DAT110 Introduction to Data Analysis and Visualisation

Introduction to elementary data analysis based on modern tools

- Datatypes and loading of data from various file formats.

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

- Some simple 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).

- Smoothing of time-dependent data.

- Cluster analysis (k-means, hierarchical clustering etc.).

Skills and insight into basic data analysis techniques based on modern data collection. In addition to these, focus will be give to work relevant soft skill such as team work and presentation skills.

The student will learn to choose appropriate methods for

1) Explorative data analysis (plotting/visualisation and descriptive statistics),

2) Visualisation,

3) Inference,

4) Modelling and prediction with continous and categorical responses (regression and classification) and validation of predictive models.

5) Introduction to cluster analysis and smoothing of time series data.