{"id":18458,"date":"2025-07-19T16:23:05","date_gmt":"2025-07-19T14:23:05","guid":{"rendered":"https:\/\/www2.mie.uth.gr\/?page_id=18458"},"modified":"2025-10-25T20:47:45","modified_gmt":"2025-10-25T17:47:45","slug":"%ce%bf%cf%800801-data-science-in-supply-chain-management-elective-production-management-track","status":"publish","type":"page","link":"https:\/\/mie.uth.gr\/?page_id=18458&lang=en","title":{"rendered":"\u039f\u03a00801\u00a0Data Science in Supply Chain Management (Elective Course 3)"},"content":{"rendered":"\t\t
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\u039f\u03a00801 DATA SCIENCE IN SUPPLY CHAIN MANAGEMENT (ELECTIVE COURSE 3)<\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Course Information<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t
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Course Outline<\/a><\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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\u03a0\u03b5\u03c1\u03af\u03b3\u03c1\u03b1\u03bc\u03bc\u03b1 \u039c\u03b1\u03b8\u03ae\u03bc\u03b1\u03c4\u03bf\u03c2<\/a><\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Course Category<\/strong>
Course Type<\/strong>
Secretary Code<\/strong>
Semester<\/strong>
Duration<\/strong>
ECTS Units<\/strong><\/p>

Instructor<\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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Undergraduate
Elective Course 3
\u039f\u03a00801
8th (Spring)
5 hours\/week
6<\/p>

Taouktsis X<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Course Category: <\/strong>Undergraduate
Course Type: <\/strong>Elective Course 3
Secretary Code: <\/strong>\u039f\u03a00801
Semester: <\/strong>8th (Spring)
Duration: <\/strong>5 hours\/week
ECTS Units:\u00a0<\/strong>6
Instructor: <\/strong>X Taouktsis<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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<\/i>Aim<\/span><\/div>

The aim of the course is to prepare and equip future scientists and executives in the field of Supply Chain Management with the necessary skills of the interdisciplinary field of Data Science. The course will\u00a0 introduce the broad scientific field of Machine Learning which is a key area of Artificial ntelligence. Emphasis will be placed on developing intelligent applications utilizing Big Data Analytics techniques for better decision-making on practical supply chain issues. Indicatively, Machine Learning algorithms, methods and models (supervised and unsupervised learning) for big data analysis will be presented, such as: regression methods, classification methods, artificial neural networks, support vector machines, deep learning models, clustering algorithms, dimensionality reduction techniques. The application of these will focus on solving practical supply chain issues, including time series analysis for demand forecasting, inventory management, various optimization problems.\u00a0 The Anaconda distribution will be used with emphasis on the use of the Python programming language, which has a lot of features and is particularly efficient for applications in the field of Data Science. The source code will be written in interactive development environments (IDEs), mainly JupyterLab and Spyder. For the implementation of practical applications, we will make use of key indicative open-source Python packages (libraries) such as Google OR-Tools, H2O, Matplotlib, NumPy, pandas, scikit-learn, SciPy, Seaborn, statsmodels.Key Cloud Services for code repository management platforms and web-based interactive development environments, such as GitHub and Google Colaboratory (Colab) respectively, will be presented. The programming language R will be presented and compared to Python, with emphasis on their applications in the field of Data Science, for modelling and decision-making in supply chain issues.\u00a0<\/p><\/div>\n\t\t\t\t\t<\/div>

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<\/i>Syllabus<\/span><\/div>
  • Presentation of the basic principles of Data Science with a focus on Supply Chain Management,\u00a0<\/li>
  • Presentation of Anaconda distribution with creation and management of virtual environments,\u00a0<\/li>
  • Introduction to Python Programming Language, <\/li>
  • Data Types and Structures, <\/li>
  • Control Structures, <\/li>
  • \u00a0File Handling (Input and Output), <\/li>
  • Define and Call Functions, <\/li>
  • Packages (libraries) for Data Manipulation, Analysis, and Visualization, <\/li>
  • Packages (libraries) for Machine Learning,<\/li>
  • Presentation of Supervised and Unsupervised learning, <\/li>
  • Presentation of Machine Learning algorithms, methods and models, such as:<\/li><\/ul>

    \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\u2013 Regression Methods,<\/p>

    \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u2013 Classification Methods,<\/p>

    \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u2013 Artificial Neural Networks,<\/p>

    \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\u2013 Support Vector Machine<\/p>

    \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u2013 Deep Learning Models,<\/p>

    \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u2013 Clustering Algorithms,<\/p>

    \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\u2013 Dimensionality Reduction Techniques. <\/p>

    • Practical applications in time series analysis for demand forecasting, <\/li>
    • Practical applications in inventory management, <\/li>
    • Practical applications in various optimization problems, <\/li>
    • \u00a0Presentation of cloud services (GitHub and Google Colab), <\/li>
    • Presentation and comparison of R programming language versus Python on Data Science in supply chain issues.<\/li><\/ul><\/div>\n\t\t\t\t\t<\/div>
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      <\/i>Literature<\/span><\/div>

      Suggested Literature:<\/strong><\/span><\/p>

      • Deitel, H. M., & Deitel, P. J. (2021). \u0395\u03b9\u03c3\u03b1\u03b3\u03c9\u03b3\u03ae \u03c3\u03c4\u03b7\u03bd Python \u03b3\u03b9\u03b1 \u03c4\u03b9\u03c2 \u0395\u03c0\u03b9\u03c3\u03c4\u03ae\u03bc\u03b5\u03c2 \u03a5\u03c0\u03bf\u03bb\u03bf\u03b3\u03b9\u03c3\u03c4\u03ce\u03bd \u03ba\u03b1\u03b9 \u0394\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03c9\u03bd. \u0395\u03ba\u03b4\u03cc\u03c4\u03b7\u03c2: \u03a7. \u0393\u039a\u0399\u039f\u03a5\u03a1\u0394\u0391 \u03a3\u0399\u0391 \u0395\u0395. ISBN: 9789605127442.<\/li>
      • \u00a0FAWCETT, T., & PROVOST, F. (2019). \u0397 \u0395\u03a0\u0399\u03a3\u03a4\u0397\u039c\u0397 \u03a4\u03a9\u039d \u0394\u0395\u0394\u039f\u039c\u0395\u039d\u03a9\u039d \u0393\u0399\u0391 \u0395\u03a0\u0399\u03a7\u0395\u0399\u03a1\u0397\u03a3\u0395\u0399\u03a3. \u0395\u03ba\u03b4\u03cc\u03c4\u03b7\u03c2: \u0395\u039a\u0394\u039f\u03a3\u0395\u0399\u03a3 \u039a\u039b\u0395\u0399\u0394\u0391\u03a1\u0399\u0398\u039c\u039f\u03a3 \u0395\u03a0\u0395. ISBN: 9789604619917.<\/li>
      • Hillier, F. S., Lieberman, G. J., & \u0394\u03b9\u03b1\u03bc\u03b1\u03bd\u03c4\u03af\u03b4\u03b7\u03c2, \u0391. (\u0395\u03c0\u03b9\u03c3\u03c4. \u0395\u03c0\u03b9\u03bc\u03ad\u03bb\u03b5\u03b9\u03b1) (2021). \u0395\u03b9\u03c3\u03b1\u03b3\u03c9\u03b3\u03ae \u03a3\u03c4\u03b7\u03bd \u0395\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03c3\u03b9\u03b1\u03ba\u03ae \u0388\u03c1\u03b5\u03c5\u03bd\u03b1 (11\u03b7 \u0388\u03ba\u03b4\u03bf\u03c3\u03b7). \u0395\u03ba\u03b4\u03cc\u03c4\u03b7\u03c2: \u0395\u039a\u0394\u039f\u03a3\u0395\u0399\u03a3 \u0391. \u03a4\u0396\u0399\u039f\u039b\u0391 & \u03a5\u0399\u039f\u0399 \u0391.\u0395. ISBN: 9789604189168. <\/li>
      • MATTHES, E. (2020). \u0397 \u0393\u039b\u03a9\u03a3\u03a3\u0391 \u03a0\u03a1\u039f\u0393\u03a1\u0391\u039c\u039c\u0391\u03a4\u0399\u03a3\u039c\u039f\u03a5 PYTHON. \u0395\u03ba\u03b4\u03cc\u03c4\u03b7\u03c2: \u0395\u039a\u0394\u039f\u03a3\u0395\u0399\u03a3 \u0394\u0399\u03a3\u0399\u0393\u039c\u0391 \u0399\u039a\u0395.
        ISBN: 9786182020036.<\/li>
      • \u00a0Muddana, A. L., & Vinayakam, S. (2024). Python for Data Science. Springer Nature Switzerland. https:\/\/doi.org\/10.1007\/978-3-031-52473-8 <\/li>
      • Sunil, C., \u0391\u03bd\u03b4\u03c1\u03bf\u03c5\u03c4\u03c3\u03cc\u03c0\u03bf\u03c5\u03bb\u03bf\u03c2, \u039a., & \u039c\u03b1\u03bd\u03c4\u03ac\u03c2, \u039c. (\u0395\u03c0\u03b9\u03c3\u03c4. \u03b5\u03c0\u03b9\u03bc\u03ad\u03bb\u03b5\u03b9\u03b1). (2020). \u0394\u03b9\u03bf\u03af\u03ba\u03b7\u03c3\u03b7 \u0395\u03c6\u03bf\u03b4\u03b9\u03b1\u03c3\u03c4\u03b9\u03ba\u03ae\u03c2 \u0391\u03bb\u03c5\u03c3\u03af\u03b4\u03b1\u03c2, 7\u03b7 \u0388\u03ba\u03b4\u03bf\u03c3\u03b7. \u0395\u03ba\u03b4\u03cc\u03c4\u03b7\u03c2: \u0395\u039a\u0394\u039f\u03a3\u0395\u0399\u03a3 \u0391. \u03a4\u0396\u0399\u039f\u039b\u0391 & \u03a5\u0399\u039f\u0399 \u0391.\u0395. ISBN: 9789604188758. <\/li>
      • \u0392\u03bb\u03b1\u03c7\u03ac\u03b2\u03b1\u03c2, \u0399., \u039a\u03b5\u03c6\u03b1\u03bb\u03ac\u03c2, \u03a0., \u0392\u03b1\u03c3\u03b9\u03bb\u03b5\u03b9\u03ac\u03b4\u03b7\u03c2, \u039d., \u039a\u03cc\u03ba\u03ba\u03bf\u03c1\u03b1\u03c2, \u03a6., & \u03a3\u03b1\u03ba\u03b5\u03bb\u03bb\u03b1\u03c1\u03af\u03bf\u03c5, \u0397. (2020). \u03a4\u0395\u03a7\u039d\u0397\u03a4\u0397 \u039d\u039f\u0397\u039c\u039f\u03a3\u03a5\u039d\u0397 – 4\u03b7 \u0395\u039a\u0394\u039f\u03a3\u0397. \u0395\u03ba\u03b4\u03cc\u03c4\u03b7\u03c2: \u0395\u03a4\u0391\u0399\u03a1\u0395\u0399\u0391 \u0391\u039e\u0399\u039f\u03a0\u039f\u0399\u0397\u03a3\u0397\u03a3 \u039a\u0391\u0399 \u0394\u0399\u0391\u03a7\u0395\u0399\u03a1\u0399\u03a3\u0397\u03a3 \u03a0\u0395\u03a1\u0399\u039f\u03a5\u03a3\u0399\u0391\u03a3 \u03a0\u0391\u039d\u0395\u03a0\u0399\u03a3\u03a4\u0397\u039c\u0399\u039f\u03a5 \u039c\u0391\u039a\u0395\u0394\u039f\u039d\u0399\u0391\u03a3. ISBN: 9786185196448. <\/li>
      • \u0394\u0399\u0391\u039c\u0391\u039d\u03a4\u0391\u03a1\u0391\u03a3, \u039a., & \u039c\u03a0\u039f\u03a4\u03a3\u0397\u03a3, \u0394. (2019). \u039c\u0397\u03a7\u0391\u039d\u0399\u039a\u0397 \u039c\u0391\u0398\u0397\u03a3\u0397. \u0395\u03ba\u03b4\u03cc\u03c4\u03b7\u03c2: \u0395\u039a\u0394\u039f\u03a3\u0395\u0399\u03a3 \u039a\u039b\u0395\u0399\u0394\u0391\u03a1\u0399\u0398\u039c\u039f\u03a3 \u0395\u03a0\u0395. ISBN: 9789604619955.<\/li><\/ul>

        Related academic journals<\/strong><\/span><\/p>

        • Big Data Mining and Analytics, IEEE <\/li>
        • Big Data Research, Elsevier <\/li>
        • \u00a0Computers & Industrial Engineering, Elsevier <\/li>
        • Data Mining and Knowledge Discovery, Springer <\/li>
        • Data Science and Engineering, Springer <\/li>
        • \u00a0European Journal of Operational Research, Elsevier <\/li>
        • \u00a0Expert Systems with Applications, Elsevier <\/li>
        • IEEE Transactions on Knowledge and Data Engineering, IEEE <\/li>
        • \u00a0International Journal of Data Science and Analytics, Springer <\/li>
        • \u00a0International Journal of Production Economics, Elsevier <\/li>
        • International Journal of Production Research, Taylor & Francis <\/li>
        • Management Science, INFORMS <\/li>
        • Operational Research, Springer<\/li><\/ul><\/div>\n\t\t\t\t\t<\/div>
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          <\/i>Teaching Language<\/span><\/div>

          Greek<\/p><\/div>\n\t\t\t\t\t<\/div>

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          <\/i>Teaching Method<\/span><\/div>

          Lectures, Laboratory Exercises<\/p><\/div>\n\t\t\t\t\t<\/div>

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          <\/i>Studen Performance Evaluation<\/span><\/div>
          Computer Lab-Based Final Exam<\/strong><\/td>70%<\/td><\/tr>
          Assignments<\/td>30%<\/td><\/tr><\/tbody><\/table><\/div>\n\t\t\t\t\t<\/div>
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          <\/i>Workload (in hours)<\/span><\/div>
          Activity<\/strong><\/td>Semester Workload<\/strong><\/td><\/tr>
          Lectures<\/td>70<\/td><\/tr>
          Laboratory Exercises<\/td>30<\/td><\/tr>
          Independent Study<\/td>50<\/td><\/tr>
          Course Total<\/strong><\/td>150<\/strong><\/td><\/tr><\/tbody><\/table><\/div>\n\t\t\t\t\t<\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

          \u039f\u03a00801 DATA SCIENCE IN SUPPLY CHAIN MANAGEMENT (ELECTIVE COURSE 3) \u039f\u03a00801 DATA SCIENCE IN SUPPLY CHAIN MANAGEMENT (ELECTIVE COURSE 3) Course Information Course Outline \u03a0\u03bb\u03b7\u03c1\u03bf\u03c6\u03bf\u03c1\u03af\u03b5\u03c2 \u039c\u03b1\u03b8\u03ae\u03bc\u03b1\u03c4\u03bf\u03c2 \u03a0\u03b5\u03c1\u03af\u03b3\u03c1\u03b1\u03bc\u03bc\u03b1 \u039c\u03b1\u03b8\u03ae\u03bc\u03b1\u03c4\u03bf\u03c2 Course Category<\/p>\n","protected":false},"author":8,"featured_media":9407,"parent":13157,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":""},"class_list":["post-18458","page","type-page","status-publish","has-post-thumbnail","hentry"],"acf":[],"yoast_head":"\n\u039f\u03a00801\u00a0Data Science in Supply Chain Management (Elective Course 3) - \u03a4\u03bc\u03ae\u03bc\u03b1 \u039c\u03b7\u03c7\u03b1\u03bd\u03bf\u03bb\u03cc\u03b3\u03c9\u03bd \u039c\u03b7\u03c7\u03b1\u03bd\u03b9\u03ba\u03ce\u03bd<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/mie.uth.gr\/?page_id=18458&lang=en\" \/>\n<meta property=\"og:locale\" content=\"el_GR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u039f\u03a00801\u00a0Data Science in Supply Chain Management (Elective Course 3) - 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