Exploratory Data Analysis and Visualization

Instructors: Dr. Magnisalis, Dr. Baltagiannis
Teaching Hours and Credit Allocation: 30 Hours, 6 Credits
Course Assessment: Exam & Coursework

 

Aims

The course covers the essential exploratory techniques for analyzing and summarizing data, with the aid of visualization techniques.  Students will learn how they can determine the underlying structure and model variables in a problem by examining the data itself, which can provide valuable insight about the problem itself and also suggest the type of formal statistical analysis to be subsequently applied. Visualization and plotting of high-dimensional data will also be studied.

 

Learning Outcomes

On completing the course, students will be able to:

  • Understand the essential exploratory techniques for summarizing data.
  • Understand and use the plotting systems in Python and/or R
  • Understand basic principles of constructing data graphics.
  • Understand and use common multivariate statistical techniques to visualize high-dimensional data

 

Content

  • Fundamentals of data visualization.
  • Constructing data graphics.
  • Analytic graphics.
  • Visualizing high dimensional data.
  • Statistical methods for exploratory analysis.
  • Essential exploratory techniques for summarizing data.
  • Dimension Reduction.
  • Perception of discrete and continuous variables.
  • Dynamic graphics.
  • Model visualization.

 

Reading

  • Unwin, A. (2015), Graphical data analysis with R, CRC Press.
  • W.L. Martinez, A.R. Martinez, J. Solka (2010), Exploratory Data Analysis with MATLAB, 2nd Ed., CRC Press.
  • D.C. Hoaglin, F. Mosteller, J.W. Tukey (2000), Understanding Robust and Exploratory Data Analysis, 1st Ed., Wiley.  
  • J. W. Tukey (1977), Exploratory Data Analysis, Pearson.