Advanced Topics in Data Analytics
|Lecture hours||3 hours|
|Lab hours||2 hours|
|Digital resources||View on Aristarchus (Open e-Class)|
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.
- 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
- 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.