| Course Type | Elective Course |
|---|
| Course Level | Master's Degree With Thesis |
|---|
| Year of Study | - |
|---|
| Semester | - |
|---|
| ECTS | 10 |
|---|
| Name of Lecturer(s) |
|
| Learning Outcomes |
At the end of this course, the students;
- know basic big data concepts and terminology
- understand the business impacts of big data
- understand fundamental big data processing approaches
- know big data storage/processing technologies
|
| Mode of Delivery | Face to face |
|---|
| Prerequisites | No |
|---|
| Recommended Optional Programme Components | |
|---|
| Course Description | Big Data is a new field impacting many areas of science, engineering, and industry. Nowadays, data is increasing in volume, is collected faster and has a more complex structure. The companies need to analyze big data in order to increase efficiency, decrease cost and reach the customer easily. In the scope of this course, the fundamentals concepts of big data will be introduced and the fundamental platforms (such as Hadoop, Spark) for data analysis will be introduced. Also, data storage methods, data processing methods and various algorithms will be presented in the course. Lastly, data visualization will be introduced. |
|---|
| Course Contents |
| WEEK |
TOPICS |
|
1st Week |
Big data: concepts and terminology |
|
2nd Week |
Big data: business motvations and drivers |
|
3rd Week |
Big data: adoption and planning |
|
4th Week |
Big data: business intelligence |
|
5th Week |
Distributed Storage and Distributed Processing Apache Hadoop |
|
6th Week |
Data Warehouse, NoSQL databases |
|
7th Week |
MapReduce I |
|
8th Week |
Midterm |
|
9th Week |
MapReduce II |
|
10th Week |
Apache Hive |
|
11th Week |
Sqoop and Kafka |
|
12th Week |
Big data visualization |
|
13th Week |
Case studies |
|
14th Week |
Project presentation |
|
15th Week |
Review |
|
| Recommended or Required Reading | Tom White. Hadoop: The Definitive Guide Third Edition |
|---|
| Planned Learning Activities and Teaching Methods |
|
|---|
| Assessment Methods and Criteria |
|
Quantity |
Percentage (%) |
| Mid-term |
1 |
30 |
| Project |
1 |
30 |
| Total(%) |
|
60 |
| Contribution of In-term Studies to Overall Grade(%) |
|
60 |
| Contribution of Final Examination to Overall Grade(%) |
|
40 |
| Total(%) |
|
100 |
|
| ECTS Allocated Based on Student Workload |
| Activities |
Quantity |
Duration (Hour) |
Total Workload (Hour) |
| Midterm exam |
1 |
2 |
2 |
| Preparation for Quiz |
|
|
|
| Individual or group work |
14 |
7 |
98 |
| Preparation for Final exam |
1 |
50 |
50 |
| Course hours |
14 |
3 |
42 |
| Preparation for Midterm exam |
1 |
45 |
45 |
| Laboratory (including preparation) |
|
|
|
| Final exam |
1 |
2 |
2 |
| Homework |
|
|
|
| Project |
1 |
50 |
50 |
| Total Work Load | 289 |
| Total Work Load / 30 (h) | 9.63 |
| ECTS Credit of the Course | 10 |
|
| Language of Instruction | Turkish |
|---|
| Work Placement(s) | No |
|---|
