Introduction to Data Science and Statistics
preliminary — subject to changes
This course introduces to the theory and practice of describing and analysing data, testing hypotheses and how to use the free statistical computing software R for your data analyses.
Topics discussed include, but are not limited to, combinatorics, probability, random variables, density and distribution functions, sampling from a population, sample properties and descriptive summary statistics, and inferring population characteristics from a random sample.
The course will enable students to describe and to analyse data using simple statistical methods and to interpret and to report their data analyses results. Students will learn how to use the statistical computing software R.
The course will follow the textbook:
The required readings from this textbook are listed below.
Additionally, the following text may be used as a reference:
Students must read the corresponding chapters of the textbook before each session. Students need access to a computer with the statistical computing software R. The software is available free of charge from www.r-project.org. As a graphical frontend RStudio is recommended.
Prof. Dr. Dennis A. V. Dittrich
You can always reach me via email. For meetings in my office appointments can be arranged through my webpage at: http://economicscience.net/content/book-appointment.
Updated information, links to the literature, additional materials, etc. can be found on my webpage as well.
A typical 3 credit course requires 150 hours of your time. The table below identifies how I expect those 150 hours will be allocated. While you do not receive direct marks for reading, reading will affect your class participation (your ability to participate in class discussions and activities) and your final exam mark.
|Class Time (3 hours / week)||45 hours|
|Reading (2 hours / week)||30 hours|
|Problem Sets (2 hour / week)||30 hours|
|Preparation and Review (3 hours / week)||45 hours|
Weekly Topics and Reading Assignments
Session 1: 13.02.
- Introduction to course and to data science and statistics
Session 2: 20.02.
Session 3: 27.02.
Session 4: 06.03.
Session 5: 13.03.
Session 6: 20.03.
Session 7: 27.03.
Session 8: 03.04.
Session 9: 10.04.
Session 10: 17.04.
Session 11: 29.04. (Monday)
Session 12: 08.05.
Session 13: 15.05.
Session 14: 22.05.
Session 15: 29.05.
Topics and reading assignments are subject to changes.