Data Structures and Algorithms
Preparatory Course
Credits: –
Learning Outcomes
The course is preliminary and introduces students to a variety of fundamental data structures and their related algorithms. It provides an overview of new types of data structure concepts such as arrays, stack, queues, trees, and graphs. Some basic graph and discrete transform algorithms for creating, manipulating and using these structures will also be discussed. Students will implement operations that can be applied to data structures using various searching and sorting techniques. Algorithm analysis and efficient code design are discussed. This course provides different data structures and how are effectively used for solving problems.
With the successful completion of the course, students will be sufficiently capable to:
- Describe all types of data structures and know how to apply them effectively in problem solving
- design or select an appropriate data structures for a particular problem
- understand the techniques of algorithm analysis
- develop basic algorithms related to data structures, such as searching and sorting algorithms
- analyze and select the most suitable and effective algorithm for solving certain problem
- develop a structured and logical process of thinking
General Competences
- Adapting to new situations
- Decision-making
- Working independently
- Production of free, creative and inductive thinking.
Course Content
- Introduction to corporate finance and corporate governance
- The time value of money – discounting and present value
- Investment evaluation and investment decisions rules under conditions of certainty
- Valuation of bonds and equity shares
- Weighted Average Cost of Capital
- Expected return and risk of investments
- Empirical facts on returns of major asset classes
- Portfolio theory and diversification
- Capital Asset Pricing Model and compensation for systematic risk
- Derivatives and applications to risk management
Student Performance Evaluation
The course is preliminary, and there is no final evaluation
Bibliography
Suggested Bibliography
- Narasimha Karumanchi, ”Data Structures And Algorithms Made Easy”, 5th edition, CareerMonk Publications
- Shaffer, C.A. ”Data Structures and Algorithm Analysis”, Dover Publications.
- Sahni, Data Structures, Algorithms and Applications, McGrawHill, latest edition
- Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein, ”Introduction to Algorithms”, MIT Press, latest edition
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