DAT110 Introduction to Data Analysis and Visualisation

Introduction to elementary data analysis based on modern tools

Syllabus - including programming of all analysis methodology:

- Datatypes and loading of data from various file formats.

- Visualization and explorative analysis for identification of structure and trends (histograms, scatterplots, box-plots etc.).

- Fundamental statistics (mean, median, variance etc.).

- Correlation and covariance (of single variables and matrix data).

- Crosstables.

- Elementary normal distribution theory, normalizing transformations and testing for normality.

- Geometric distributions, binomial distributions, Poisson-distributions.

- Inference (parametrical and non-parametrical for investigation of one and two samples) and simple analysis of variance.

- Least squares modelling (linear and polynomial fit).

- Logistic regression (classification with two groups).

Demonstrations:

- Cluster analysis (k-means etc.).

Skills and insight into basic statistical techniques for data analysis.

Students learn about appropriate analysis methods for

1) Exploratory data analysis (plotting/visualization and simple descriptive statistical measures),

2) Visualization,

3) Inference,

4) Modeling and prediction with continuous and categorical responses (regression (simple and multiple) and classification) and validation of predictive models.

5) Demonstration of cluster analysis.