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      FACULTY OF ENGINEERING

      Department of Mechatronics Engineering

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      CE 221 | Course Introduction and Application Information

      Course Name
      Data Structures and Algorithms I
      Code
      Semester
      Theory
      (hour/week)
      Application/Lab
      (hour/week)
      Local Credits
      ECTS
      CE 221
      Fall/Spring
      3
      2
      4
      7

      Prerequisites
        SE 116 To get a grade of at least FD
      Course Language
      English
      Course Type
      Elective
      Course Level
      First Cycle
      Mode of Delivery -
      Teaching Methods and Techniques of the Course Problem Solving
      Application: Experiment / Laboratory / Workshop
      Lecture / Presentation
      National Occupation Classification -
      Course Coordinator
      • Prof. Dr. Cem EVRENDİLEK
      Course Lecturer(s)
      • Dr. Öğr. Üyesi Çağlar DURMAZ
      • Prof. Dr. Cem EVRENDİLEK
      • Prof. Dr. Hüseyin AKCAN
      Assistant(s)
      • Araş. Gör. Melek Büşra Temuçin
      Course Objectives The objective of this course is to teach students the notion of an abstract data type (ADT) which is central to the design and analysis of computer algorithms. This course introduces abstract data types, and presents algorithms and data structures for implementing several ADTs. It emphasizes the efficiency of algorithms as evaluated by asymptotic analysis of running time. The programming assignments will be given in the programming languages taught in SE 115 and/or SE116.
      Learning Outcomes
      #
      Content
      PC Sub
      * Contribution Level
      1
      2
      3
      4
      5
      1will be able to analyze the loop structures of either recursive or non-recursive algorithms to express their asymptotic running times using big-Oh notation.
      2will be able to assess the relative advantages of using array or linked list implementations versus hashing in efficiently solving search problems with concurrent insertion, and/or deletions on collections of data.
      3will be able to develop efficient computer programs running in O (log n) per searching, insertion and/or deletion of data items by employing correct variants of tree data structures covered in the course.
      4will be able to select the right sorting algorithm for efficient applications requiring an order on data items.
      5will be able to describe the usage of various data structures.
      6will be able to explain the operations for maintaining common data structures.
      7will be able to devise appropriate data structures for solving specific computing problems.
      8will be able to use various graph algorithms to design solutions to simple computing problems.
      Course Description Algorithm analysis, linear data structures, trees, hashing, priority queues, sorting, and graph algorithms.
      Related Sustainable Development Goals

       



      Course Category

      Core Courses
      Major Area Courses
      Supportive Courses
      Media and Management Skills Courses
      Transferable Skill Courses

       

      WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

      Week Subjects Related Preparation Learning Outcome
      1 Introduction: Mathematics Review and Recursion M. A. Weiss, Data Structures and Algorithm Analysis in Java, 3/e, Pearson, 2012 (Ch. 1.1, 1.2, 1.3)
      2 Algorithm Analysis (basic concepts of algorithms, modeling runtimes, recurrences, Big-Oh notations, Running Time Calculations) M. A. Weiss, Data Structures and Algorithm Analysis in Java, 3/e, Pearson, 2012 (Ch. 2.1, 2.2, 2.3)
      3 Algorithm Analysis and Linear Data Structures: (Linked Lists) M. A. Weiss, Data Structures and Algorithm Analysis in Java, 3/e, Pearson, 2012 (Ch. 2.4, 3.1 - 3.5)
      4 Linear Data Structures (Linked Lists, Stacks, Stack Applications) M. A. Weiss, Data Structures and Algorithm Analysis in Java, 3/e, Pearson, 2012 (Ch. 3.5, 3.6)
      5 Linear Data Structures (Queues) and Trees (Binary trees) M. A. Weiss, Data Structures and Algorithm Analysis in Java, 3/e, Pearson, 2012 (Ch. 3.7, 4.1, 4.2)
      6 Trees (Binary search trees) M. A. Weiss, Data Structures and Algorithm Analysis in Java, 3/e, Pearson, 2012 (Ch. 4.3)
      7 Trees (AVL Trees) M. A. Weiss, Data Structures and Algorithm Analysis in Java, 3/e, Pearson, 2012 (Ch. 4.4)
      8 Midterm
      9 Hashing M. A. Weiss, Data Structures and Algorithm Analysis in Java, 3/e, Pearson, 2012 (Ch. 5.1 – 5.5)
      10 Priority Queues: Binary Heaps M. A. Weiss, Data Structures and Algorithm Analysis in Java, 3/e, Pearson, 2012 (Ch. 6.1, 6.2, 6.3)
      11 Sorting (Insertion Sort, Shellsort, Heapsort) M. A. Weiss, Data Structures and Algorithm Analysis in Java, 3/e, Pearson, 2012 (Ch. 7.1, 7.2, 7.3, 7.4, 7.5)
      12 Sorting (Mergesort, Quicksort) M. A. Weiss, Data Structures and Algorithm Analysis in Java, 3/e, Pearson, 2012 (Ch. 7.6, 7.7)
      13 Graph Algorithms (Definitions, Representation, Topological Sort) M. A. Weiss, Data Structures and Algorithm Analysis in Java, 3/e, Pearson, 2012 (Ch. 9.1 - 9.2)
      14 Graph Algorithms (Shortest Path Algorithms) M. A. Weiss, Data Structures and Algorithm Analysis in Java, 3/e, Pearson, 2012 (Ch. 9.3)
      15 Semester Review
      16 Final Exam

       

      Course Notes/Textbooks

      M. A. Weiss, Data Structures and Algorithm Analysis in Java, 3/e, Pearson, 2012, 978-0132576277

      Suggested Readings/Materials

      R. Sedgewick, K. Wayne, Algorithms, 4/e, Addison-Wesley Professional, 2011, 978-0321573513

       

      EVALUATION SYSTEM

      Semester Activities Number Weigthing LO 1 LO 2 LO 3 LO 4 LO 5 LO 6 LO 7 LO 8
      Participation
      Laboratory / Application
      8
      40
      Field Work
      Quizzes / Studio Critiques
      Portfolio
      Homework / Assignments
      Presentation / Jury
      Project
      Seminar / Workshop
      Oral Exams
      Midterm
      1
      20
      Final Exam
      1
      40
      Total

      Weighting of Semester Activities on the Final Grade
      2
      60
      Weighting of End-of-Semester Activities on the Final Grade
      1
      40
      Total

      ECTS / WORKLOAD TABLE

      Semester Activities Number Duration (Hours) Workload
      Theoretical Course Hours
      (Including exam week: 16 x total hours)
      16
      3
      48
      Laboratory / Application Hours
      (Including exam week: '.16.' x total hours)
      16
      2
      32
      Study Hours Out of Class
      14
      3
      42
      Field Work
      0
      Quizzes / Studio Critiques
      0
      Portfolio
      0
      Homework / Assignments
      1
      40
      40
      Presentation / Jury
      0
      Project
      0
      Seminar / Workshop
      0
      Oral Exam
      0
      Midterms
      1
      24
      24
      Final Exam
      1
      24
      24
          Total
      210

       

      COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

      #
      PC Sub Program Competencies/Outcomes
      * Contribution Level
      1
      2
      3
      4
      5
      1

      To have knowledge in Mathematics, science, physics knowledge based on mathematics; mathematics with multiple variables, differential equations, statistics, optimization and linear algebra; to be able to use theoretical and applied knowledge in complex engineering problems

      -
      -
      -
      -
      -
      2

      To be able to identify, define, formulate, and solve complex mechatronics engineering problems; to be able to select and apply appropriate analysis and modeling methods for this purpose.

      -
      -
      -
      -
      -
      3

      To be able to design a complex electromechanical system, process, device or product with sensor, actuator, control, hardware, and software to meet specific requirements under realistic constraints and conditions; to be able to apply modern design methods for this purpose.

      -
      -
      -
      -
      -
      4

      To be able to develop, select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in Mechatronics Engineering applications; to be able to use information technologies effectively.

      -
      -
      -
      -
      -
      5

      To be able to design, conduct experiments, collect data, analyze and interpret results for investigating Mechatronics Engineering problems.

      -
      -
      -
      -
      -
      6

      To be able to work effectively in Mechatronics Engineering disciplinary and multidisciplinary teams; to be able to work individually.

      -
      -
      -
      -
      -
      7

      To be able to communicate effectively in Turkish, both in oral and written forms; to be able to author and comprehend written reports, to be able to prepare design and implementation reports, to present effectively, to be able to give and receive clear and comprehensible instructions.

      -
      -
      -
      -
      -
      8

      To have knowledge about global and social impact of engineering practices on health, environment, and safety; to have knowledge about contemporary issues as they pertain to engineering; to be aware of the legal ramifications of engineering solutions.

      -
      -
      -
      -
      -
      9

      To be aware of ethical behavior, professional and ethical responsibility; information on standards used in engineering applications.

      -
      -
      -
      -
      -
      10

      To have knowledge about industrial practices such as project management, risk management and change management; to have awareness of entrepreneurship and innovation; to have knowledge about sustainable development.

      -
      -
      -
      -
      -
      11

      Using a foreign language, he collects information about Mechatronics Engineering and communicates with his colleagues. ("European Language Portfolio Global Scale", Level B1)

      -
      -
      -
      -
      -
      12

      To be able to use the second foreign language at intermediate level.

      -
      -
      -
      -
      -
      13

      To recognize the need for lifelong learning; to be able to access information; to be able to follow developments in science and technology; to be able to relate the knowledge accumulated throughout the human history to Mechatronics Engineering.

      -
      -
      -
      -
      -

      *1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest


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