This repository is a companion resource for the free Udemy short course 'Teaching Data Science: A Short Course for Instructors' by MatrixDS The purpose of the course is to provide a meta-framework and considerations when building a curriculum. There is a growing body of tutorials and courses that cover data science, machine learning, and artificial intelligence. However, many of these courses are done without context or with tools that are difficult to apply after instruction.
For example, consider Andrew Ng's Machine Learning course on Corsera. This course is top-rated and very well done in almost every respect. However, all of the exercises are done with Matlab / Octave; languages that do not enjoy significant popularity in the data science community. Students must refactor code and examples into another programming language after the course if they want to apply the lessons with R or Python.
The following sections are references for the course and attempt to provide a set of resources for professors, instructors, and teachers building courses that have a lasting impact on their students.
- Stanford Machine Learning (CS229)
- Stanford Deep Learning (CS230)
- Stanford CNN (CS231n)
- Stanford RNN (CS224d)
- MIT Self Driving Cars
- Hugo Larochelle
- IBM Advanced Data Science
- Machine Learning with Tensorflow by Google
- Amazon Web Services Machine Learning Training
- Microsoft Professional Program in Data Science
- Fast.ai
- Data Camp Introduction to R
- Data Camp Introduction to Python for Data Science
- Deeplearning.ai
- RStudio Server
- Jupyterlab
- Tableau (Dashboards and BI)
- Superset (Dashboards and BI)
- Python Data Science Handbook