| School/Faculty/Institute |
Graduate School |
| Course Code |
ITC 505 |
| Course Title in English |
Big Data Analytics and Management |
| Course Title in Turkish |
Big Data Analytics and Management |
| Language of Instruction |
EN |
| Type of Course |
Exercise,Flipped Classroom,Lecture |
| Level of Course |
Intermediate |
| Semester |
Fall |
| Contact Hours per Week |
| Lecture: 3 |
Recitation: |
Lab: |
Other: |
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| Estimated Student Workload |
174 hours per semester |
| Number of Credits |
7.5 ECTS |
| Grading Mode |
Standard Letter Grade
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| Pre-requisites |
None |
| Expected Prior Knowledge |
Querying on RDBMS systems |
| Co-requisites |
None |
| Registration Restrictions |
Only Graduate Students |
| Overall Educational Objective |
To learn the basic designing relational database and big data systems. |
| Course Description |
Big Data Analysis is the hot topic job nowadays. But it’s a big problem too. In this lesson’s aim is how to query on RDBMS and big data ecosystems products, designing modern edition data warehouses and managing massively parallel processing data warehouse technologies on cloud platforms. We will start to ask a few questions: What’s the problem of data world. What’s the technologies? Why does this technology exist and why do I need it? How can I get the best out of it utilizing something familiar like SQL. How can I design and query on RDBMS system, Hadoop ecosystem products like Pig Latin, Hive, Spark etc. and MPP products like Azure SQL DW, AWS Redshift, Azure Stream Analytics, Big Data Lake Analytics etc. |
| Course Description in Turkish |
Bu ders büyük veri analizine giriş olarak tasarlanmıştır. Günümüzde gittikçe önem kazanan ve aynı zamanda bir problem olarak karşımıza çıkan büyük veri yapılarının tasarlanması ve üzerinde çalışılması hedeflenmektedir. Derse bir kaç soru ile başlayacağız. Gerçek dünyanın veri dünyasındaki problemleri nelerdir ve bu problemler için hangi teknolojiler mevcuttur. Hangi problemde hangi teknolojileri ve muadillerini kullanmalıyız. Veritabanı ve büyük veri ekosistemindeki ürünlerin tasarlanması ve sorgulanması için gerekli programlama dillerinin öğrenilmesi ve ürünlerin kurulumlarının gerçekleştirilerek üzerinde gerçek dünya ile ilgili problemlerin çözümlerinin sunulması. |
Course Learning Outcomes and Competences
Upon successful completion of the course, the learner is expected to be able to:
1) Design and querying OLTP Systems, some of NoSQL Products and Hadoop products.
2) Selecting the right products (compare other products) to related with problems
3) Designing of modern data warehouse architecture
4) Execution on real world problems and data systems
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| Program Learning Outcomes/Course Learning Outcomes |
1 |
2 |
3 |
4 |
| 1) An ability to develop and deepen one's knowledge in the field of mechatronics and robotics engineering at the level of expertise based on acquired undergraduate level qualifications. |
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| 2) An ability to acquire scientific and practical knowledge in mechatronics and robotics. |
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| 3) A comprehensive knowledge about analysis and modeling methods in mechatronics and their limitations. |
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| 4) An ability to design and apply analytical, modeling and experimental based research by analyzing and interpreting complex situations encountered in the design process. |
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| 5) An ability to transmit the process and results of the work of mechatronics and robotics systems systematically and clearly in written and oral form in national and international environments. |
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| 6) An ability to recognize social, scientific and ethical values in the stages of designing and realizing mechatronics and robotic systems and in all professional activities. |
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| 7) An ability to follow new and developing practices in the profession and to apply them in their work. |
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| 8) An ability to take leadership in multi-disciplinary teams, taking responsibility in the design and analysis of mechatronics and robotic systems in complex situations. |
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| 9) An ability to communicate verbally and in writing in English at least at the level of B2 of European Language Portfolio. |
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| 10) An understanding of the social and environmental aspects of mechatronics and robotics applications. |
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Relation to Program Outcomes and Competences
| N None |
S Supportive |
H Highly Related |
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Program Outcomes and Competences |
Level |
Assessed by |
| 1) |
An ability to develop and deepen one's knowledge in the field of mechatronics and robotics engineering at the level of expertise based on acquired undergraduate level qualifications. |
N |
|
| 2) |
An ability to acquire scientific and practical knowledge in mechatronics and robotics. |
N |
|
| 3) |
A comprehensive knowledge about analysis and modeling methods in mechatronics and their limitations. |
N |
|
| 4) |
An ability to design and apply analytical, modeling and experimental based research by analyzing and interpreting complex situations encountered in the design process. |
N |
|
| 5) |
An ability to transmit the process and results of the work of mechatronics and robotics systems systematically and clearly in written and oral form in national and international environments. |
N |
|
| 6) |
An ability to recognize social, scientific and ethical values in the stages of designing and realizing mechatronics and robotic systems and in all professional activities. |
N |
|
| 7) |
An ability to follow new and developing practices in the profession and to apply them in their work. |
N |
|
| 8) |
An ability to take leadership in multi-disciplinary teams, taking responsibility in the design and analysis of mechatronics and robotic systems in complex situations. |
N |
|
| 9) |
An ability to communicate verbally and in writing in English at least at the level of B2 of European Language Portfolio. |
N |
|
| 10) |
An understanding of the social and environmental aspects of mechatronics and robotics applications. |
N |
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| Prepared by and Date |
, |
| Course Coordinator |
İLKER BEKMEZCİ |
| Semester |
Fall |
| Name of Instructor |
Prof. Dr. İLKER BEKMEZCİ |
Course Contents
| Week |
Subject |
| 1) |
1. History of Data
2. Vendors of Big Data
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| 2) |
Data Warehousing and Business Intelligence Insights
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| 3) |
Designing & Querying on OLTP Systems
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| 4) |
Designing ETL Layer
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| 5) |
Sql On Hadoop
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| 6) |
Real Time Analytics |
| 7) |
Working with Unstructured Data |
| 8) |
Massively Parallel Processing Products |
| 9) |
Massively Parallel Processing Products |
| 10) |
Data Visualization |
| 11) |
Advanced Analytics |
| 12) |
Advanced Analytics |
| 13) |
Big Data Lake Analytics |
| 14) |
Big Data Lake Analytics |
| 15) |
Final Examination Period |
| 16) |
Final Examination Period |
Information Package / Course Catalogue