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Detaljni izvedbeni plan
Akademska godina 2023. / 2024. Semestar Zimski
Studij:

Sveučilišni diplomski studij povijesti, Sveučilišni diplomski studij sociologije, Sveučilišni diplomski studij komunikologije		 Godina studija:

Sveučilišni diplomski studij povijesti: 1., 2.;
Sveučilišni diplomski studij sociologije: 1., 2.;
Sveučilišni diplomski studij komunikologije: 1., 2.;
Usmjerenje Znanstveno istraživanje medija i odnosi s javnošću, Interkulturalna komunikacija i novinarstvo, Upravljanje i javne politike

I. OSNOVNI PODACI O PREDMETU
Naziv predmeta Data Science for Social Scientists
Kratica predmeta IZBD251 Šifra predmeta 252571
Status predmeta Izborni ECTS bodovi 6
Preduvjeti za upis predmeta Prerequisites only for Croatian students - Introduction to Statistics
Ukupno opterećenje predmeta
Vrsta nastave Ukupno sati
Predavanja 30
Seminari 30
Mjesto i vrijeme održavanja nastave HKS – prema objavljenom rasporedu

II. NASTAVNO OSOBLJE
Nositelj predmeta
Ime i prezime Luka Šikić
Akademski stupanj/naziv Doktor znanosti Izbor Docent
Kontakt e-mail luka.sikic@unicath.hr Telefon +385 (1)
Konzultacije Prema objavljenom rasporedu
Suradnici na predmetu
Ime i prezime Petra Palić
Akademski stupanj/naziv Doktorica znanosti Izbor Docent
Kontakt e-mail petra.palic@unicath.hr Telefon +385 (1)
Konzultacije Prema objavljenom rasporedu

III. DETALJNI PODACI O PREDMETU
Jezik na kojem se nastava održava Engleski
Opis
predmeta

 

Course Objectives:

This course covers the fundamentals of data science for social scientists on a graduate level, including data collection, analysis, and visualization. Students will gain hands-on experience using statistical software, data collection, statistical analysis, and machine learning algorithms to analyze data and answer social science research questions. The course will also cover effective communication of data findings, helping students develop skills to communicate their research findings to different audiences effectively.

 

Course Content:

Introduction to Data Science for Social Scientists. Data Collection and Cleaning. Exploratory and Confirmatory Data Analysis. Machine Learning for Social Science. Communicating Data Findings. Capstone Project.

 
Očekivani ishodi
učenja na razini
predmeta		 1. Understand the basics of data science and how it can be applied to social science research. 2. Develop proficiency in using statistical software for data analysis. 3. Learn how to collect, clean, and organize data for analysis. 4. Understand different data visualization techniques and how to communicate data findings effectively. 5. Apply data science techniques to real-world social science problems and research questions.
Literatura
Obvezna

Wickham, H., & Grolemund, G. (2017). R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. O’Reilly Media, Inc.
Dopunska

Provost, F., & Fawcett, T. (2013). Data Science for Social Good: What You Need to Know about Data Mining and Data-Analytic Thinking. O’Reilly Media, Inc.

McKinney, W. (2017). Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython. O’Reilly Media, Inc.

Healy, K. (2018). Data Visualization: A Practical Introduction. Princeton University Press.

Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer.
Način ispitivanja i ocjenjivanja
Polaže seDa Isključivo kontinuirano praćenje nastaveNe Ulazi u prosjekDa
Preduvjeti za dobivanje
potpisa i polaganje
završnog ispita

Attendance is crucial for success in this course, and students are expected to attend at least 70% of lectures and seminar sessions. This will allow them to stay up-to-date with the course content and participate in discussions and group activities.

In addition to attending lectures and seminars, students will be required to complete a data analysis and visualization project, which will be presented as an oral seminar presentation. This project will allow students to apply the data science skills they have learned to a real-world social science research problem.

To successfully complete the course, students must accumulate at least 35% of their grade through class activities, including written and presented and seminar project. This will ensure that students are regularly engaging with the course content and actively working towards mastering the skills and concepts covered in the course.
Način polaganja ispita

Class activities: Midterm exam (written), seminar presentation (written and oral) and final exam (oral)
Način ocjenjivanja

 

Final course grade is based on 100 points earned through student’s continuous involvement in class activities:

Fair (2) – 50 to 64 points

Good (3) – 65 to 79 points

Very good (4) – 80 to 89 points

Excellent (5) – 90 to 100 points

Earning credits:

Class activities contribute to 70% of the grade:

Midterm exam – maximum 40 points

Seminar – maximum 20 points

Seminar presentation – maximum 10 points

Final exam contributes to 30% of the grade:

Final exam – maximum of 30 points
Detaljan prikaz ocjenjivanja unutar Europskoga sustava za prijenos bodova
VRSTA AKTIVNOSTI ECTS bodovi - koeficijent
opterećenja studenata		 UDIO
OCJENE
(%)
Pohađanje nastave 1.5 0
Kolokvij-međuispit 1.8 40
Seminarski rad 0.9 20
Seminarsko izlaganje 0.45 10
Ukupno tijekom nastave 4.65 70
Završni ispit 1.35 30
UKUPNO BODOVA (nastava+zav.ispit) 6 100
Datumi kolokvija The first exam in the 7th week of the course and the second exam in the 15th week.
Datumi ispitnih rokova Prema objavljenom rasporedu

IV. TJEDNI PLAN NASTAVE
Predavanja
Tjedan Tema
1. Introduction to the Course.
2. Traditional Data Types
3. Modern Data Sources.
4. Basics of the R Programming Language.
5. Data Manipulation and Preparation.
6. Collecting Data from the Internet I.
7. Collecting Data from the Internet II.
8. Working with Databases.
9. Descriptive Statistics.
10. Univariate Statistical Analysis.
11. Multivariate Statistical Analysis.
12. Introduction to Machine Learning.
13. Machine Text Analysis.
14. Presentation, Publication, and Sharing of Results.
15. Final Exam.
Seminari
Tjedan Tema
1. Introduction to the Course.
2. Traditional Data Types
3. Modern Data Sources.
4. Basics of the R Programming Language.
5. Data Manipulation and Preparation.
6. Collecting Data from the Internet I.
7. Collecting Data from the Internet II.
8. Working with Databases.
9. Descriptive Statistics.
10. Univariate Statistical Analysis.
11. Multivariate Statistical Analysis.
12. Introduction to Machine Learning.
13. Machine Text Analysis.
14. Presentation, Publication, and Sharing of Results.
15. Final Exam.