İzmir Ekonomi Üniversitesi
  • TÜRKÇE

  • FACULTY OF ENGINEERING

    Department of Mechanical Engineering

    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
    Course Lecturer(s)
    Assistant(s)
    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.

     



    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 adequate knowledge in Mathematics, Mathematics based physics, statistics and linear algebra and Mechanical Engineering; to be able to use theoretical and applied information in these areas on complex engineering problems.

    -
    -
    -
    -
    -
    2

    To be able to identify, define, formulate, and solve complex Mechanical Engineering problems; to be able to select and apply proper analysis and modeling methods for this purpose.

    -
    -
    -
    -
    -
    3

    To be able to design a thermal and mechanical system, process, device or product under realistic constraints and conditions, in such a way as to meet the requirements; to be able to apply modern design methods for this purpose.

    -
    -
    -
    -
    -
    4

    To be able to devise, select, and use modern techniques and tools needed for analysis and solution of complex problems in engineering applications.

    -
    -
    -
    -
    -
    5

    To be able to design and conduct experiments, gather data, analyze and interpret results for investigating complex engineering problems or Mechanical Engineering research topics.

    -
    -
    -
    -
    -
    6

    To be able to work efficiently in Mechanical Engineering disciplinary and multi-disciplinary teams; to be able to work individually.

    -
    -
    -
    -
    -
    7

    To be able to communicate effectively in Turkish, both orally and in writing; 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; to have knowledge about standards utilized in engineering applications.

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    -
    -
    -
    -
    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

    To be able to collect data in the area of Mechanical Engineering, and to be able to communicate with colleagues in a foreign language.

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    -
    -
    -
    -
    12

    To be able to speak a second foreign language at a medium level of fluency efficiently.

    -
    -
    -
    -
    -
    13

    To recognize the need for lifelong learning; to be able to access information, to be able to stay current with developments in science and technology; to be able to relate the knowledge accumulated throughout the human history to Mechanical Engineering.

    -
    -
    -
    -
    -

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

     


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