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Two common use-cases for unsupervised learning are exploratory analysis and dimensionality reduction. Unsupervised learning is very useful in exploratory analysis because it can automatically identify structure in data.
"We choose supervised learning for applications when labeled data is available and the goal is to predict or classify future observations," Thota said. "We use unsupervised learning when labeled data is not available and the goal is to build strategies by identifying patterns or segments from the data."
The most commonly used Supervised Learning algorithms are decision tree, logistic regression, linear regression, support vector machine. The most commonly used Unsupervised Learning algorithms are k-means clustering, hierarchical clustering, and apriori algorithm.
Unsupervised learning is helpful for data science teams that don't know what they're looking for in data. It can be used to search for unknown similarities and differences in data and create corresponding groups. For example, user categorization by their social media activity.
If you a have labeled dataset you can use both. If you have no labels you only can use unsupervised learning.
It´s not a question of "better". It´s a question of what you want to achieve. E.g. clustering data is usually unsupervised – you want the algorithm to tell you how your data is structured. Categorizing is supervised since you need to teach your algorithm what is what in order to make predictions on unseen data.
See 1.
On a side note: These are very broad questions. I suggest you familiarize yourself with some ML foundations.
Good podcast for example here: http://ocdevel.com/podcasts/machine-learning
Very good book / notebooks by Jake VanderPlas: http://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/Index.ipynb
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