https://github.com/acclab/moda
Modern Data Analysis seminar
https://github.com/acclab/moda
Last synced: 5 months ago
JSON representation
Modern Data Analysis seminar
- Host: GitHub
- URL: https://github.com/acclab/moda
- Owner: ACCLAB
- License: apache-2.0
- Created: 2022-10-31T04:02:37.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2025-11-04T07:56:00.000Z (7 months ago)
- Last Synced: 2025-11-04T09:23:09.990Z (7 months ago)
- Language: Jupyter Notebook
- Homepage: https://acclab.github.io/moda/
- Size: 10.7 MB
- Stars: 0
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Modern Data Analysis
By [Sangyu Xu](https://xusangyu.com/), [Joses
Ho](https://twitter.com/jacuzzijo), [Yishan
Mai](https://twitter.com/myish_irl), and [Adam
Claridge-Chang](http://www.claridgechang.net/)
### Introducing biomedical scientists to data analysis with Python
The goal of this class is to introduce biomedical scientists to data
analysis with Python notebooks. Ideally, this should be a semester-long
course. This 2-hour class is presented to two groups.
- Duke-NUS PhD programme in Clinical and Translational Sciences.
Foundations of Precision Medicine Hands-on Workshops (GMS6812).
- Duke-NUS PhD programme in Integrated Biology and Medicine. Ethics and
Personal and Professional Development Sessions.
### The class has the following steps
1. **Preparation 1.** Read the
[Introduction](https://acclab.github.io/moda/introduction.html) and
install the prerequisite software on your laptop.
2. **Preparation 2.** Go through a [Quick Tour of the
Notebook](https://acclab.github.io/moda/quick_tour_of_the_notebook.html)
to familiarise yourself to the jupyter notebook environment.
3. **Session Coding.** Bring your laptop to class, and we will go
through the examples in [Data Analysis with
Jupyter](https://acclab.github.io/moda/data_analysis_with_jupyter_and_python.html).
4. **Lecture.** Crash course on the history of and key issues in data
visualization.
5. **Short introduction to estimation and LLMs.** Introduction to
estimation statistics [web app](https://www.estimationstats.com/#/)
and [python
package](https://acclab.github.io/moda/dabest_introduction.html).
6. **Continuing education.** Review the Additional Resources page to
continue your self-education in data analysis.
If you have any questions about these materials, please contact your
course coordinator.