The second of five courses where you learn how to select appropriate data types to represent a dataset in a C++ program.
Module
R for Data Science
Module information>
This module provides you with a wide range of applied data analysis techniques in R.
With its strong focus on practical application of the techniques, and learning by doing, you will gain confidence in your ability to select appropriate techniques for attacking a wide variety of data sources.
Topics covered
- Data sources: quantitative and qualitative
- Data sources: textual and mixed
- Planning of data gathering
- Interpretation of results
- Graphical displays and other outputs
- Practical implementation using the R statistical environment
- Visualization with R
- Decision making based on data analysis
- Data analysis case studies
- Correspondence analysis principal components analysis, cluster analysis
Credits
15 (150 hours)
Assessment
- Coursework item 1 (30%)
- Coursework item 2 (70%)
