Introduction to Python
January 2018
Schedule: here
Day 1
Note: Make sure you have Anaconda (Python 3.6) and a text editor (e.g., Atom) installed.
Relevant cheatsheets: basics, numpy
Directory containing files for exercises: here
Solutions to exercises: here (for basics_part_1.py), here (for basics_part_2.py)
Day 2
Note: Make sure you have PsychoPy installed.
Relevant cheatsheets: pandas (basic), pandas (advanced)
Directory containing files for exercises (related to content covered in DataCamp): same as Day 1
Directory containing files for exercises with PsychoPy: here
Solutions to exercises: here (for Psychopy)
Day 3
Exercises: same as Day 2
If you're done, create your own experiment with PsychoPy!
Day 4
Choose one of two tracks:
Exercises: same as Day 4
January 2018
Schedule: here
Day 1
Note: Make sure you have Anaconda (Python 3.6) and a text editor (e.g., Atom) installed.
Relevant cheatsheets: basics, numpy
Directory containing files for exercises: here
Solutions to exercises: here (for basics_part_1.py), here (for basics_part_2.py)
Day 2
Note: Make sure you have PsychoPy installed.
Relevant cheatsheets: pandas (basic), pandas (advanced)
Directory containing files for exercises (related to content covered in DataCamp): same as Day 1
Directory containing files for exercises with PsychoPy: here
Solutions to exercises: here (for Psychopy)
Day 3
Exercises: same as Day 2
If you're done, create your own experiment with PsychoPy!
Day 4
Choose one of two tracks:
- Python Data Science Toolbox (Part I)
- Deep Learning in Python
- Exercises: Apply what you learn to your own dataset or other datasets
- https://machinelearningmastery.com/handwritten-digit-recognition-using-convolutional-neural-networks-python-keras/ (uses the same dataset as in DataCamp, but you get to see the pre-proccessing steps as well)
- https://en.wikipedia.org/wiki/List_of_datasets_for_machine_learning_research
- https://machinelearningmastery.com/handwritten-digit-recognition-using-convolutional-neural-networks-python-keras/ (uses the same dataset as in DataCamp, but you get to see the pre-proccessing steps as well)
- Exercises: Apply what you learn to your own dataset or other datasets
Exercises: same as Day 4