Introduction to Data Science
Learning Outcomes
The aim of the course is to teach basic skills related to the use of computer systems and their applications as the main way for processing and understanding data today. After successful completion of the course, students are expected to be able to:
- Describe the role and importance of Data Science in the field of Economics
- Utilize methods for approaching statistical data processing problems through data science
- Use spreadsheets and the R programming language to carry out basic statistical analysis and data manipulation
- Assess and evaluate computing tools with respect for data processing and statistical analysis depending on the nature of the problem
Course Contents
The importance of data science in Economics. Use of spreadsheets (Excel/OpenOffice Calc) for statistical data processing. Descriptive statistics with spreadsheets. Generating random data. Data visualization. Introduction to the R programming language. Handling and manipulating economic data sets with using data frames. Data slicing using data frames. Descriptive statistics. Data visualization. Use of open data from government organizations (e.g. statistical authorities) to facilitate the learning of data handling, manipulation and statistical processing with both set of tools. Comparing and assessing the strength of each tool in the context of data science. Data science and artificial intelligence.
Teaching Activities
Lectures (4 hours per week) and Laboratory exercises (2 hours per week)
Teaching Organization
|
Activity |
Semester workload |
| Lectures (3 hours per week x 13 weeks) |
39 hours |
| Lab exercises (2 hours per week x 13 weeks) |
26 hours |
| Team homework |
52 hours |
| Individual quizzes and self study |
33 hours |
| Total number of hours for the Course (25 hours of work-load per ECTS credit) |
150 hours (total student work-load) |
Assessment
- Quizzes on the E-Learning platform
- Two homework assignments conducted in team work on using software to perform statistical data processing and analysis: 30%
- Final exam (Short and problem-solving questions): 70%
Use of ICT
- Slides and notes to support lectures
- Software for demonstration and practical application purposes to show statistical data processing
- Use of the E-Learning platform eclass in order to:
- Organize the course material (slides, notes, examples, code snippets etc)
- Perform weekly online quizzes to evaluate the understanding of the related course material
- Hand in homeworks
- Communicate with the students and the class
Teaching Hours: 5
ECTS Credits: 6
Teaching Credits: 3
Weight: 1.5
Type:
Language: Ελληνική
Teaching Method: Πρόσωπο με πρόσωπο
General Competences: Αναζήτηση, ανάλυση και σύνθεση δεδομένων και πληροφοριών, με τη χρήση και των απαραίτητων τεχνολογιών, Ομαδική εργασία
Γνωστικό Αντικείμενο: Internet Information Systems
Οργανική Μονάδα / Εργαστήριο:
E-mail: daskalou@upatras.gr
Τηλέφωνο: +30 2610 997788
Ώρες γραφείου: Monday 13:30-15:00 Wednesday 11:00-12:30
Γνωστικό Αντικείμενο: Information and Knowledge Management
Οργανική Μονάδα / Εργαστήριο:
E-mail: tzagara@upatras.gr
Τηλέφωνο: +30 2610 962588
Ώρες γραφείου: Mon 10:00 - 12:00 Fri 11:00-12:00
eclass URL: e-class
– Suggested bibliography:
- Η Επιστήμη των Δεδομένων, Β, Βερύκιος, Σ. Κωτσιαντής, Η. Σταυρόπουλος, Μ. Τζαγκαράκης, ΕΚΔΟΣΕΙΣ ΝΕΩΝ ΤΕΧΝΟΛΟΓΙΩΝ ΙΔΙΩΤΙΚΗ ΚΕΦΑΛΑΙΟΥΧΙΚΗ ΕΤΑΙΡΕΙΑ, ISBN 9789605780432, 2018 (in greek)
- Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking, Provost, F., Fawcett, T., O’Reilly Media, ISBN 1449361323, 2013
- Patterson, D. A., Basham, R. E., Data Analysis With Spreadsheets, Pearson College Div, 020540751X, 2005
- Winston, W., Microsoft Excel Data Analysis and Business Modeling, Microsoft Press, ISBN 1509304215, 2016
- Wickham, H., Grolemund, G., R for Data Science: Import, Tidy, Transform, Visualize, and Model Data, O’Reilly, ISBN 1491910399, 2017
- Matloff, N.,The Art of R Programming: A Tour of Statistical Software Design, No Starch Press, ISBN 1593273843, 2011
