| 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;
- Learn big data concepts, terminology, data analytics features, big data types.
- Comprehend analysis techniques such as qualitative and quantitative data mining, statistical analysis, A/B testing, correlation, regression analysis.
- Master storage concepts such as clustering, distributed file systems, relational database systems, in-memory storage and big data processing concepts such as parallel, distributed, batch data processing.
|
| Mode of Delivery | Face to face |
|---|
| Prerequisites | No |
|---|
| Recommended Optional Programme Components | None |
|---|
| Course Description | Big data in general terms refers to data that is structured, semi-structured or unstructured, requires large storage areas, and is difficult to process in reasonable times with known software tools. The aim of this course is to introduce the basic concepts and methods in big data analysis and data science and to enable students to understand the basic features of the use of "Big Data" in the real world. |
|---|
| Course Contents |
| WEEK |
TOPICS |
|
1st Week |
What is big data? |
|
2nd Week |
Big data and open data: key concepts |
|
3rd Week |
Big data technologies and tools |
|
4th Week |
Data mining with big data |
|
5th Week |
Big data security - privacy |
|
6th Week |
Data visualization, communication and machine learning |
|
7th Week |
Big data analysis and method |
|
8th Week |
Midterm |
|
9th Week |
Cyber security |
|
10th Week |
Big data political and legal framework in Turkey |
|
11th Week |
Big data use in public-private organizations - examples |
|
12th Week |
Big data and applications in the mobile communications industry |
|
13th Week |
Social media analytics data |
|
14th Week |
Social media analytics techniques and their applications |
|
| Recommended or Required Reading | 1. Big Data Fundamentals: Concepts, Drivers & Techniques (1st ed.). Thomas Erl, Wajid Khattak, and Paul Buhler. Prentice Hall Press, Upper Saddle River, NJ, USA. 2016.
2. Big Data, Principles and Best Practices of Scalable Realtime Data Systems, Nathan Marz and James Warren, Manning Publications 2015. |
|---|
| Planned Learning Activities and Teaching Methods |
- Lecture
- Discussion
- Questions/Answers
- Problem Solving
- Experiment
- Practice
|
|---|
| Assessment Methods and Criteria |
|
Quantity |
Percentage (%) |
| Mid-term |
1 |
25 |
| Practice |
1 |
20 |
| Project |
1 |
10 |
| Total(%) |
|
55 |
| Contribution of In-term Studies to Overall Grade(%) |
|
55 |
| Contribution of Final Examination to Overall Grade(%) |
|
45 |
| Total(%) |
|
100 |
|
| ECTS Allocated Based on Student Workload |
| Activities |
Quantity |
Duration (Hour) |
Total Workload (Hour) |
| Midterm exam |
1 |
3 |
3 |
| Preparation for Quiz |
|
|
|
| Individual or group work |
13 |
10 |
130 |
| Preparation for Final exam |
1 |
45 |
45 |
| Course hours |
13 |
3 |
39 |
| Preparation for Midterm exam |
1 |
30 |
30 |
| Laboratory (including preparation) |
|
|
|
| Final exam |
1 |
3 |
3 |
| Homework |
1 |
20 |
20 |
| Project |
1 |
30 |
30 |
| Total Work Load | 300 |
| Total Work Load / 30 (h) | 10 |
| ECTS Credit of the Course | 10 |
|
| Language of Instruction | Turkish |
|---|
| Work Placement(s) | No |
|---|
