The aim of this course is to introduce the concept of analytical modelling, optimization problems and the fundamental properties of an optimization problem. Students will learn basics of transforming problems into analytical/quantitative/mathematical models, and how to formulate and solve simple mathematical models that represent optimization problems. Both exact algorithms and approximate algorithms, particularly heuristic techniques will be covered in order to form an understanding of algorithms and algorithm design to solve optimization problems. Throughout the course linear, nonlinear and integer optimization problems, network flow and network design problems will be the main focus with examples from the data science and data analytics domain.
Modeling and Optimization (DA 507)
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. 1
3. Gain experience and confidence in the dissemination of project/research outputs 1
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. 2
2. Establish experimental setups, conduct experiments and/or simulations. 2
3. Analytically acquire and interpret data. 4
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, 3
3. Improve decision making by turning high-volume data into useful information and integrating data analysis tools 3
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 3
5. Understand the data quality, data integrity and data accuracy concepts, and occupational ethics regarding data privacy and intellectual property 2