https://github.com/astrazeneca/data-science-python-course
https://github.com/astrazeneca/data-science-python-course
Last synced: about 1 year ago
JSON representation
- Host: GitHub
- URL: https://github.com/astrazeneca/data-science-python-course
- Owner: AstraZeneca
- License: apache-2.0
- Created: 2022-02-06T17:01:06.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2023-09-26T12:36:33.000Z (over 2 years ago)
- Last Synced: 2025-03-31T17:59:06.484Z (about 1 year ago)
- Language: Jupyter Notebook
- Size: 26 MB
- Stars: 21
- Watchers: 5
- Forks: 17
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
## Data Science in Python
### Venue
Online training course run by the Data Science Academy, AstraZeneca, Cambridge (UK)
### Trainers
Gabriella Rustici, Sergio Martínez Cuesta, Leo Souliotis, Katarzyna Nurzynska, Michał Oziębło, Daniel Roythorne, Samuel Lewis, Caterina Darcy
### Course structure and links
Week | Title | Activity | Materials | Trainer
:---:|:-----:|:--------:|:---------:|:-------:
0 | Troubleshooting software installations | preparation | [Introduction and installations](notebooks/week0_materials.ipynb) | all
0 | Basic Python concepts and functions | preparation | [Materials](notebooks/week0_lecture.ipynb) | all
0 | Basic Python concepts and functions | preparation | [Solutions](notebooks/week0_solutions.ipynb) | all
1 | Data handling | Lecture | [Materials](notebooks/week1_lecture.ipynb) | JB
1 | Data handling | Recap | [Solutions](notebooks/week1_solutions.ipynb) | JB
2 | Data visualisation | Lecture | [Materials](notebooks/week2_lecture.ipynb) | JB
2 | Data visualisation | Recap | [Solutions](notebooks/week2_solutions.ipynb) | JB
3 | Key concepts in statistics and machine learning | Lecture | [Materials](notebooks/week3_lecture.ipynb) | LS
4 | Data analysis and modelling | Lecture | [Materials](notebooks/week4_lecture.ipynb) | LS
4 | Data analysis and modelling | Recap | [Solutions](notebooks/week4_solutions.ipynb) | LS
5 | Introduction to Machine Learning - part 1 | Lecture | [Materials](notebooks/week5_lecture_2.ipynb) | MO
5 | Introduction to Machine Learning - part 1 | Recap | [Solutions](notebooks/week5_solutions_2.ipynb) | MO
6 | Introduction to Machine Learning - part 2 | Lecture | Materials | MO
6 | Introduction to Machine Learning - part 2 | Recap | Solutions | MO
7 | Networks and Knowledge Graphs | Lecture | [Materials](notebooks/week7_lecture.ipynb) | KN
### References
- Data Science in Python by Data Science Academy at AstraZeneca:
- [June-July 2020 course](https://github.com/semacu/data-science-python)
- [January-February 2021 course](https://github.com/semacu/202101-data-science-python)
- [May-June 2021 course](https://github.com/semacu/202105-data-science-python)
- [October-November 2021 course](https://github.com/semacu/202110-data-science-python)
- The University of Cambridge [Introduction to Python course](https://github.com/pycam/python-basic)
- The University of Cambridge [Data Science in Python course](https://github.com/pycam/python-data-science)
- Data Carpentry [Python lessons](https://datacarpentry.org)
- The CRUK-CI [Introduction to R during COVID-19 course](https://bioinformatics-core-shared-training.github.io/r-intro/)
- Python pandas [documentation](https://pandas.pydata.org/docs/)