List of useful Bites of Information
Resources that might be helpful is
Things to remember:
- SlideShare (http://www.slideshare.net/)
- Available Datasets (http://www.statsci.org/datasets.html)
- Youtube: There are lots of good videos that can walk you through multitude of algorithms, application, and challenges. This can be just a start point. You need to do your own search on what you are interested to know.
- Kaggle: a data science competition platform (https://www.kaggle.com/)
- Jabref (reference management software) (http://jabref.sourceforge.net/) [We will talk about it a little when we meet next time]
- Coursera (https://www.coursera.org/)
Things to remember:
- Abstracts, introductions, and conclusions give you a quick overview of the content of the paper so always start with them and then assess if you will read through the paper or not.
- Write review notes of the papers you read for later reference.
- Always think "What?Why?How?"
- When you are reading a paper
- What is the problem that they are trying to solve?
- Why is it important?
- How did they solve it?
- After reading a paper?
- What can I contribute?
- Why is it valuable?
- How will I do it?
- When you are reading a paper
- Adobe Reader provides a read out loud feature that might help you read papers faster (it depends on individual preference)