Category: Learn Data Science
-
Big Data & Analytics Summit Canada 2016
Early next year (February 2016 to be exact), Canada’s first Cross-Industry Big Data Summit will be held in Toronto. The speaker lineup looks strong with presenters from Twitter, Rogers, Boeing and more. Also, the conference contains both a technical and a business track. When: February 17th & 18th 2016 Where: Sheraton Centre Toronto More Info:…
-
The Data Science Industry: Who Does What
The fine folks at DataCamp, a great site for learning data science right in your browser, have come up with another great infographic. This time it compares some of the many job titles in the data science field. The infographic lays out the roles and skills needed for the following job titles. Note: not all…
-
Want a Quick Jupyter Notebook?
If you have been hearing about Jupyter (formerly iPython) and have not tried it out, here are a couple quick, free, and easy options for giving it a try. No installation need, and no account setup. Just visit a link. Easy Jupyter Notebook Try Jupyter and tmpnb are two projects for instantly getting a jupyter…
-
Dat – Version Controlled Data
Dat is an open source project focusing on data storage. In particular, the project wants to version control data. What is version control? In short it allows for tracking of history associated with something (typically source code files or documents). Dat takes the idea a bit further, and the data is versioned at the row…
-
Building a Data Science Capability from Booz Allen
Tips for Building a Data Science Capability (PDF) – some excellent tips to help create a data-driven organization While you are already visiting the Booz Allen website, a few years back they published a great resource title: The Field Guide to Data Science.
-
An executive’s guide to machine learning | McKinsey & Company
via An executive’s guide to machine learning | McKinsey & Company. A nice read if you are looking for a short introduction to the history and importance of machine learning.
-
Understanding Machine Learning: From Theory to Algorithms (Free Book Download)
Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-Shwartz, Associate Professor at the School of Computer Science and Engineering at The Hebrew University, Israel, and Shai Ben-David, Professor in the School of Computer Science at the University of Waterloo, Canada. The book looks very thorough. Below is just a sampling of the topics covered.…
-
Software Engineering Podcasts for Data Science
If you are a former software engineer looking to gain some data science skills, here are a list of podcasts that will most likely interest you. Software Engineering Daily A nice podcast which just ran a series of podcasts about data science. Data Science Overview with Yad Faeq Applied Data Science with Edwin Chen Kaggle…
-
Statistics for Hackers Slides by Jake VanderPlas
Jake provides an excellent slidedeck about using programming simulation to do statistics. Lots of great information packed into these slides. Slides are available at Statistics for Hackers on SpeakerDeck
-
Why Data Science? – Presentation
Recently, I was invited to speak about data science to the research department of a regional hospital system. I thought I would share my slides. A clarification note on one of my quotes from the presentation, “Data Science doesn’t need big data” I am not trying to say big data is not important. I am…
-
One Algorithm To Learn Anything [An Interview with Pedro Domingos, Author of The Master Algorithm]
Releasing today (Sept. 22, 2015) is the fantastic new book, The Master Algorithm, by machine learning expert and University of Washington Computer Science Professor, Pedro Domingos. Recently, I got the opportunity to visit with Dr. Domingos about his new book and machine learning in general. See below for his fears of machine learning, thoughts on…