Difference between revisions of "DataScience"
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(Created page with 'category:Pragmatic Programming '''Open Source Statistics with R''' =Introduction=') |
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[[category:Pragmatic Programming]] | [[category:Pragmatic Programming]] | ||
| − | ''' | + | '''What would a course on Data Science look like?''' |
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| + | [[Media:intro-to-data-science-nov15.pdf|Intro to Data Science]] | ||
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| + | <!-- | ||
=Introduction= | =Introduction= | ||
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| + | [[Image:Data_Science_VD.png|400px|thumbnail|center|Drew Conway's Venn diagram of data science]] | ||
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| + | =Topics would include= | ||
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| + | * What is relevant for the UoB? | ||
| + | * y=f(x) relationships:- classifiers & regression | ||
| + | ** Examples: Linear & logistic regression, K-Nearest Neighbours, Decision Trees, Neural Networks etc. | ||
| + | * Data topics: | ||
| + | ** Training, Test & validation data. | ||
| + | ** Sources of data, e.g. web scraping. | ||
| + | ** Exploratory Data Analysis (EDA). | ||
| + | ** Cleaning & munging data (90% of your effort?). Useful Linux tools. | ||
| + | ** Feature selection. | ||
| + | * Model selection & training topics: | ||
| + | ** Algorithms that scale. | ||
| + | ** Supervised vs. Unsupervised training. | ||
| + | ** Overfitting. | ||
| + | ** The curse of dimensionality. | ||
| + | * Programming Skills: | ||
| + | ** "Clean code shows clarity of mind," | ||
| + | ** Languages: R? Python? Others? | ||
| + | ** Version control. | ||
| + | ** Build systems. | ||
| + | ** Testing. | ||
| + | ** Scripting and automation. | ||
| + | --> | ||