![Course details
• CSE205
• LTP – 3 0 2 [3 lectures, 2 practicals /week]
• Book to Follow
Data Structures (Schaums' Outlines Series)
Author - Seymour Lipschutz](https://image.slidesharecdn.com/0-221028150849-a15582e2/85/0-CSE205-3-ppt-2-320.jpg)
0. CSE205 (3).ppt
- 1. CSE205 Data Structures and Algorithms Lecture #0 The kick start session Presented by Dr. Prakash Singh Tanwar Associate Professor Department of Computer Science & Engineering SEEE
- 2. Course details • CSE205 • LTP – 3 0 2 [3 lectures, 2 practicals /week] • Book to Follow Data Structures (Schaums' Outlines Series) Author - Seymour Lipschutz
- 3. Course Orientation – CSE205 COMPETITIVE EXAMINATION (Higher Education) KNOWLEDGE ENHANCEMENT PLACEMENT EXAMINATION(Mass Recruiters)
- 4. Marks Break-Up Attendance 5 Marks Academic Tasks (CA) 55 Marks End Term Exam (MCQ + Subjective) 40 Marks
- 5. Continuous Assessment Model 1. Practice Problems 30 Marks 2. Coding Test 15 Marks 3. Online Quiz 10 Marks
- 6. Practice Problems • Students will be assigned 50 Practice Problems during the term (5 problems/ week) and 1 problem in a day • Students will be given 24 Hours to solve a problem. • Student will be assigned 1 marks for each problem solved after 15 problems (Max 30 Marks)
- 7. Coding Test • Online coding test will be conducted in the 10th Week on Saturday, 15th October 2022 on Hackerrank platform. • Students will have to report in the designated rooms to appear for the Test.
- 8. Online Quiz • Best 1 out of the 2 quizzes will be counted. • Quiz 1 will be conducted on 10th September 2022. • Quiz 2 will be conducted on 5th November 2022. • Students will have to report in the designated rooms to appear for the Quiz.
- 9. MOOCs Name • Introduction To Data Structures and Algorithms using Python Organization • NPTEL Benefits • All the CAs will be exempted based on the proctored exam result Note: 1. Students have choice to appear for Academic Task or MOOCs etc. 2. The student may appear for both, In this case best obtained marks will be considered.
- 10. The course contents CSE205 • Introduction to data structures • Computation of complexity • Arrays, Sorting & Searching • Linked List • Stack and Queues • Recursions • Trees • Hashing • Graphs
- 11. The hitch… The BURNING questions in mind… • Why are we learning data structures? • What will be the course outcome?
- 12. CO, PO, PSO and PEO • CO: Course Outcome • PO: Program Outcome • PSO: Program Specific Objective • PEO: Program Educational Objectives 12
- 13. PEO (Program Educational Objective) • PEOs are: – Statement of areas or fields where the graduates find employment – Preparedness of graduates to take up higher studies 13
- 14. Program Outcomes • PO1 Engineering Knowledge:: Apply the knowledge of mathematics, science, engineering fundamentals, and an engineering specialization to the solution of complex engineering problems. • PO2 Problem Analysis:: Identify, formulate, research literature, and analyze complex engineering problems reaching substantiated conclusions using first principles of mathematics, natural sciences, and engineering sciences. • PO3 Design/development of solutions:: Design solutions for complex engineering problems and design system components or processes that meet the specified needs with appropriate consideration for the public health and safety, and the cultural, societal, and environmental considerations. • PO4 Conduct investigations of complex problems:: Use research-based knowledge and research methods including design of experiments, analysis and interpretation of data, and synthesis of the information to provide valid conclusions.
- 15. • PO5 Modern tool usage:: Create, select, and apply appropriate techniques, resources, and modern engineering and IT tools including prediction and modeling to complex engineering activities with an understanding of the limitations. • PO6 The engineer and society:: Apply reasoning informed by the contextual knowledge to assess societal, health, safety, legal and cultural issues and the consequent responsibilities relevant to the professional engineering practice. • PO7 Environment and sustainability:: Understand the impact of the professional engineering solutions in societal and environmental contexts, and demonstrate the knowledge of, and need for sustainable development. • PO8 Ethics:: Apply ethical principles and commit to professional ethics and responsibilities and norms of the engineering practice. • PO9 Individual and team work:: Function effectively as an individual, and as a member or leader in diverse teams, and in multidisciplinary settings. Program Outcomes
- 16. • PO10 Communication:: Communicate effectively on complex engineering activities with the engineering community and with society at large, such as, being able to comprehend and write effective reports and design documentation, make effective presentations, and give and receive clear instructions. • PO11 Project management and finance:: Demonstrate knowledge and understanding of the engineering, management principles and apply the same to one’s own work, as a member or a leader in a team, manage projects efficiently in respective disciplines and multidisciplinary environments after consideration of economic and financial factors. • PO12 Life-long learning:: Recognize the need for, and have the preparation and ability to engage in independent and life-long learning in the broadest context of technological change. • PO13 Competitive Skills:: Ability to compete in national and international technical events and building the competitive spirit along with having a good digital footprint. Program Outcomes
- 17. PSO (Program Specific Outcome) 17 • These are what the students should be able to do at the time of graduation.
- 18. What will be the course outcome? • Describe the process to find efficiency of algorithms using asymptotic notations • Illustrate the importance of linked list in context of real world problems • Differentiate the stack and queue data structures for problem solving • Practice of recursion in iteration process and tree data structure • Analyze the effectiveness of AVL Tree and Heap Data Structures • Use of graph and hashing techniques in problem solving
- 19. Program Outcomes achieved from the course • Engineering knowledge: Apply the knowledge of mathematics, science, engineering fundamentals, and an engineering specialization to the solution of complex engineering problems. • Design/development of solutions: Design solutions for complex engineering problems and design system components or processes that meet the specified needs with appropriate consideration for the public health and safety, and the cultural, societal, and environmental considerations.
- 20. Program Outcomes achieved from the course • Conduct investigations of complex problems: Use research-based knowledge and research methods including design of experiments, analysis and interpretation of data, and synthesis of the information to provide valid conclusions. • Modern tool usage: Create, select, and apply appropriate techniques, resources, and modern engineering and IT tools including prediction and modeling to complex engineering activities with an understanding of the limitations. • Life-long learning: Recognize the need for, and have the preparation and ability to engage in independent and life-long learning in the broadest context of technological change.
- 21. Program Educational Objectives achieved from the course • Those employed in industry will be able to apply fundamentals of technical knowledge in multidisciplinary areas related to automobile, thermal, manufacturing and mechatronics by participating as top professionals in leading Industries. • Pursue advanced education, research and development in science, engineering, and technology, as well as other professional endeavors. • Be receptive to professional and ethical responsibilities for the impact of engineering solutions on society being as a successful innovator, consultant and entrepreneur
- 22. Q/A • Which of the following is not PO? – A. Engineering Knowledge – B. Communication – C. Life-long learning – D. Competitive Skills – E. Analyze the effectiveness of AVL Tree 22
- 24. Why are we learning data structures? Helps us to – get an idea of running time of algorithm – implement efficient algorithms – decide on hardware requirements – decide What is feasible vs. What is impossible – prepare job interviews
- 25. Next Class: Basic Data Structures