Root Cause Analysis: The 5-Whys

Published: Nov 11, 2017
Author

Root Cause Analysis: The 5-Whys

Deciding what problem you should try and solve is one of the hardest steps to get right in Data Science. If you get it wrong, then you’ll spend significant amounts of time free wheeling around the rest of the data science process and end up with something that nobody wants or cares about. There is nothing worse that someone suggesting that your work has no value. The solution is to get the correct problem defined at the start.

Published: Oct 29, 2017
Author

Probability Distributions

Welcome! This workshop is from Winder.ai. Sign up to receive more free workshops, training and videos.

This workshop is about another way of presenting data. We can plot how frequent observations are to better characterise the data.

Imagine you had some data. For sake of example, imagine that is a measure of peoples’ height. If you measured 10 people, then you would see 10 different heights. The heights are said to be distributed along the height axis.

Published: Oct 27, 2017
Author

Why do we use Standard Deviation and is it Right?

It’s a fundamental question and it has knock on effects for all algorithms used within data science. But what is interesting is that there is a history. People haven’t always used variance and standard deviation as the defacto measure of spread. But first, what is it?

Standard Deviation

The Standard Deviation is used throughout statistics and data science as a measure of “spread” or “dispersion” of a feature. The standard deviation of a population is:

Published: Oct 27, 2017
Author

Mean and Standard Deviation

Welcome! This workshop is from Winder.ai. Sign up to receive more free workshops, training and videos.

This workshop is about two fundamental measures of data. I want to you start thinking about how you can best describe or summarise data. How can we best take a set of data and describe that data in as few variables as possible? These are called summary statistics because they summarise statistical data. In other words, this is your first model!

Published: Oct 16, 2017
Author

Slides

Abstract

Have you ever wondered which parts of your job you love or hate? Chances are that like most developers you love learning and new problems to solve. You hate monotony and bureaucracy.

Published: Oct 6, 2017
Author: Dr. Phil Winder
CEO

If you ask anyone what they think AI is, they’re probably going to talk about sci-fi. We sometimes even need to set the record straight during our AI consulting projects. Science fiction has been greatly influenced by the field of artificial intelligence, or A.I.

Probably the two most famous books about A.I. are I, Robot, released in 1950 by Isaac Asimov and 2001: A Space Odyssy, released in 1968 by Arthur C. Clarke.

I, Robot introduced the three laws of robotics. 1) A robot must not injure a human being, 2) a robot must obay the orders, except where the orders would conflict with the First Law and 3) a robot must protect its own existance as long as such protection does not conflict with the First or Second Laws.

2001: A Space Odyssey is a story about a psychopathic A.I. called HAL 9000 that intentionally tries to kill the humans on board a space station to save it’s own skin, in a sense.

But the history of AI stems back much further…

Published: Oct 3, 2017
Author

Slides

Abstract

The Hitchhiker’s Guide says the meaning of life is 42. Considering that the field of Data Science is going through a period of exponential growth it too could soon find that the meaning of an artificial life is also 42. But if you are not involved on a day-to-day basis, the expansion can seem bewildering. The story of how disparate disciplines have combined to produce Data Science is fascinating.

Published: Jul 15, 2017
Author: Dr. Phil Winder
CEO

Data Science is an emerging field that is plagued by lurid, often inconsequential reports of success. The press has been all too happy to predict the future demise of the human race.

But sifting through chaff, we do see some genuinely interesting reports of work that affects both bottom-line profit and top-line revenue.

Published: Jun 21, 2017
Author: Dr. Phil Winder
CEO

Cloud-Native, a collection of tools and best practices, disrupts the ideas behind traditional software development. I am a firm believer of the core concepts, which include visibility, repeatability, resiliency and robustness.

The idea begins in 2015 when the Linux Foundation formed the Cloud-Native Computing Foundation. The idea was to collect the tools and processes that are often employed to develop cloud-based software.

However, the result was a collection of best practices which extend well beyond the realms of the cloud. This post introduces the essential components: DevOps, continuous delivery, microservices and containers.

Published: Jun 12, 2017
Author

Abstract

The proliferation of Data Science is largely due to: ubiquitous data, increasing computational power and industry acceptance that solutions are an asset.

Data Science applications are no longer a simple dataset on a single laptop. In a recent project, we help develop a novel cloud-native machine learning service. It is unique in that problems are packaged as containers and submitted to the cloud for processing. This enables users to distribute and scale their models easily.

AI, Machine Learning, Reinforcement Learning, and MLOps Articles