In this course, we will cover fundamental aspects of Data Management including traditional data management as well as new models for big data. We will start with conceptual data modelling (ER and UML models), then study relational model, and how conceptual models could be converted to relational model. We will cover SQL language for querying relational data. We will continue with more recent models such as key-value stores, document databases and graph databases. Students will do practical work on database systems such as MySQL, Cassandra, and MongoDB.
Introduction to Data Modeling and Processing (DA 505)
2021 Fall
Faculty of Engineering and Natural Sciences
Data Analytics(DA)
3
6
Hasan Sait Ölmez olmez@sabanciuniv.edu,
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Doctoral, Master
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CONTENT
PROGRAMME OUTCOMES
1. Develop the ability to use critical, analytical, and reflective thinking and reasoning 5
2. Reflect on social and ethical responsibilities in his/her professional life. 2
3. Gain experience and confidence in the dissemination of project/research outputs 2
4. Work responsibly and creatively as an individual or as a member or leader of a team and in multidisciplinary environments. 3
5. Communicate effectively by oral, written, graphical and technological means and have competency in English. 3
6. Independently reach and acquire information, and develop appreciation of the need for continuously learning and updating. 4
1. Design and model engineering systems and processes and solve engineering problems with an innovative approach. 3
2. Establish experimental setups, conduct experiments and/or simulations. 2
3. Analytically acquire and interpret data. 5
1. Comprehend the conceptual foundations of analytical methods and techniques within the scope of business analytics, 5
2. Acquire theoretical and practical knowledge on applied information systems by developing fundamental programming skills, 4
3. Improve decision making by turning high-volume data into useful information and integrating data analysis tools 4
4. Turn high-volume data into useful information by using quantitative models and understanding and managing data analysis techniques, communicate and visualize the results for business use 4
5. Understand the data quality, data integrity and data accuracy concepts, and occupational ethics regarding data privacy and intellectual property 4