The ultimate goal First lets discuss what the goal is. What is the goal? The goal is to make a decision or a prediction Based upon what? Information How can we improve the quality of the decision or prediction? The quality of the solution is defined by the certainty represented by the information. Think about this for a moment. It’s a key insight. Think about your projects. Your research. The decisions you make.
Read moreProblems in Data Science Understanding the problem “the five-whys” Different questions dramatically effect the tools and techniques used to solve the problem. Data Science as a Process More Science than Engineering High risk High reward Difficult Unpredictable By Kenneth Jensen CC BY-SA 3.0, via Wikimedia Commons Impacts of Data Science What is the purpose of the project? Who is affected? Which parts of the business are affected? Do we need help?
Read moreWhat is Data Science? Software Engineering, Maths, Automation, Data A.k.a: Machine Learning, AI, Big Data, etc. It’s current rise in popularity is due to more data and more computing power. For more information: https://winderresearch.com/what-is-data-science/ Examples US Supermarket Giants Target: Optimising Marketing using customer spending data. Walmart: Predicting demand ahead of a natural disaster. Discovery Most projects are “Discovery Projects”. Primary Business goals: Increase Revenue, save costs, save time. Budgets can come from other parts of the business.
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Testing Model Robustness with Jitter Welcome! This workshop is from Winder.ai. Sign up to receive more free workshops, training and videos. To test whether your models are robust to changes, one simple test is to add some noise to the test data. When we alter the magnitude of the noise, we can infer how well the model will perform with new data and different sources of noise. In this example we’re going to add some random, normally-distributed noise, but it doesn’t have to be normally distributed!
Read moreQuantitative Model Evaluation Welcome! This workshop is from Winder.ai. Sign up to receive more free workshops, training and videos. We need to be able to compare models for a range of tasks. The most common use case is to decide whether changes to your model improve performance. Typically we want to visualise this, and we will in another workshop, but first we need to establish some quantitative measures of performance.
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Qualitative Model Evaluation - Visualising Performance Welcome! This workshop is from Winder.ai. Sign up to receive more free workshops, training and videos. Being able to evaluate models numerically is really important for optimisation tasks. However, performing a visual evaluation provides two main benefits: Easier to spot mistakes Easier to explain to other people It is so easy to miss a gross error when looking at summary statistics alone. Always visualise your data/results!
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Hierarchical Clustering - Agglomerative Clustering Welcome! This workshop is from Winder.ai. Sign up to receive more free workshops, training and videos. Clustering is an unsupervised task. In other words, we don’t have any labels or targets. This is common when you receive questions like “what can we do with this data?” or “can you tell me the characteristics of this data?”. There are quite a few different ways of performing clustering, but one way is to form clusters hierarchically.
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Evidence, Probabilities and Naive Bayes Welcome! This workshop is from Winder.ai. Sign up to receive more free workshops, training and videos. Bayes rule is one of the most useful parts of statistics. It allows us to estimate probabilities that would otherwise be impossible. In this worksheet we look at bayes at a basic level, then try a naive classifier. Bayes Rule For more intuition about Bayes Rule, make sure you check out the training.
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Detrending Seasonal Data Welcome! This workshop is from Winder.ai. Sign up to receive more free workshops, training and videos. statsmodels is a comprehensive library for time series data analysis. And it has a really neat set of functions to detrend data. So if you see that your features have any trends that are time-dependent, then give this a try. It’s essentially fitting the multiplicative model: $y(t) = Level * Trend * Seasonality * Noise$
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Visualising Underfitting and Overfitting in High Dimensional Data Welcome! This workshop is from Winder.ai. Sign up to receive more free workshops, training and videos. In the previous workshop we plotted the decision boundary for under and overfitting classifiers. This is great, but very often it is impossible to visualise the data, usually because there are too many dimensions in the dataset. In thise case we need to visualise performance in another way.
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