```{r, include = F}
knitr::opts_chunk$set(echo = F, comment = NA)
```
Introduction to Open Data Science
========================================================
incremental: false
autosize: true
transition: rotate
University of Helsinki, spring 2017
- Tuomo Nieminen and Emma Kämäräinen with
- Adjunct professor Kimmo Vehkalahti
Powered by Rpresentation. The code for this presentation is [here](
https://raw.githubusercontent.com/TuomoNieminen/Helsinki-Open-Data-Science/master/docs/index.Rpres)
From data wrangling to exploration and modelling
========================================================
type: prompt
```{r, fig.align = "center", fig.width = 9}
library(ggplot2); library(ggExtra)
learning2014 <- read.table("http://www.helsinki.fi/~kvehkala/JYTmooc/learning2014.txt", sep = "\t", header = TRUE)
df <- learning2014[learning2014$points > 0,]
theme_set(theme_grey(base_size = 18))
p <- qplot(attitude, points, col = gender, data = df) + geom_smooth(method = "lm")
ggMarginal(p,type="histogram", bins=15, colour="white")
```
Contents
========================================================
type: prompt
1. Regression and model validation
2. Logistic regression
3. Clustering and classification
4. Dimensionality reduction techniques
```{r child ="regression.md"}
```
```{r child = "logistic_regression.md"}
```
```{r child = "cluster_classification.md"}
```
```{r child = "dimensionality_reduction.md"}
```
========================================================
type: prompt