İzmir Ekonomi Üniversitesi
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  • GRADUATE SCHOOL

    Applied Mathematics and Statistics – With Thesis

    MATH 512 | Course Introduction and Application Information

    Course Name
    Research Design and Methods in Applied Mathematics and Statistics
    Code
    Semester
    Theory
    (hour/week)
    Application/Lab
    (hour/week)
    Local Credits
    ECTS
    MATH 512
    Spring
    3
    0
    3
    7.5

    Prerequisites
    None
    Course Language
    English
    Course Type
    Required
    Course Level
    Second Cycle
    Mode of Delivery -
    Teaching Methods and Techniques of the Course Discussion
    Lecture / Presentation
    National Occupation Classification -
    Course Coordinator
    Course Lecturer(s)
    Assistant(s)
    Course Objectives This course introduces core principles of research design, scientific inquiry, and ethical standards in applied mathematics and statistics. It equips students with practical skills in computational tools, LaTeX, responsible AI use, and preparing manuscripts for peer-reviewed journals.
    Learning Outcomes

    The students who succeeded in this course;

    • Differentiate the conceptual framework of research and the types of research (basic, applied, action).
    • Apply the scientific method to design, conduct, and interpret mathematical and statistical research.
    • Design computational workflows, including AI-assisted methods, for data processing, visualization, and modeling.
    • Evaluate information from literature databases such as MathSciNet, JSTOR, Web of Science, and Scopus.
    • Produce professional manuscripts in LaTeX with correctly formatted equations, tables, and figures.
    • Apply research ethics principles, including plagiarism avoidance, prevention of scientific misconduct, intellectual property protection, and responsible AI usage.
    • Assess research outputs using journal quality indicators and research metrics (Impact Factor, SNIP, SJR, IPP, CiteScore).
    • Communicate research findings clearly and effectively in oral presentations and written reports.
    Course Description This course explores research design and scientific methodology in applied mathematics and statistics. Topics include conceptual frameworks of research, types of inquiry, scientific writing, and ethics in research. Students will learn literature review strategies, principles of report preparation, and manuscript writing in mathematics. The course also emphasizes computational tools and AI-supported workflows for research productivity. Practical sessions include LaTeX, programming environments, and database usage. This course explores research design and scientific methodology in applied mathematics and statistics. Topics include conceptual frameworks of research, types of inquiry, scientific writing, and ethics in research. Students will learn literature review strategies, principles of report preparation, and manuscript writing in mathematics. The course also emphasizes computational tools and AI-supported workflows for research productivity. Practical sessions include LaTeX, programming environments, and database usage. This course explores research design and scientific methodology in applied mathematics and statistics. Topics include conceptual frameworks of research, types of inquiry, scientific writing, and ethics in research. Students will learn literature review strategies, principles of report preparation, and manuscript writing in mathematics. The course also emphasizes computational tools and AI-supported workflows for research productivity. Practical sessions include LaTeX, programming environments, and database usage.
    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
    1 Introduction to research: meaning, concept, and conceptual framework Marder, M. P. (2011). Research Methods for Science (2nd ed.). Cambridge University Press. Ch. 1
    2 Ethics in research Adams, Kathrynn A.; McGuire, Eva K. (2022). Research Methods, Statistics, and Applications (3rd ed.), Ch.1
    3 AI and ethics in scientific research Çelik, Özgür. Demystifying Ethical Use of AI in Academic Writing: A Scenario-Based Approach, Ch. 1
    4 Understanding AI and Ethics in Academic Writing Çelik, Özgür. Demystifying Ethical Use of AI in Academic Writing: A Scenario-Based Approach, Ch. 1
    5 The Methodology of Mathematics Brown, Ronald; Porter, Timothy. The Methodology of Mathematics (1995)
    6 Research Desing in Statistics Adams, Kathrynn A.; McGuire, Eva K. (2022). Research Methods, Statistics, and Applications (3rd ed.) Ch.4
    7 Literature review: MathSciNet, JSTOR, Web of Science, Scopus Çelik, Özgür. Demystifying Ethical Use of AI in Academic Writing: A Scenario-Based Approach, Ch. 1
    8 Midterm
    9 General guidelines on scientific report writing Michael J. Katz. From Research to Manuscript: A Guide to Scientific Writing. Springer, 2009. Part II Chapters 1-2
    10 General guidelines on scientific report writing (research paper) Michael J. Katz. From Research to Manuscript: A Guide to Scientific Writing. Springer (2009). Part III Chapters 1-4
    11 General guidelines on scientific report writing (research paper) Michael J. Katz. From Research to Manuscript: A Guide to Scientific Writing. Springer (2009). Part III Chapters 1-4
    12 Programming and computational tools for research in mathematics and statistics: MATLAB, Python, R, Mathematica (advantages, disadvantages, workflows) Michael J. Katz. From Research to Manuscript: A Guide to Scientific Writing. Springer, 2009, Part III and MATLAB/Python Documentation
    13 Integrating computational tools, AI, and research workflow Michael J. Katz. From Research to Manuscript: A Guide to Scientific Writing. Springer, 2009, Part III and MATLAB/Python Documentation
    14 LaTeX software: writing equations, tables, figures Stefan Kottwitz. LaTeX Beginner’s Guide. 2nd edition. Packt Publishing (2021).
    15 Student project presentations
    16 Student project presentations

     

    Course Notes/Textbooks

    Marder, M. P. (2011). Research Methods for Science (2nd ed.). Cambridge University Press. ISBN 978-0-521-18614-1.
     

    Adams, Kathrynn A.; McGuire, Eva K. (2022). Research Methods, Statistics, and Applications (3rd ed.). SAGE Publications. ISBN-13 978-1071817834.
     

    Brown, Ronald; Porter, Timothy. The Methodology of Mathematics. The Mathematical Gazette, Volume 79, Issue 485, July 1995, pp. 232–246.
     

    Çelik, Özgür. Demystifying Ethical Use of AI in Academic Writing: A Scenario-Based Approach. Ihlamur Akademi, 2025. ISBN 978-6256633568.
     

    Stefan Kottwitz. LaTeX Beginner’s Guide. 2nd edition. Packt Publishing, 2021. ISBN 978-1801078658.
     

    Michael J. Katz. From Research to Manuscript: A Guide to Scientific Writing. Springer, 2009. ISBN 978-1402094668.

    Suggested Readings/Materials

    MathSciNet, JSTOR, Web of Science, Scopus (databases)
     

    Selected research articles provided throughout the semester
     

    N. J. Higham. Handbook of Writing for the Mathematical Sciences. SIAM, 1998. ISBN 978-0898714203.

     

    EVALUATION SYSTEM

    Semester Activities Number Weigthing
    Participation
    Laboratory / Application
    Field Work
    Quizzes / Studio Critiques
    Portfolio
    Homework / Assignments
    1
    30
    Presentation / Jury
    Project
    1
    40
    Seminar / Workshop
    Oral Exams
    Midterm
    1
    30
    Final Exam
    1
    Total

    Weighting of Semester Activities on the Final Grade
    3
    100
    Weighting of End-of-Semester Activities on the Final Grade
    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
    0
    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
    1
    50
    50
    Seminar / Workshop
    0
    Oral Exam
    0
    Midterms
    1
    45
    45
    Final Exam
    1
    0
        Total
    225

     

    COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

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

    To be able to demonstrate independent and critical thinking in Applied Mathematics and Statistics.

     
    -
    -
    -
    -
    -
    2

    To be able to define problems in Applied Mathematics/Statistics and verify whether they are mathematically/statistically consistent.

    -
    -
    X
    -
    -
    3

    To be able to analyse and solve real life problems using applied methods and interdisciplinary approach of Mathematics/Statistics.

    -
    -
    -
    -
    -
    4

    To be able to independently conduct, conclude, and report on specialized research in Applied Mathematics and Statistics.

     
    -
    X
    -
    -
    -
    5

    To be able to efficiently use national and international resources, for staying updated in the field, communicating with colleagues, and following the related literature.

    -
    -
    X
    -
    -
    6

    To be able to develop proficiency in using computer software widely utilized in the fields of Applied Mathematics and Statistics.

    -
    -
    -
    X
    -
    7

    To be able to evaluate solution processes efficiently using mathematical reasoning and modeling in order to contribute to the solutions of social and scientific problems.

    -
    -
    -
    -
    X
    8

    To be able to synthesize theoretical frameworks with practical applications through mathematical and statistical methods.

     
    -
    -
    -
    -
    -
    9

    To be able to develop strategies, policies and plans for problems and research areas in Applied Mathematics/Statistics in order to interpret the results and translate them into practice.

     
    -
    X
    -
    -
    -
    10

    To be able to translate key topics, events, and phenomena in Applied Mathematics and Statistics into the context of other scientific disciplines.

     
    -
    -
    -
    -
    -
    11

    To be able to engage in lifelong learning by continuously updating and improving knowledge and skills in Applied Mathematics and Statistics.

    -
    -
    -
    -
    -

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


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