Data Analysis
ECTS : 6
Description du contenu de l'enseignement :
Course description and objectives
The aim of this course is to introduce the students to the fundamental methods in Data analysis. This course aims to teach students how to present, analyze and interpret data by using the statistical analysis software package R. Following the course will help students to get familiar to the R ecosystem and learn how to use R for the most common data analysis’s projects.
Topics include numerical and graphical summaries of data, qualitative and quantitative univariate analysis, bivariate analysis with the study of the links between two variables, analysis of variance, regression, principal components, factor analysis and cluster analysis.
The course focuses on simple predictive analysis (linear regression or multidimensional analysis, factor approach, principal components approach). The courses take place in the computer lab in order to emphasize on practical aspects of data analysis. However, with the Covid 19 crisis, a distance course has been built allowing interactions with the students.
Course structure
- Data visualization with a statistic software
- Descriptive statistics
- Sampling and statistical inference
- Analyzing relationships among two qualitative variables
- ANOVA
- Regression and prediction
- Time series
- Principal components analysis
- Correspondence analysis
- Clustering
- Application
- Final Exam
Compétence à acquérir :
Learning outcomes
At the end of the course, students are able to describe and present data, to summarize different types of variables, to analyze the relation between these variable, to practice regression and prediction, to cluster and compare different groups of observations. At the end of the course all students are quite familiar with the R environment.
Mode de contrôle des connaissances :
Assignments and grading
- Active class participation is a main feature of the course’s organization. Students work independently on real datasets with data analysis software. A final exam evaluates the students. The final exam is also on computer. The final exam counts for 50% of the note. Participation counts for another 50% of the note.
The numerical grade distribution will dictate the final grade. The passing grade for a course is 10/20.
Class participation: Active class participation – this is what makes classes lively and instructive. Come on time and prepared. Class participation is based on quality of comments, not quantity.
Exam policy: In the exam, students will not be allowed to bring any document (except if allowed by the lecturer). Unexcused absences from exams or failure to submit cases will result in zero grades in the calculation of numerical averages. Exams are collected at the end of examination periods.
The numerical grade distribution will dictate the final grade. The passing grade for a course is 10/20.
Class participation: Active class participation – this is what makes classes lively and instructive. Come on time and prepared. Class participation is based on quality of comments, not quantity.
Exam policy: In the exam, students will not be allowed to bring any document (except if allowed by the lecturer). Unexcused absences from exams or failure to submit cases will result in zero grades in the calculation of numerical averages. Exams are collected at the end of examination periods.Bibliographie, lectures recommandées :
Bibliography
- Heumann (2016), Introduction to Statistics and Data Analysis, Springer, 455 pages
- J.L. Devore (2011), Introduction to Statistics and Data Analysis, 4th Edition, 944 pages
- C. Judd (2017), Data Analysis, New Edition, 366 pages
- D.S Moore (2009), Introduction to the Practice of Statistics, Freeman, 690 pages
- ·Daniel J. Denis (2020), Univariate, Bivariate, and Multivariate Statistics Using R, Wiley, 384 pages
- Mustapha Abiodun Akinkunmi (2019), Business Statistics with Solutions in R, De Gruyter, 276 pages
- Christian Heumann, Michael Schomaker, et al. (2017), Introduction to Statistics and Data Analysis: With Exercises, Solutions and Applications in R, Springer
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