|Subject Area||Applications and Foundations of Computer Science|
|Semester||Semester 7 – Fall|
Panagiota Tsompanopoulou, Associate Professor
The course includes an introduction to programming environments and algorithms for machine learning. Emphasis is placed on environments Excel, Python and R and data mining environments, Orange, Rapidminer and Weka. The course introduces statistical machine learning techniques, categorization and regression (linear regression, nonlinear regression, decision trees), artificial neural networks, Support Vector Machines, data mining techniques (classification, clustering, and association), and applications in large amounts of unstructured data for business analytics and sentiment analysis and opinion mining.
The area of Data Science is designed to extract knowledge from large volumes of data. The science of data makes extensive use of algorithms, machine learning and statistical inference for extracting knowledge and predictions. Science is an interdisciplinary area that resulted from the combination of a) significant developments in numerical analysis, algorithms and machine learning techniques based on statistical principles and(b) the rapid developments in the area of management and processing of heterogeneous, continuously changed large volume of data (Big Data). There is a strong scientific and business interest in data scientists.
This course provides the student an introduction a) in learning technique for analyzing large volume of data from business applications and social networks and b) in problem solving environments Excel, Python, R, orange, rapidminer, weka for solving problems with data mining techniques.