This course will introduce you to Data Science Machine Learning with Python, which is currently one of the most popular topic in data science.
The aim of the course is to teach some of the most important modeling and prediction techniques, along with relevant applications over the 5 days. Topics include linear regression, classification, tree-based methods, support vector machines, clustering, deep learning, hidden Markov models, and more.
The course starts at the very basics and is explicitly intended for students who have no or only little programming experience. After finishing the course successfully, participants will be able to think computationally about data-related problems, and design and write Python programs for computational tasks.
The course requires no specific previous knowledge, in particular no
prior programming skills.
Students need to bring their 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.
Students are expected to be familiar with Google Colab, the platform we’ll use for the course, which requires no installation.
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.
No graded activities are included in this course. Therefore, it is not possible to provide students with a transcript of grades. Students will obtain a certificate upon completion of this course.
The schedule and the topics of the meetings can be found here document.
TBD
Before coming to the first class, you will need to familiarize yourself with Google Colab, which is the platform we’ll be using during our sessions. Google Colab provides a cloud-based environment that allows you to write and execute Python code through your browser. It is easily accessible and does not require any local installation.
For a comprehensive understanding of how to use Google Colab, please refer to the instructions provided here.
Additionally, we encourage you to read some extra contextual information on Python and complete the computational thinking exercises available on the right.
We look forward to welcoming you all!
Bring a laptop computer to the course and make sure that you have full write access and administrator rights to the machine. Some corporate laptops come with limited access for their users, we therefore advice you to bring a personal laptop computer, if you have one.
Python (named after the British comedy group Monty Python) is one of the most popular programming languages today. It has been released for the first time already back in 1990, but gained extreme popularity only in the last years, together with the increasing popularity of big data, deep learning and data science.
There is a lot of free literature about Python available that you can use for the course. Here are some links to useful Python online books:
In order to start thinking in a computational way, we provide some
exercises that you can work on before the course starts. Those exercises
can be found in the following document.
The solutions
to these exercises will be discussed during the first meeting.
Materials will be uploaded later.
| When? | What? | |
|---|---|---|
| TBD | TBD | — |
| TBD | TBD | — |
| Lunch | ||
| TBD | TBD | — |
| TBD | TBD | — |
Materials will be uploaded later.
| When? | What? | |
|---|---|---|
| TBD | TBD | — |
| TBD | TBD | — |
| Lunch | ||
| TBD | TBD | — |
| TBD | TBD | — |
Materials will be uploaded later.
| When? | What? | |
|---|---|---|
| TBD | TBD | — |
| TBD | TBD | — |
| Lunch | ||
| TBD | TBD | — |
| TBD | TBD | — |
Materials will be uploaded later.
| When? | What? | |
|---|---|---|
| TBD | TBD | — |
| TBD | TBD | — |
| Lunch | ||
| TBD | TBD | — |
| TBD | TBD | — |
Materials will be uploaded later.
| When? | What? | |
|---|---|---|
| TBD | TBD | — |
| TBD | TBD | — |
| Lunch | ||
| TBD | TBD | — |
| TBD | TBD | — |