Introduction
Every one has their own learning sytle! If you need close hand holding and guidance – an easy going MOOC is probably the best place to start. However, if you are a quick learner and don’t need some one to explain a lot of context, some one who prefers to glance through concepts, apply them a bit and then again refer back to these concepts – presentations can be really handy!
The beauty about learning from presentations is that you can quickly zoom in on the section you want to and come out at your own pace – no one decides that for you! If it fits your learning style, they can be the quickest way of learning a new concept in data science, even faster than videos & blogs. These presentations allow you to achieve much more in less time. So, here is a list of some best data science presentations I have come across on SlideShare.
How can you learn from these Presentations?
Among all forms of media, presentations are probably the most crisp & to the point! You can use them in multiple ways:
- If you already know a concept, you can quickly revise them.
- If you don’t know a concept, you can quickly grab basic knowledge about them.
- If you are fed up of video buffering and reading lengthy articles, here’s the wittiest version of them.
Just like our gigantic universe is composed of several heavenly bodies, data science is composed of several disciplines which completes its universe. I’ve categorized the presentations in following segments:
Note: These presentations have been shortlisted on the basis of their relevancy and views. You are free to access the list in any way sequence you wish. These presentations are in no order.
Data Science, in General
How to Become a Data Scientist?
Views – 51,469
Data By The People, For The People
Views – 17,053
The Science of Managing Data Scientists
Views – 64,302
What does a Data Scientist do?
Views – 81,156
A Statistician View on Big Data and Data Science
Views – 36,305
Myths and Mathematical Superpowers of Data Scientists
View – 42,095
How to Interview a Data Scientist?
Let’s get a bit technical now!
Machine Learning
Introduction to Machine Learning
Views – 102,230
Introduction to Supervised Machine Learning and Pattern Classification
Views – 14,677
Machine Learning and Hadoop
Views – 17,822
Machine Learning and Data Mining: 12 Classification Rules
Views – 19,503
Machine Learning and Mahout
Views – 40,394
10 Lessons learnt from Building Machine Learning Systems
Neural Networks & Deep Learning
Deep Neural Networks
Views – 24,610
Neural Network and its Applications
Views – 59,550
Introduction to Artificial Neural Networks
Views – 43,554
Artificial Intelligence
Views – 40,180
Tutorial on Deep Learning
Views – 14730
Tutorials on Deep Learning and Applications
Views – Uploaded Recently
Deep Learning for Natural Language Processing
Views – Uploaded Recently
Hands on Deep Learning in Python
Data Science Competitions
Tips For Winning Data Science Competitions
Views – 9010
10 R Packages to Win Kaggle Competitions
Views – 53, 828
Not a presentation person? You can learn from YouTube videos, GitHub tutorials, data science books, reddit discussions, data science bootcamps and what not! Yet, here’s another surprise for you.
End Notes
I enjoyed curating this list and wanted to add more presentations to this list, but I realized that the list is getting too lengthy. Not to worry, I’ll surely add it in my next article. In this article, I have enlisted useful resources (presentations) in Machine Learning, Deep Learning, Neural Networks and Deep Learning.
This isn’t an exhaustive list. This list comprises of my most liked presentations. If you wish to recommend me any useful presentation on slideshare which I might have missed, feel free to share it with me in the comments section.
Did you find this article helpful? Please share your opinions / thoughts in the comments section below.
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Kunal is a post graduate from IIT Bombay in Aerospace Engineering. He has spent more than 10 years in field of Data Science. His work experience ranges from mature markets like UK to a developing market like India. During this period he has lead teams of various sizes and has worked on various tools like SAS, SPSS, Qlikview, R, Python and Matlab.
