Revision as of 13:28, 5 January 2015

What would a course on Data Science look like?

Introduction

Drew Conway's Venn diagram of data science

Topics would include:

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,"
- Version control.
- Build systems.
- Testing.
- Scripting and automation.

@@ Line 5: / Line 5: @@
 [[Image:Data_Science_VD.png|400px|thumbnail|center|Drew Conway's Venn diagram of data science]]
+Topics would include:
+* 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,"
+** Version control.
+** Build systems.
+** Testing.
+** Scripting and automation.