Data and Visualization
Course code
IFI6256.DT
old course code
Course title in Estonian
Andmed ja visualiseerimine
Course title in English
Data and Visualization
ECTS credits
6.0
Assessment form
Examination
lecturer of 2024/2025 Autumn semester
Not opened for teaching. Click the study programme link below to see the nominal division schedule.
lecturer of 2024/2025 Spring semester
Not opened for teaching. Click the study programme link below to see the nominal division schedule.
Course aims
Develop an in-depth understanding and skills in methods and techniques for the analysis and visualization of complex data sets and their application in different fields. Develop a critical understanding of predictive modeling, data mining and ethical considerations for evaluating and implementing data-driven solutions.
Brief description of the course
Data analysis and data mining techniques. Classification. Cluster analysis and segmentation. Deep learning and neural networks. Natural Language Processing and techniques. Sentiment analysis. Topic modeling. Named entity recognition. Text summarisation. Time series analysis. Anomaly detection. Data visualization. Interactive dashboards and reports. Graphical techniques, heatmaps, idea and concept maps, radial diagrams. Geospatial visualization and GIS. Dynamic and animated visualizations. Data storytelling. Big data analytics and technologies. Data Warehouses and Data Lakes. Social Network Analysis. Distributed computing for big data analytics. Unstructured data analysis techniques. Convolutional and Recurrent Neural Networks. Machine learning and predictive analytics. Supervised and unsupervised learning. Model evaluation and validation. Machine learning methods and model optimisation. Deployment of predictive models. Ethics of data analysis. Data protection and security. Ethical considerations in data use. Regulatory frameworks. Responsible use of AI. Applied data analytics projects. Case studies and real-world applications. Collaborative projects. Data analysis projects, encompassing the entire data analysis process, from data collection to visualization.
Learning outcomes in the course
Upon completing the course the student:
- has knowledge of various methods and techniques of data analysis and analytics;
- can apply data analytics methods and techniques to complex data sets;
- can use data visualization tools to effectively communicate the results of data analytics;
- understands how to develop and validate prediction models using machine learning algorithms;
- critically evaluate ethical and governance issues in data analytics;
- can execute and manage data analysis projects.
- has knowledge of various methods and techniques of data analysis and analytics;
- can apply data analytics methods and techniques to complex data sets;
- can use data visualization tools to effectively communicate the results of data analytics;
- understands how to develop and validate prediction models using machine learning algorithms;
- critically evaluate ethical and governance issues in data analytics;
- can execute and manage data analysis projects.
Teacher
Sirje Virkus
Study programmes containing that course