Content. This course introduces the critical tasks of data collection and data wrangling, presentation and understanding of descriptive statistics and basics of visualization. The course focuses on classic statistics methods and their applications, such as data collection and sampling, generalization from the sample to the population and hypothesis testing.
Relevance. About 80% of data science tasks are composed of managing data, from understanding and altering features of the dataset and variables, to combining various datasets. Learning to manipulate and clean dataset, prepare for analysis and carry out exploratory data analysis are crucial skills for anyone set to carry out data analysis.
By the end of the course, students will be able to
- Become familiar with learn the basics of working with data, manipulating datasets, doing exploratory data analysis as well visualization.
- Be able to produce meaningful descriptive statistics and informative graphs.
- Successfully formulate research questions that are answerable by empirical analysis;
- Discuss and interpret results, understand validity and constraints.
Other Outcomes - see syllabus
Grading will be based on the total score out of 100, in line with CEU Department of Economics and Business grading guidelines. In particular:
a. The median student can expect to get a B+
b. Probably not more than 1/3 of the students can expect to get an A or A-
c. The passing grade is 50%
The final grade is based on:
- start-of-the-class quizzes [10%]. There are six quizzes (2p), the best five of them count
- a final closed-book exam [90%]