Avoiding a Common Mistake with Time Series
A basic mantra in statistics and data science is correlation is not causation, meaning that just because two things appear to be related to each other doesn’t mean that one causes the other. This is a lesson worth learning.

5 Must-Have Marketing Technologies to Drive New Business
1) It All Begins with the Data
2) Get Faster Access to Bigger Data
3) Analyze This and Visualize That
4) Onboard to Enhance Your Digital Marketing
5) It’s Time to Get Personal

Deep learning – Introduction
Slides available at: https://www.cs.ox.ac.uk/people/nando…. Course taught in 2015 at the University of Oxford by Nando de Freitas with great help from Brendan Shillingford.

Surviving Data Science “at the Speed of Hype”
There is this idea endemic to the marketing of data science that big data analysis can happen quickly, supporting an innovative and rapidly changing company. But in my experience and in the experience of many of the analysts I know, this marketing idea bears little resemblance to reality.

Similarity in the Wild
Finding similarity across observations is one of the most common tasks/projects which a data scientist does. Collaborative Filtering purely depends on finding similar items(videos for Netflix, products for Amazon) for users. If you are doing a classification task with KNN(K Nearest Neighbor), you are classifying the new observations purely on the distance that you have in the training set. Most of the instance based learning algorithms in one way or another is built on the similarity distances of observations. Clustering algorithms (k-means, manifold learning) depends on the distance between observations.

10 Steps to Big Data Success
1. Data Governance – Inside and Out
2. Big Data Roles
3. Who Owns it?
4. Flex Up for Big Results
5. Include Unstructured Content Management
6. Business Value – View From the Middle
7. Set the Dial on Precision
8. Be Purely Practical
9. Formalize the Hand-Off
10. Overseeing Evolves