# Data Analysis – MoBi Bachelor – WS 2019/2020¶

*Carl Herrmann carl.herrmann (at) bioquant.uni-heidelberg.de*

The purpose of this course is an introduction to basic techniques of scientific data analysis, which every scientist will be confronted with. We will cover:

data visualization (plots)

descriptive statistics (mean, variance, correlation,…)

data exploration and reduction (PCA, clustering,…)

inference statistics (P-values, tests,…)

data modelling (linear/logistic regression,…)

Each lecture (Thursday) will be followed by a practical session (Friday) under R/RStudio

## Reference¶

**Books:**

Intuitive Biostatistics von Harvey Motulsky (Oxford University Press)

Discovering statistics using R von A. Field, J. Miles, Z. Field (SAGE publications)

**Websites:**

## Practical information¶

## Lecture slides¶

`Part 1 (18.10 - )`

: Intro, plots, descriptive statistics`Part 2 (25.10 - )`

: Clustering, PCA`Part 3 (15.11 - )`

: Distribution, statistical inference`Part 4 (22.11 - )`

: Hypothesis testing, t-tests`Part 5 (13.12 - )`

: Proportion test, multiple testing correction`Part 6 (20.12 - )`

: Linear regression

## Link to practical parts¶

## Exercises¶

This is a link to a bundle of exercise sheets from the previous years, together with solutions for most of them. This will help to prepare for the exam

## Shiny applets¶

Shiny makes it possible to build interactive R scripts that can be used using user interface. No programming skills are needed!

For some topics of the lecture, we provide Shiny applets to illustrate some concepts, (hopefully) helping in the understanding:

Topic 1 : QQ Plots

Topic 2 : Statistical inference

Topic 3 : Distributions

Topic 4 : Confidence intervals

Topic 5 : Power of a test