Statistics I

Uitgebreide vaknaam	Statistics I
Leerdoelen	Upon the successful completion of this course, students will be able to: - Understand data sampling including sampling techniques, advantages and drawbacks, and generalisability - Understand the basic characteristics of variables and associated limitations - Inspect and analyse the data taking into account the variable and data-set characteristics and the research design - Interpret the results - Report and reflect on method, data, and results
Omschrijving	Practical knowledge of statistics is a fundamental skill for researchers in the all scientific disciplines. The recent growth of Big Data Applications and Data Science only enhances the need for students to have solid theoretical knowledge of statistical analysis to help them understand their own data as well as the analyses performed by others. The aim of Statistics I is to engage students with the fundamental concepts of statistical analysis and basic tools in statistical analysis using descriptive and univariate analysis, and research data management. Starting with the theoretical background of statistics, students are taught to engage critically with data-set characteristics; samples and populations, sampling strategies, generalisability, and bias. Subsequently, characteristics of the data in the data-set are discussed, dealing with measurement levels, central tendency, dispersion, distributions, and generalisations using the central limit theorem. The final part of the course focuses on univariate statistical techniques: z-score and z-test, t-test and non-parametric alternatives, binomial test, and difference of proportion test. Throughout the course, students are required to use R for their statistical analysis. A working knowledge of R, with its large developer support and comprehensive library of basic and cutting edge statistical packages, means students will be able to easily transition from basic to more advanced statistical tools. R also provides interfaces from R to Python, and from Python to R, which allows students to easily transfer their knowledge between Statistics I, and Introduction to Programming and Introduction to Data Science. Throughout the course, students will be taught to follow best-practices regarding ‘reproducible research’, in data management and transformation, analysis, and visualisation.
Uren per week
Onderwijsvorm	nog niet bekend (2 hours Lectures and 2 hours computer lab each week)
Toetsvorm	nog niet bekend (active participation (10% of final grade), four multiple choice tests (each 5% of the grade, 20% of the final grade in total), two take home (computer) exercises (each 10% of the final grade, 20% of final grade in total), presentation (20% of final grade) and final essay (30% of final grade).)
Vaksoort	bachelor
Docent(en)	O. Engel, PhD. , Vijaya Kedari
Verplichte literatuur	Titel Auteur ISBN Prijs R for data science (open-access through http://r4ds.had.co.nz/). Grolemund, G., & Wickham, H. (2017) OpenIntro Statistics (open-access through https://leanpub.com/openintro-statistics click on read free sample, then pdf) Diez, D. M., Barr C. D., & Çetinkaya-Rundel, M. (2019)
Entreevoorwaarden	Admission to the Bachelor’s degree programme of Global Responsibility & Leadership
Opmerkingen
Opgenomen in	Opleiding Jaar Periode Type BSc Global Responsibility and Leadership 1 semester II a verplicht