The repository of the course to share example codes and materials of lectures.
Course Description
This course will provide an introduction to the field of Data Science, with applications to statistical testing, explainable AI and sustainability related data (examples of spatial data, citizen science data). This course provides connections with other courses on Data Science course, Statistics, Research Methods, Citizen Science and Exploring Sustainability and Artificial Intelligence.
The course will first cover the basics of data cleaning, analysis, visualization and statistical testing. The aim of this first part is to build intuition working with data (notion of dataframe), in particular in the context of testing for the significance of associations between variables. This will be of particular importance to the personal research project that involves the collection of quantitative data. In the second part we will speak about the foundations of AI, which will play an important role for other courses further and will help students to orient better in numerical methods for analysis being created nowadays. Why focus on the foundation of AI, explainable data analysis and network data? Over the past decade, developing explainable AI methods became an important milestone in science and technologies. In the foundations of AI we will speak about the Network studies, which have had significant impact in disciplines as varied as mathematics, sociology, physics, biology, computer science or quantitative geography, giving birth to Network Science as a field of itself. With the recent rise of social networks in the last decade, their use has now become widespread in the digital world. We will provide theoretical foundations of the field of Network Science and Embeddings, which are both widely used for processing the data and development of algorithms. In the practical hands-on part we will speak about analysis and visualization of real-world data.
Course objective (Pedagogical objective) At the end of the course the students will have gained intuition to analyze real-world data and get introduction to some AI methods. They will be able to use Python for statistical analyses and working with data. They will know practical tools and packages to work with sustainability data, as well as network visualization tools. Finally, they will have obtained good practices for code and data management.
Syllabus and Agenda:
18th September: Morning: Elements of statistics for data analysis: building intuition with a dataset) Afternoon: Introduction to data science, network science 20th September Morning: Foundations on AI for data science: from theory to practice on data fitting, embedding, modeling Afternoon: Spatial data analysis, Data and Network visualization