DataScience - Revision history

GethinWilliams at 10:45, 5 November 2015

2015-11-05T10:45:45Z

2015-11-05T10:45:15Z

2015-11-05T10:44:17Z

2015-11-05T10:43:34Z

2015-01-05T13:49:43Z

Topics would include

2015-01-05T13:29:32Z

Introduction

2015-01-05T13:28:51Z

Introduction

2015-01-05T13:17:02Z

Introduction

2015-01-05T13:15:37Z

Introduction

2015-01-05T13:14:39Z

← Older revision		Revision as of 10:45, 5 November 2015
Line 3:		Line 3:


−	[[~~File~~:intro-to-data-science-nov15.pdf\|Intro to Data Science]]	+	[[Media:intro-to-data-science-nov15.pdf\|Intro to Data Science]]

	<!--		<!--

← Older revision		Revision as of 10:45, 5 November 2015
Line 3:		Line 3:


−	[[:File:intro-to-data-science-nov15.pdf\|Intro to Data Science]]	+	[[File:intro-to-data-science-nov15.pdf\|Intro to Data Science]]

	<!--		<!--

← Older revision		Revision as of 10:44, 5 November 2015
Line 3:		Line 3:


−	[[:File:intro-to-data-science-nov15.pdf]]	+	[[:File:intro-to-data-science-nov15.pdf\|Intro to Data Science]]

	<!--		<!--

@@ Line 2: / Line 2: @@
 '''What would a course on Data Science look like?'''
 =Introduction=
@@ Line 29: / Line 33: @@
 ** Testing.
 ** Scripting and automation.

@@ Line 24: / Line 24: @@
 * Programming Skills:
 ** "Clean code shows clarity of mind,"
 ** Version control.
 ** Build systems.
 ** Testing.
 ** Scripting and automation.

@@ Line 6: / Line 6: @@
 [[Image:Data_Science_VD.png|400px|thumbnail|center|Drew Conway's Venn diagram of data science]]
-Topics would include:
+=Topics would include=
 * What is relevant for the UoB?
 * y=f(x) relationships:- classifiers & regression

← Older revision		Revision as of 13:28, 5 January 2015
Line 5:		Line 5:

	[[Image:Data_Science_VD.png\|400px\|thumbnail\|center\|Drew Conway's Venn diagram of data science]]		[[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.

← Older revision		Revision as of 13:17, 5 January 2015
Line 4:		Line 4:
	=Introduction=		=Introduction=

−	[[Image:~~R-lm(cars)-abline~~.png\|400px\|thumbnail\|center\|~~linear regression~~ of ~~stopping distance against speed from the built-in~~ data ~~set, cars~~]]	+	[[Image:Data_Science_VD.png\|400px\|thumbnail\|center\|Drew Conway's Venn diagram of data science]]

← Older revision		Revision as of 13:15, 5 January 2015
Line 3:		Line 3:

	=Introduction=		=Introduction=
		+
		+	[[Image:R-lm(cars)-abline.png\|400px\|thumbnail\|center\|linear regression of stopping distance against speed from the built-in data set, cars]]

@@ Line 1: / Line 1: @@
 [[category:Pragmatic Programming]]
-'''Open Source Statistics with R'''
+'''What would a course on Data Science look like?'''
 =Introduction=