Data Science: Programming with Python

About

Python has become the dominant programming language used in data science. This course offers an introduction to computational thinking about data-related problems and the implementation of data analysis programs with Python. It starts at the very basics and is explicitly intended for students who have no or only little programming experience.

Programming is the process of designing and building an executable computer program for accomplishing a specific computational task. The course will introduce you to programming with Python, which is currently one of the most popular programming languages in (data) science. After familiarization with the basics (input and output, variables, data types, data structures, conditional branching, loops, functions, etc.) the course will address specific data science topics, such as statistical analyses with the pandas package and data visualization with matplotlib.

Every day, short lectures will be combined with practicals, where students can practice with example datasets that will vary over the course of the week. In the afternoon, students will work in small project groups on applying the lessons of the day to a real-life dataset.

More details on the day-by-day programme can be found in a separate file. Broadly, the following topics are discussed:

Day 1: getting started, the programming environment, editing and running Python programs, input and output, variables, arithmetic expressions, conditional branching

Day 2: loops, functions, the standard library, data structures

Day 3: basics of object-oriented programming, file I/O, data frames, statistical analyses with the pandas package

Day 4: data visualization with matplotlib, matrix computations with numpy

Day 5: group presentations, best practices for software project management

Course credits of 1.5 EC are offered to students who attend meetings every day, actively participate in the exercises and participate in the presentations of the group assignments on the final day of the course.

The course will use freely available literature that will be made available to course participants during the course. The literature serves both as a practical guide to course materials, and more in-depth reading that can be done during or after the course.

Participants are requested to bring their own laptop computer. Software will be available online.

Course leader

dr. Anastasia Giachanou

Target group

The course requires no specific previous knowledge, in particular no prior programming skills. You will need to bring your own laptop to do the exercises. Any operating system (Windows, Mac OSX, Linux) is fine, as long as new software can be installed on the machine. We assume that you have elemental computer skills such as browser usage, storing files, installing programs, etc.

Course aim

After finishing the course successfully, you will be able to:

- think computationally about data-related problems
- design programs for specific computational tasks
- write Python programs for specific computational tasks, including, e.g., asking and reading input from the user, - loading data from files, preprocessing and analyzing data, performing calculations, simulating processes, -
- visualizing data and results, storing data and results into files
- validate Python programs for correct functioning
- document and describe Python programs

Fee info