rajadavidh / coursera-applied-data-science

Applied Data Science course from University Michigan in Coursera (in progress)

Applied Data Science from University of Michigan - Coursera

Specialization: https://www.coursera.org/specializations/data-science-python

TODO

• Course 1: Introduction to Data Science with Python
• Course 2: Applied Plotting, Charting & Data Representation
• Course 3: Applied Machine Learning
• Course 4: Applied Text Mining
• Course 5: Applied Social Network Analysis

How to download Jupyter notebook from the course

1. Open https://bmezydqoczesyhncakuvix.coursera-apps.org/tree
2. Select file one by one, then click download

Download whole directory (optional)

Source: jupyter/notebook#3092

Content of dog-project folder are:

1. Dogproject.ipynb
2. dogimages folder
3. some other usefull files

Now you want to zip the dog-project folder.

1. Click on the orange Jupyter circle on the top left of the workspace(Dogproject.ipynb).
2. You will see dog-project folder. Now don't go inside it.
3. Open Terminal by Clicking New(look right ) -> Terminal.
4. Zip the dog-project folder with the following command in the terminal: zip -r dog-project.zip dog-project
5. Alternatively one can use tar command if zip is not available: tar czf dog-project.tar.gz dog-project

Course 1: Introduction to Data Science with Python

https://www.coursera.org/learn/python-data-analysis

Read more on lesson learned from Course 1

View interesting quiz solution in:

• week#1
• week#2
• week#3
• week#4

Course 2: Applied Plotting, Charting & Data Representation

https://www.coursera.org/learn/python-plotting

TODO

Course 3: Applied Machine Learning

https://www.coursera.org/learn/python-machine-learning

TODO

Course 4: Applied Text Mining

https://www.coursera.org/learn/python-text-mining

TODO

Course 5: Applied Social Network Analysis

https://www.coursera.org/learn/python-social-network-analysis

TODO

Additional course resources

• Python Docs (for general Python documentation)
• Python Classes Docs
• Pandas on Stack Overflow