Data Science

General

• Code: 462
• Semester: 4th
• Study Level: Undergraduate
• Course type: Special Background
• Teaching and exams language: Greek
• Teaching Methods (Hours/Week): Lectures and Practice work (3)
• ECTS Units: 5
• Course homepage: https://elearning.cm.ihu.gr/course/view.php?id=639
• Instructors: Kydros Dimitrios
• Class Schedule:
  • Tue 15:00 - 18:00: Θ+Α, Κύδρος, Υπ. Διδ. Γκέγκας, ΕΡΓ. ΠΛΗΡ. ΣΥΣΤ.
• Exams Schedule:
  • 21/06/2024, 13:00 - 15:00: Κύδρος (Εργαστήριο Πληροφορικής)

Course Contents

1. Introductory points.
2. The Python language: installation, usage, expressions.
3. Python: Lists and their management.
4. Python: Conditional Statements, Repeat Statements.
5. Python: Functions, recursion.
6. Python: Arrays, Search, Sort.
7. Python: Numpy, Pandas.
8. Python visualizations, matplotlib.
9. Python and machine learning.
10. R: Installation and presentation.
11. Basic commands of R.
12. R: Visualizations single and multiple variables, Hypothesis testing.
13. Course revision.

Educational Goals

The purpose of the course is to introduce the students to the concepts of Data Science, including the concepts of big data analytics, data mining, business data analysis, etc. For this purpose, the teaching of the Python programming language is initially followed  and reference is made in the (statistical purpose) programming language R. After acquiring the knowledge of the tools, an introduction is made to the basic issues of Data Science.

After successful completion of the course, students will be able to:

• Have developed a sufficient knowledge of the Python programming language and an introductory knowledge of the R programming language.
• Study ready-made code that solves specific data science problems.
• Develop code in Python and/or R to solve specific problems.
• Gain an initial knowledge in issues such as Neural Networks, Artificial Intelligence, Machine Learning.
• Recognize the need for data modeling
• Apply and model issues related to the analysis of business and financial issues (in the context of Data Science), propose and implement related solutions.
• Have the ability and skills to present their results graphically.
• Evaluate situations in which it is necessary to study a large amount of data.
• Develop an ability to gain insights into the issue of analyzing large volumes of data.

General Skills

• Working independently.
• Working in an interdisciplinary environment.
• Decision making.
• Search for, analysis and synthesis of data and information, with the use of the necessary technology.

Teaching Methods

• In the classroom, face to face.

Use of ICT means

• Basic software (windows, word, power point, the web, etc.).
• Support of learning process through the electronic platform / e-class.

Teaching Organization

Students Evaluation

Written final exams (60%) that may include:

• Judgemental questions.
• Short answer questions.
• Application exercises.
• Comparative theoratical questions.
• In each question, corresponding evaluation points are specified.

Optional assignment (Essay writing and presentation) corresponds to 40% of the final grade.

Recommended Bibliography

1. Νικόλαος Αβούρης, Μιχαήλ Κουκιάς, Βασίλειος Παλιουράς, Κυριάκος Σγάρμπας, Python – Εισαγωγή στους υπολογιστές, ΙΔΡΥΜΑ ΤΕΧΝΟΛΟΓΙΑΣ & ΕΡΕΥΝΑΣ-ΠΑΝΕΠΙΣΤΗΜΙΑΚΕΣ ΕΚΔΟΣΕΙΣ ΚΡΗΤΗΣ, 2018.
2. Μανής, Γ., 2015. Εισαγωγή στον Προγραμματισμό με αρωγό τη γλώσσα Python. [ηλεκτρ. βιβλ.] Αθήνα:Σύνδεσμος Ελληνικών Ακαδημαϊκών Βιβλιοθηκών. Διαθέσιμο στο: http://hdl.handle.net/11419/2745
3. Robert Johanson, Numerical Python, Apress, 2015, link.springer.com/content/pdf/10.1007%2F978-1-4842-0553-2.pdf
4. David Ditrich et al., Data Science and Big Data Analytics, Wiley, 2014, https://onlinelibrary.wiley.com/doi/pdf/10.1002/9781119183686