Welcome

Welcome#

`PyPro-SCiDaS` ¹ #

An Initiation to Programming using Python (Init2Py) #

¹ Python Proficiency for Scientific Computing and Data Science

This course is created with Jupyter notebooks that allow you to interact with all programming concepts to understand them better.

Key words:

Programming, Python language, Scientific computing, Data science.

Lectures#

Lectures can be viewed online as notebooks. They all have the same content. Upon opening the notebooks, you can launch them in Google Colab, or run them locally.

	Notebooks	PDF Takeaway	HTML view
0.	Introduction to python	lecture0.pdf	lecture0.html
1.	Variables & assignments	lecture1.pdf	lecture1.html
2.	Strings & Files	lecture2.pdf	lecture2.html
3.	Iterable objects or Containers	lecture3.pdf	lecture3.html
4.	Flow control	lecture4.pdf	lecture4.html
5.	Functions, Modules & Packages	lecture5.pdf	lecture5.html
6.	Numpy	lecture6.pdf	lecture6.html
7.	Matplotlib	lecture7.pdf	lecture7.html

Light practicals#

Download the lab notebooks and solve the questions locally, or launch them in Google Colab.

	Notebooks	A bit more
1.	Variables & assignments	Practical_1_more
2.	Strings & Files	Practical_2_more
3.	Iterable objects or Containers	Practical_3_more
4.	Flow control	Practical_4_more
5.	Functions, Modules & Packages	Individual projects
6.	Numpy	Individual projects
7.	Matplotlib	Individual projects

Challenges#

The aim of programming challenges is to enhance problem-solving skills, reinforce concepts, and improve coding proficiency by applying theoretical knowledge in practical scenarios. They encourage students to think critically, develop persistence, and learn new techniques and approaches. Challenges also promote creativity, resilience, and the ability to break down complex problems into manageable parts. Moreover, they provide excellent preparation for technical interviews and competitions, build confidence through a sense of accomplishment, and foster community building and networking among peers. By engaging in these challenges, students are exposed to a diverse set of problems that simulate real-world scenarios, making them better equipped to tackle unfamiliar issues. For this Python class, you will be participating in three programming challenges, which will help them solidify your understanding of the language, enhance their problem-solving capabilities, and prepare you for more advanced coding tasks.

Challenge Number	Challenge notebooks	Difficulty level	Due date
1	challenge_1	Beginner	September 29, 2024 at 8pm
2	challenge_2	Intermediate	October 5, 2024 at 8pm
3	challenge_3	Advanced	October 10, 2024 at 8pm

Labs#

Download the lab notebooks and solve the questions locally, or launch them in Google Colab.

Lab	Notebooks
0.	Taking control of a jupyter notebook
1.	Version Control with Git: `local`
2.	Version Control with Git: `remote`

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Note à l’attention des étudiants francophones#

Pour les étudiants francophones, les sites web suivants pourraient se révéler particulièrement utiles : In2Py avec des cahiers-virtuels, Atelier-3P, et le tutoriel Python. D’incroyables ressources peuvent également être trouvées à:

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Get your hands dirty

Retrieve all materials by cloning the GitHub repo. To run the notebooks locally, see the prerequisites.

Have some feedback?

If you notice any issue, or have suggestions or requests, please go the issue tracker or directly click on the icon on top of the page and then ‘open issue`. We also welcome pull requests :).

Useful resources#

Credit & Aknowledgement#

Aknowledgement - Technical Support

This jupyter book was done with the support of the technical team of ai.technipreneurs; Awadi Katanga and Domini Leko. Fell free to also connect with me on LinkedIn.

Credit - Book Template

This jupyter book was designed using this template and the credit goes to Joaquin Vanschoren.