When you have finished this course, you should feel comfortable using a computer and modern, powerful open source software. The ultimate goal of this course is to make sure that all students, regardless of their affinity with computers coming in to this course, can either solve simple problems themselves or know how to find a solution (online).
To achieve this, students learn to write texts in markdown and create jupyter notebooks. Students learn elementary programming concepts (loops, data structures, dataframes, ...) in Python and R. Students also learn linear algebra, making this course self-contained with respect to required mathematical knowledge. Students will be able to analyze data, report analyses using graphs or use simulations to explain difficult concepts to a non-technical audience (say, your future employer and colleagues).
For students with little or no background in programming this course may seem daunting. This course is developed in such a way that every student, who puts some effort into it, will acquire basic programming skills. This course creates a level playing field, in terms of computer skills, in anticipation of more advanced courses using R, Python and other software.
Notebooks allow you to combine code (in either R or python) with text to explain what you are doing. This makes it easy to reproduce your research. Moreover, it allows you to explain the economics and econometrics that you use to people who do not necessarily have a background in economics.
Python and R are two very popular programming languages. Python is a general purpose language, while R is a programming language dedicated to data analysis and statistics.
Interactive tutorials and some lectures
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