The course aims to facilitate data driven decision making by giving students fundamental knowledge about data gathering, data structures, analysis, and visualization. The course will cover the following topics:

• Common data structures (flat and relational databases including basic SQL)
• Data analysis tools and interfaces (R, RStudio and RMarkdown)
• Data gathering, preparation and exploration ("data wrangling")
• Data aggregation and feature engineering
• Data and model visualization (ggplot)
• Data modeling

Knowledge:

• Understand the properties of raw data structures and their implications for the use of data analysis techniques
• Know common database structures and their implication for data management and data extraction
• Know important techniques for data preparation, transformation, aggregation, and exploration ("data wrangling")
• Understand how these pre-analysis choices affect analysis and interpretation of results

Skills:

• Have basic skills in R and be able to work with these in an appropriate interface (e.g., RStudio or RMarkdown)
• Be able to take "messy" raw data and prepare it for analysis
• Be able to perform basic feature engineering tasks, e.g., variable selection, transformation, and aggregation
• Be able to create informative data analysis visualizations
• Create reproducible reports using, e.g., RMarkdown

General competence:

• Effectively communicate results using text, tables, and visualizations
• Understand what compromises may be necessary in the data analysis process from raw data to discussion of results and how these may affect, or bias, the analysis