COMPUTER ENGINEERING (ENGLISH, PHD) | |||||
PhD | TR-NQF-HE: Level 8 | QF-EHEA: Third Cycle | EQF-LLL: Level 8 |
Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
BDA5011 | Big Data and Analytics | Fall | 3 | 0 | 3 | 12 |
The course opens with the approval of the Department at the beginning of each semester |
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. |
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. |
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. |
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 |
Course Notes: | Lecture notes will be provided. |
References: | Big Data Science & Analytics: A Hands-On Approach. Bahga, A. and Madisetti, V., 2016. |
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 |
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 |
No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
Program Outcomes | Level of Contribution | |
1) | Ability to identify and apply advanced concepts in computer engineering | |
2) | Cooperate efficiently in intra-disciplinary and multi-disciplinary teams. | |
3) | Have theoretical and practical basis in computer engineering and science to be able to conduct related academic research independently. | |
4) | Ability to apply advanced mathematical and engineering knowledge on real problems. | |
5) | Ability to search the scientific literature related to a research project and find the relationships with own research | |
6) | Ability to interprete scientific research and use the findings | |
7) | Ability to work efficiently in interdisciplinary research teams | |
8) | Ability to attain scientific knowledge | |
9) | Ability find ways to improve upon current knowledge | |
10) | Ability to present research findings in seminars and conferences | |
11) | Ability to write research progress reports by referring to published theses and papers. | |
12) | Ability to show the responsibility of professional and ethical behavior |