Introduction to Data Science lectures
Here is the material for a course I will be giving in a Master of Data Science and AI for junior Data Scientists.
This is somehow a prequel of the other modules on
- Statistical Learning 📈
- Deep Learning 🦾
- Time Series ⌛
- Computer Vision Hands-On 👀️
- Recommender Systems 🚀
Install requirements
As usual, it is advisable to create a virtual environment to isolate dependencies. One can follow this guide and the suitable section according to the OS.
Once the virtual environment has been set up, one has to run the following instruction from a command line
pip install -r requirements.txt
This installs all the packages the code in this repository needs.
Interact with notebooks
Binder
You can use Binder, to interact with notebooks and play with the code and the exercises.
DeepNote
Alternatively, you can work on these notebooks in another online workspace called Deepnote. This allows you to play around with the code and access the assignments from your browser.
Contents of lectures
You can find the list of the arguments and some relevant material here.
Your lecturer 👨🏫
Oscar de Felice
I am a theoretical physicist, a passionate programmer and an AI curious.
I write medium articles (with very little amount of regularity), you can read them here. I also have a github profile where I store my personal open-source projects.
Questions
If you have any question, doubt or if you find mistakes, please open an issue or drop me an email.
Buy me a coffee ☕️
If you like these lectures, consider to buy me a coffee ☕️ !
