Course description:
This course on data mining and visualization cover methodology and applications in this field. By introducing principal ideas in statistical learning, the course will help students to understand methods in data mining and computational aspects of algorithm implementation. The course also explores the question of what visualization is, and why one should use visualizations for quantitative data. Students are required to work on projects to practice applying existing software and to a certain extent, developing their own algorithms. Classes will be provided in three forms: lecture, project discussion, and special topic survey. Project discussion will enable students to share and compare ideas with each other and to receive specific guidance from the instructors. By surveying special topics, students will be exposed to literature and become more aware of recent research. In particular, basics for classification and clustering, e.g., linear classification methods, prototype methods, decision trees, and hidden Markov models, will be introduced. Five course lab sessions will be included with emphasis on understanding and using existing learning algorithms. Lab sessions will focus on providing practice using real-world data.

A general introduction and overview of the subject is given in the following series of slides, while the official course description is given in here .

Learning outcomes:

• On a general level the students should be able to understand and identify the challenges in analyzing massive data.
• They will learn to select a proper data mining algorithm for extracting meaningful information from such data.
• The student will also become familiar with a wide range of visualizations techniques from which he/she can select to properly present both to specialists and to general public results of performed data mining.

Textbooks:

• PRIMARY - An Introduction to Statistical Learning with Applications in R , by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani, or, on slightly higher level and with several additional topics, The Elements of Statistical Learning , by Trevor Hastie, Robert Tibshirani, and Jerome Friedman, Springer 2009, 2nd Edition.
• SUPPORTING -
  • Data Mining and Business Analytics with R , by Johannes Ledolter, Wiley 2013.
  • Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data, Bruce Ratner, Hoboken : CRC Press, 2011
  • Principles of Data Mining, by H. Mannila, P. Smyth and D. J. Hand;
  • Data Mining: Concepts and Techniques, by J. Han and M. Kamber.
  • Data Mining with R. Learning with Case Studies , by Luis Torgo, CRC Press 2011.

Supporting computer package:
All projects and exercises can be completed using

• Statistical R-package available for free download

• KDNuggets
• Elegant notebook interface for R and Python (Jupyter)

• Krzysztof Podgorski , Department of Statistics, e-mail: Krzysztof.Podgorski@stat.lu.se Office Hours: Monday 16:00-17:00 or by appointment. Course evaluation form/letter

Comprehensive Exam: The work throughout the course will be compounded into one comprehensive examination paper. It will comprise three parts:

1. Take-home Assignments;
2. In-class Computer Projects;
3. Oral Presentations.

The final grades will be assigned according to the following table: