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Data Analysis Computerphile NxYEzbbpk 4

Data Analysis Computerphile NxYEzbbpk 4 - Biography & Analysis

Big Data does not equate to Big Knowledge - unless you use A litre of fuel but a pint of milk - time to get all your Seeing is believing - Dr Mike Pound helps us understand how to turn our datapoints into Powerpoints. This is part 2 of the Too much data? Dr Mike Pound on how best to reduce your dataset. This is part 5 of the What is data? Dr Mike Pound begins to formalise this much used word. This is part 1 of the Grouping similar things together - either users with similar habits, or products in an online shop. Dr Mike Pound on Clustering.

A clean sweep. How to get rid of the unnecessary clutter in your How do computers represent multi-dimensional Following a look at 'Sensemaking' Associate Professor Dr Kai Xu delves into some more tricks of the visualisation trade. Principal Component Analysis, is one of the most useful

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Visual Gallery

Data Analysis 0: Introduction to Data Analysis - Computerphile

Data Analysis 4: Data Transformation - Computerphile

Data Analysis 6: Principal Component Analysis (PCA) - Computerphile

Data Analysis 2: Data Visualisation - Computerphile

Data Analysis 5: Data Reduction - Computerphile

Data Analysis 1: What is Data? - Computerphile

Data Analysis 8: Classifying Data - Computerphile

Data Analysis 7: Clustering - Computerphile

Data Analysis 3: Cleaning Data - Computerphile

Multi-Dimensional Data (as used in Tensors) - Computerphile

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