Course details

Learning outcomes

Objective

The course aims to introduce students to the basic concepts comprising big data analytics, i.e. characteristics of big data applications, contemporary big data architectures, exploration and visualization of big data, knowledge extraction from big data.

Knowledge

Upon completion of the course, graduate students will be familiar with:

  • Big data challenges and advantages
  • Popular big data domains
  • Big data analysis tasks
  • Machine learning techniques used for big data analytics problems
Skills

The course participants upon completion will be able to:

  • Query big data infrastructures
  • Visualize and (pre)process big data collections
  • Understand the fundamental machine learning techniques and algorithms
  • Apply machine learning techniques (classification, clustering, regression analysis, outlier/deviation detection) to pilot problems
  • Select the most efficient algorithm, based on problem requirements
  • Design the methodology for big data analysis problems of medium complexity

Course contents

  1. Introduction to Big data analytics: Definitions Examples Application areas
  2. Modeling big data – Big data management architectures
  3. Data exploration/visualization
  4. Data Preparation and Preprocessing
  5. Machine learning techniques (Part 0): Model evaluation
  6. Machine learning techniques (Part I): Classification, Overview Definitions Algorithms
  7. Machine learning techniques (Part II): Clustering, Overview Definitions Algorithms
  8. Machine learning techniques (Part III): Regression, Overview Definitions Algorithms
  9. Machine learning techniques (Part IV): Outlier/deviation detection, Overview Definitions Algorithms
  10. Machine learning techniques (Part IV): Ensemble methods, meta-learning
  11. Automated machine learning: Overview, techniques, hyper-parameter optimization

Recommended bibliography

  1. Introduction to Data mining, P. Tan, M. Steinbach & V. Kumar, Addison Wesley, 2005.
  2. Data Mining; Concepts and Techniques, 2nd edition, J. Han and M. Kamber, Morgan Kaufmann, 2006.