LectureSlidesDataScienceChapter 3

Happily a whole host of models and algorithms can be

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problems when we express them mathematically. Happily, a whole host of models and algorithms can be used to classify and predict. A real challenge as a data scientist, once you’ve become familiar with how to implement them, is understanding which ones to use depending on the context of the problem and the underlying assumptions.

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Three Basic Algorithms This partially comes with experience you start seeing enough problems that you start thinking, “Ah, this is a classification problem with a binary outcome” or, “This is a classification problem, but oddly I don’t even have any labels” and you know what to do. In the first case, you could use logistic regression or Naive Bayes, and in the second you could start with k-means
Three Basic Algorithms kNN Linear Regression K-Means

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Chapter 4 Data Visualization
Introduction to Visualization Visual Representation of Data : For exploration, discovery, insight, .. Interactive component provides more insight as compared to a static image

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Type of Data Visualization Scientific Visualization Structural Data Seismic, Medical, .. Information Visualization No inherent structure News, stock market, top grossing movies, facebook connections Visual Analytics Use visualization to understand and synthesize large amounts of multimodal data audio, video, text, images, networks of people ..
Scientific Visualization Medical Metrological

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Visual Analytics Integration of interactive visualization with analysis techniques to answer a growing range of questions in science, business, and analysis. • Making sense of multimodal data -audio clips, video, photographs, transcripts, …

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Visual Analytics
88 Graph A network is a set of nodes connected by edges. A mathematical representation of network is Graph Mathematics vertices edges, arcs Computer science nodes links Physics sites bonds Sociology actors ties, relations G= (V,E)

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Types of Graph Directed Graph Un-Directed Graph
Graph processing Nodal Degree Degree Centrality Closeness Centrality

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Nodal Degree Presentaion title 91 Nodal Degree measures number of edges of the node in the network nodalDegree(node_i) = numberOfEdges(node_i)
Example 1 Nodal Degree in Undirected graph Nodes= 6 © 2014 MIMOS Berhad. All Rights Reserved. Node Nodal Degree Person 1 3 Person 2 3 Person 3 4 Person 4 4 Person 5 3 Person 6 1

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