Advanced Topics in Data Analytics

Professors Maria Halkidi
Course category OPT/SDS
Course ID DS-532
Credits 5
Lecture hours 3 hours
Lab hours 2 hours
Digital resources View on Aristarchus (Open e-Class)

Learning Outcomes

The students after the successful completion of the course will be able:

  • to model and analyze data with appropriate analysis techniques, assess the quality of input
  • to choose the appropriate exploratory and/or inferential method for analyzing data, and interpret the results contextually.
  • to use supervised and unsupervised learning techniques for solving many analysis problems such as prediction, classification, segmentation.
  • to apply methods for the evaluation of the data analysis results.

Course Contents

  • Collection, preparation and representation of data for analysis
  • Linear, logistic regression
  • Classification Techniques (probabilistic classification, decision trees, support vector machines)
  • Predictive analytics and neural networks
  • Recommender systems
  • Graph analysis (applications on social networks)
  • Text mining – sentiment analysis
  • Evaluation of data analysis results

Recommended Readings

  • Mohammed J. Zaki, Wagner Meira Jr. (2018): Data Mining and Analysis Fundamental Concepts and Algorithms, Cambridge University Press.
  • Jure Leskovec, Anand Rajaraman, Jeffrey David Ullman (2014): Mining massive datasets, Cambridge University Press.
  • Top of Form.
  • Bottom of Form.