Advanced Topics in Data Analytics

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.