
Final 1 % 50
Paper Submission % 0
Jury % 0
Bütünleme % 0
Total % 100
PERCENTAGE OF SEMESTER WORK % 30
PERCENTAGE OF FINAL WORK % 70
Total % 100

ECTS / Workload Table

Activities Number of Activities Duration (Hours) Workload
Course Hours 14 3 42
Laboratory 0 0 0
Application 0 0 0
Special Course Internship (Work Placement) 0 0 0
Field Work 0 0 0
Study Hours Out of Class 14 10 140
Presentations / Seminar 0 0 0
Project 1 12 12
Homework Assignments 0 0 0
Quizzes 0 0 0
Preliminary Jury 0 0 0
Midterms 1 3 3
Paper Submission 0 0 0
Jury 0 0 0
Final 1 3 3
Total Workload 200

Contribution of Learning Outcomes to Programme Outcomes

No Effect 1 Lowest 2 Low 3 Average 4 High 5 Highest
           
Program Outcomes Level of Contribution
1) Have sufficient background and an ability to apply knowledge of mathematics, science, and engineering to identify, formulate, and solve problems of electrical and electronics engineering.
2) Be able to define, formulate and solve sophisticated engineering problems by choosing and applying appropriate analysis and modeling techniques and using technical symbols and drawings of electrical and electronics engineering for design, application and communication effectively.
3) Have an ability to design or implement an existing design of a system, component, or process to meet desired needs within realistic constraints (realistic constraints may include economic, environmental, social, political, health and safety, manufacturability, and sustainability issues depending on the nature of the specific design).
4) Elektrik ve elektronik mühendisliği yapabilmek ve yeni uygulamalara uyum gösterebilmek için gerekli yenilikçi ve güncel teknikler, beceriler, bilgi teknolojileri ve modern mühendislik araçlarını geliştirmek, seçmek, uyarlamak ve kullanmak.
5) Be able to design and conduct experiments, as well as to collect, analyze, and interpret relevant data, and use this information to improve designs.
6) Be able to function individually as well as to collaborate with others in multidisciplinary teams.
7) Be able to communicate effectively in English and Turkish (if he/she is a Turkish citizen).
8) Be able to recognize the need for, and to engage in life-long learning as well as a capacity to adapt to changes in the technological environment.
9) Have a consciousness of professional and ethical responsibilities as well as workers’ health, environment and work safety.
10) Have the knowledge of business practices such as project, risk, management and an awareness of entrepreneurship, innovativeness, and sustainable development.
11) Have the broad knowledge necessary to understand the impact of electrical and electronics engineering solutions in a global, economic, environmental, legal, and societal context.