This course aims at discussing the key principles of knowledge discovery process through various case studies arising from different application areas. The students are expected to learn the main steps to traverse when they face new data analytics problems. With each case study, the tools for cleaning, processing and altering the data shall be visited. A particular attention shall be given to data inspection, feature reduction and model selection. Each case study will be completed by a thorough discussion and interpretation of the results.
Practical Case Studies in Data Analytics (DA 515)
2021 Summer
Faculty of Engineering and Natural Sciences
Data Analytics(DA)
3
6
Birol Yüceoğlu byuceoglu@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
2. Reflect on social and ethical responsibilities in his/her professional life.
3. Gain experience and confidence in the dissemination of project/research outputs
4. Work responsibly and creatively as an individual or as a member or leader of a team and in multidisciplinary environments.
5. Communicate effectively by oral, written, graphical and technological means and have competency in English.
6. Independently reach and acquire information, and develop appreciation of the need for continuously learning and updating.
1. Design and model engineering systems and processes and solve engineering problems with an innovative approach.
2. Establish experimental setups, conduct experiments and/or simulations.
3. Analytically acquire and interpret data.
1. Comprehend the conceptual foundations of analytical methods and techniques within the scope of business analytics,
2. Acquire theoretical and practical knowledge on applied information systems by developing fundamental programming skills,
3. Improve decision making by turning high-volume data into useful information and integrating data analysis tools
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
5. Understand the data quality, data integrity and data accuracy concepts, and occupational ethics regarding data privacy and intellectual property