Programming in Python

• Course: Programming in Python | Programming for Data Science
• Instructor: Thomas Kopinski
• Semester: Winter 22/23
• Time: Check elearning platform for Zoom Credentials
• TAs: Felix Neubürger, Viktor Wolf

This course gives an introduction to modern Python programming for data science based on a unique BBC news media data set. The course contents start with a basic introduction to programming with Python as well as the relevant elements using Anaconda, git and Jupyter Notebooks. We then continue with learning about programming basics, routines and how to use external libraries. More advanced topics will cover Python-specific elements, data visualisation as well as object-oriented topics. Advanced elements will deal with data science essentials, typical problems in data science and how to solve basically any data question.

• Readings
• Grading
• Help
• Policies

Learning goals

• Acquire the fundamental programming concepts that are relevant to programming and data science. This goal will be assessed in the short homework assignments and the exams.
• Analyze and understand state-of-the-art algorithms and programming techniques for dealing with data science tasks. This goal will be assessed in the exams and the assigned projects.
• Implement state-of-the-art algorithms and programming techniques for dealing with data. This goal will be assessed in the assigned projects.
• Adapt and apply state-of-the-art programming technology to new problems and settings. This goal will be assessed in assigned projects.

Readings

Readings will be drawn mainly from my notes and notebooks. Additional readings may be assigned from books, blogposts, and tutorials.

Supplemental textbooks

These books will deepen your understanding of the material.

An introductory book to Python can be found in Think Python by Allen Downey.

A good starting point for resources can be found here.

Grading

To pass this course you will need to take part in the final exam. To pass the exam, 50% of the possible points need to be collected. No supplementary material is required during the exam. More details will be provided during class.

Depending on the current situation, the exam will be either a written or an oral test.

Office hours

My office hours are flexible, please contact me on demand.

Class policies

Attendance will not be taken, but you are responsible for knowing what happens in every class. If you cannot attend class, make sure you check up with someone who was there.

Respect your classmates and your instructor by preventing distractions. This means be on time, turn off your cellphone, and save side conversations for after class. If you can't read something I wrote on the board, or if you think I made a mistake in a derivation, please raise your hand and tell me!

Using a laptop in class is likely to reduce your education attainment. This has been documented by multiple studies, which are nicely summarized in the following article:

• https://www.nytimes.com/2017/11/22/business/laptops-not-during-lecture-or-meeting.html

I am not going to ban laptops, as long as they are not a distraction to anyone but the user. But I suggest you try pen and paper for a few weeks, and see if it helps.

Prerequisites

There are no prerequisites when taking this course, we will learn Python from scratch.

Collaboration policy

One of the goals of the assigned work is to assess your individual progress in meeting the learning objectives of the course. You may discuss the homework and projects with other students, but your work must be your own -- particularly all coding and writing. For example:

• It is allowed to discuss ideas of implementation, problems and concepts. How do algorithms work? What is the difference between various implementations? Which is the more efficient or elegant solutions.

• Solutions should not be copied from others but rather demonstrate one's own capability of solving a task or an issue. It is not acceptable to solve parts and exchange these with other students. Each code must be one's own contribution.

Using other people’s text or figures without attribution is plagiarism, and is never acceptable.

Data and Copyright

Data sets provided for this class have been collected by me with the permsission of its owners. Your are allowed to use the data set for tasks within this class, and may work with it on your own. It is not allowed to copy, alter, reproduce or change elements of these data sets on your own or present alterations or variations of any manner as your own work. Do not reproduce or distribute any material from these pages to other locations and/or persons.