Teachers
Teaching Assistants
Lectures/classes Tuesdays 18.00-22.00.
Location
Materials The lectures are backed reading material from various sources. These should be seen at suggestions. If you dislike the writing, try searching the web. There's a huge community behind the tools we are working with in this course. The reading material can be found in the weekplans.
Exercises The weekplans contain instructions and exercises for each week.
Slides Lecture slides on Github.
Data and other ressources Access the course repo on Github.
Plan is tentative and may be changed during the semester.
| Week | Topics |
|---|---|
| 1 | Command line tools and git |
| 2 | Working with data in Jupyter and Python #1 |
| 3 | Working with data in Jupyter and Python #2 |
| 4 | Databases #1 |
| 5 | Databases #2 |
| 6 | Project work |
| Holiday | |
| 7 | Streaming #1 |
| 8 | Streaming #2 |
| 9 | Project work |
| 10 | Parallel computation in Spark #1 |
| 11 | Parallel computation in Spark #2 |
| 12 | Guest lecture, title TBA |
| 13 | Guest lecture: "Voice Recognition in Python using Convolutional Neural Networks", Cosmin Sanda |
There are three projects in the course. Project descriptions will appear here, and you will be introduced to the projects in the lectures.
Collaboration policy All mandatory individual exercises are subject to the following collaboration policy. The exercises are individual. It is not allowed to collaborate on the exercises, except for discussing the text of the exercise with teachers and fellow students enrolled on the course in the same semester. Under no circumstances is it allowed to exchange, hand-over or in any other way communicate solutions or part of solutions to the exercises. It is not allowed to use solution from previous years, solutions from similar courses, or solutions found on the internet or elsewhere.
Can I skip lectures/classes due to conflicting courses, travelling, ...? The is no requirement for attendance, but we highly recommend attending for support and coaching.