| Course Objectives: |
The objective of this course is to introduce students to the concept of big data and to provide them with knowledge of current technologies used in processing, storing, and analyzing this data. The course covers the architectures and application areas of big data platforms such as Hadoop, Spark, and NoSQL databases. It also aims to develop students' data processing, analysis, and management skills in big data environments. A key objective of the course is to develop competence in selecting and applying appropriate technologies to address real-world big data problems. |
| Course Content: |
This course covers the concept of big data, its characteristics, and application areas. Within the big data ecosystem, distributed file systems and parallel data processing approaches are examined. The Hadoop ecosystem, Hadoop Distributed File System (HDFS), and MapReduce architecture; the core components and data processing model of Apache Spark are included in the course content. Furthermore, NoSQL databases, data models, and use cases are discussed and compared with relational databases. Throughout the course, data collection, storage, processing, and analysis processes in big data environments are examined through application examples; students are supported in identifying and implementing appropriate technologies for different big data problems. |
Course Learning Outcomes (CLOs) are those describing the knowledge, skills and competencies that students are expected to achieve upon successful completion of the course. In this context, Course Learning Outcomes defined for this course unit are as follows:
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| Knowledge
(Described as Theoritical and/or Factual Knowledge.)
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1) Having knowledge about basic information technologies and computer systems.
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| Skills
(Describe as Cognitive and/or Practical Skills.)
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1) Having practical knowledge about the technologies used in big data environments.
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2) To be able to produce technological solutions to real-world big data problems.
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| Competences
(Described as "Ability of the learner to apply knowledge and skills autonomously with responsibility", "Learning to learn"," Communication and social" and "Field specific" competences.)
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1) Gaining competence in data analysis, data processing and database management.
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2) Ability to use appropriate software and tools in the data analysis process.
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Programme Learning Outcomes |
Contribution Level (from 1 to 5) |
| 1) |
It explains fundamental concepts in mathematics, statistics, and probability; and applies this knowledge to data analysis, modeling, and interpretation of results. |
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| 2) |
It explains the principles of algorithm design and develops software for solving problems using at least one programming language. |
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| 3) |
It compares machine learning and data mining algorithms, selects the appropriate method, and applies it to real data. |
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| 4) |
Big data platforms utilize distributed systems and cloud computing architectures to perform data processing operations. |
5 |
| 5) |
They apply natural language processing techniques to text data and develop basic NLP-based applications. |
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| 6) |
It analyzes different data sources, transforms them into meaningful outputs, and presents them using appropriate visualization tools. |
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| 7) |
It creates data-driven decision models using decision support systems. |
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| 8) |
It develops optimization models and produces solutions for industrial and sectoral problems. |
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| 9) |
In professional practice, we operate within the framework of ethical principles, data security, and social responsibility. |
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| 10) |
They keep up with current technological developments in their field, actively participate in teamwork, and develop a lifelong learning awareness. |
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| WORKLOAD OF TEACHING & LEARNING ACTIVITIES |
| Teaching & Learning Activities |
# of Activities per semester |
Duration (hour) |
Total Workload |
| Course |
14 |
3 |
42 |
| Laboratory |
0 |
0 |
0 |
| Application |
14 |
3 |
42 |
| Special Course Internship (Work Placement) |
0 |
0 |
0 |
| Field Work |
0 |
0 |
0 |
| Study Hours Out of Class |
0 |
0 |
0 |
| Presentations / Seminar |
0 |
0 |
0 |
| Project |
1 |
1 |
1 |
| Homework Assignments |
1 |
10 |
10 |
| Total Workload of Teaching & Learning Activities |
- |
- |
95 |
| WORKLOAD OF ASSESMENT & EVALUATION ACTIVITIES |
| Assesment & Evaluation Activities |
# of Activities per semester |
Duration (hour) |
Total Workload |
| Quizzes |
1 |
1 |
1 |
| Midterms |
1 |
20 |
20 |
| Semester Final Exam |
1 |
30 |
30 |
| Total Workload of Assesment & Evaluation Activities |
- |
- |
51 |
| TOTAL WORKLOAD (Teaching & Learning + Assesment & Evaluation Activities) |
146 |
| ECTS CREDITS OF THE COURSE (Total Workload/25.5 h) |
6 |