Data science is a dynamic and fast-growing interdisciplinary research field. Industry, governments, and academia operate on large amounts of data. But how do we deal with such large amounts of data? Is there a general framework to gather, analyze, model and visualize the data? What techniques do we use? What are the legal and ethical aspects regarding these datasets? This course will introduce methods for a number of key aspects of data science: data gathering, data analysis, data visualization and ethical and privacy issues. During the course, you work in a small team of students on a series of projects that bind together all elements of the data science process; from formulating a research question, gathering data, exploring the data, modeling the data and communicating and visualizing the results. We will be using Python for all programming assignments and projects.
• The student can explain the main stages and common challenges in the data science process
• The student understands the foundations of gathering data for data science projects
• The student can evaluate and interpret the outcomes of the data science process.
• The students are able to formulate a right research question corresponding to a recognized business, scientific, or societal need.