Data Analysis: Advanced Level
- This course aims to provide an overview of advanced statistical techniques that arise in data analytic applications. In this class, you will learn and practice advanced data analytic techniques. One or more practical applications associated with each technique will also be discussed.
- At the end of the course, the students should be able to: - identify properties of the data, detect potential problems - treat data problems: sample bias, missings, excessively small/large samples
- - be able to conduct time-series analysis and analysis of choice
- - Define causal effects using potential outcomes
- - Implement causal inference methods (matching, instrumental variables, regression discontinuity, difference-in-difference, fixed effects) - Identify which causal assumptions are necessary for each type of statistical method
- - Express assumptions with causal graphs
- 1. Data problems: what can be wrong?
- 2. Correction of sample bias.
- 3. Missing data treatment.
- 4. Survival analysis
- 5. Choice modeling
- 6. Basics of causal inference
- 7. Causal Diagrams.
- 8. Statistical instruments for causal inference.
- Practical AssignmentsFor each topic, there will be a practical assignment. Students have to complete it either at class or as a homework. Each task wil be graded as 1 (done) or 0 (not done). Maximum grade for this part is 8, if all the tasks are completed.
- Project 1This project is assigned at the end of the first module. Students have to demonstrate their abilities to detect potential data problems and fix these problems. Two elements of grading are correct coding and correct interpretation.
- Project 2The project is assigned at the end of the second module. Students have to demonstrate their skills to implement a causal inference method and to rationalize their choice of the method.
- ExamExam is conducted in a form of take-home project. Students have to apply a set of methods studied during the course to get an answer for the given research question. Students have 48 hours to individually prepare and submit the paper
- 2022/2023 2nd module0.2 * Practical Assignments + 0.3 * Exam + 0.25 * Project 2 + 0.25 * Project 1
- Bertail, P., Blanke, D., Cornillon, P.-A., & Matzner-Løber, E. (2019). Nonparametric Statistics : 3rd ISNPS, Avignon, France, June 2016. Cham, Switzerland: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2044916
- Crawley, M. J. (2014). Statistics : An Introduction Using R (Vol. Second edition). Chichester, West Sussex, UK: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=846213
- Tabachnick, B. G., & Fidell, L. S. (2014). Using Multivariate Statistics: Pearson New International Edition (Vol. 6th ed). Harlow, Essex: Pearson. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=nlebk&AN=1418064
- Field, A. V. (DE-588)128714581, (DE-627)378310763, (DE-576)186310501, aut. (2012). Discovering statistics using R Andy Field, Jeremy Miles, Zoë Field.