The repository consists of eight classes covering the successive steps of Data Science and ML process. Each class includes a Jupyter notebook with the core material and additional artifacts (flat files, Julia scripts, images, etc.). The notebooks creates a logically progressing series, but may be run independently as well.
The topics covered are as follows:
- Data Retrieval and Manipulation
- Programming Exercises in Simulation, ML and Statistics
- Unsupervised Machine Learning
- Evaluation of Classification Models
- Evaluation and Analysis of OLS Regression Model
- Explaining The ML Models - Interpretable AI
- AutoML
- Model Deployment and Monitoring
If you'd like to test your Julia and Data Science skills in practice, you may be interested in the Hands-On Data Science with Julia - a collection of projects focused on solving business problems with Julia, data analysis and modelling.
Make sure you have the Julia installed. The course was prepared under Julia 1.9.2.
- Clone the repository to a local folder on your computer:
git clone https://github.com/KrainskiL/JuliaDataScienceTutorial- Start Julia in your local folder:
cd JuliaDataScienceTutorial
julia --project- Run the following commands in the Julia REPL:
using Pkg
Pkg.instantiate()
Pkg.status()- Start Jupyter Notebook with:
using IJulia
notebook(dir=pwd())Preparation of the educational materials has been supported by the Polish National Agency for Academic Exchange under the Strategic Partnerships programme, grant number BPI/PST/2021/1/00069/U/00001.
