The purpose of this repository is to serve as an interactive "book" for a thorough introduction to Python.
All examples and case studies are created with the goal to prepare the student for further courses in the big and young field of data science.
The "chapters" are written as Jupyter notebooks which are a de-facto standard for exchanging code and results among data science professionals and researchers. As such they can be viewed in a plain web browser (just click on the links in the list above).
However, it is recommended to install Python and Jupyter locally and run the notebooks. This way, the student can play with the code and learn more efficiently. Precise installation instructions are either in the 00th notebook or further below.
Feedback is encouraged and will be incorporated.
Ideally, connect with me on LinkedIn.
- understanding of the English language
- basic mathematics from high school (i.e., addition, subtraction, multiplication, division, and a bit more)
- ability / willingness to invest 2-4 hours a day for a month (see "ABC"-rule in the 00th notebook)
To follow this course, a working installation of Python 3.6 or higher is expected.
To download the course's materials as a ZIP file, click on the green "Clone or download" button on the top right on this website. Then, unpack the ZIP file into a folder of your choosing (the folder should be under your personal folder if you want to avoid some tedious configuation using the Anaconda option below). Alternatively, use the git command-line tool and clone this repository.
Since Jupyter notebooks have become a de-facto standard for communicating and exchanging results in the data science community (both in academia and business), the corresponding extensions have to be installed as well.
There are two recommended ways to install Python and Jupyter for this course. The second approach is probably more beginner friendly.
First, check the official
Python installation instructions for your
operating system and install the latest version. This includes only the core
parts of Python and the Standard Library. Then, using the pip or pipenv
command-line tools install the jupyter package (see the notes at the bottom
of this link), ideally using a
virtual environment.
After installation, open a terminal, switch to the folder created as above, and
type jupyter notebook, possibly after activating the virtual environment
(e.g., with entering source venv/bin/activate). Then a new tab should open in
your web browser and display a file overview type of page. Go to the folder
where you unpacked the downloaded ZIP archive and start working through the
notebooks in order.
Another very popular way is to use a Python distribution that comes pre-packaged with a lot of the so-called "scientific" packages (jupyter being among them) such as the Anaconda Distribution. Just go to the download page and install the latest version for your operating system.
Then you will find an entry in your start menu like below. Click on it and a new tab in your browser will open, where you can switch between folders as you could in your computer's default file browser. Go to the folder where you unpacked the downloaded ZIP archive and start working through the notebooks in order.
