| Course Type | Elective Course |
|---|
| Course Level | Master's Degree Without 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 | E-Learning |
|---|
| 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 |
Introduction to data mining |
|
2nd Week |
Data mining tasks |
|
3rd Week |
Data mining tasks |
|
4th Week |
Components of data mining |
|
5th Week |
Components of data mining |
|
6th Week |
Comparison of data mining and the other analytical methods |
|
7th Week |
Comparison of data mining and the other analytical methods |
|
8th Week |
Midterm |
|
9th Week |
Decision processes, business intelligence and data mining |
|
10th Week |
Methodology of data mining |
|
11th Week |
Classification of data mining methods |
|
12th Week |
Statistical infrastructure of data mining |
|
13th Week |
Data mining methods and their applications |
|
14th Week |
Data mining methods 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 |
- Questions/Answers
- Problem Solving
- Experiment
- Practice
- Lecture
- Discussion
|
|---|
| Assessment Methods and Criteria |
|
Quantity |
Percentage (%) |
| Project |
1 |
20 |
| Total(%) |
|
20 |
| Contribution of In-term Studies to Overall Grade(%) |
|
20 |
| Contribution of Final Examination to Overall Grade(%) |
|
80 |
| Total(%) |
|
100 |
|
| ECTS Allocated Based on Student Workload |
| Activities |
Quantity |
Duration (Hour) |
Total Workload (Hour) |
| Midterm exam |
|
|
|
| Preparation for Quiz |
|
|
|
| Individual or group work |
14 |
10 |
140 |
| Preparation for Final exam |
1 |
55 |
55 |
| Course hours |
14 |
1 |
14 |
| Preparation for Midterm exam |
1 |
45 |
45 |
| Laboratory (including preparation) |
|
|
|
| Final exam |
1 |
3 |
3 |
| Homework |
|
|
|
| Project |
1 |
35 |
35 |
| Total Work Load | 292 |
| Total Work Load / 30 (h) | 9.73 |
| ECTS Credit of the Course | 10 |
|
| Language of Instruction | Turkish |
|---|
| Work Placement(s) | No |
|---|
