Data Science Winter School: an introduction to data science.
The Data Science Winter School is a 2-week course (24 hours in total), organized in collaboration with the University of York. The course will provide an introduction to data science by covering the basic methods and practices of a data science project. The course makes use of lectures and practical assignments and is designed around the data science life cycle to show the techniques for handling a data science project. You will be exposed to basic programming skills in Python and learn how to select, clean, analyse, visualise and interpret data.
This course will make use of lectures and practical assignments organised as follows:
- Lecture: Introduction to data science.
- Practical: Introduction to programming in Python: This session will give you the basic skills to program in Python. By the end of this session, you will have gained familiarity with programming and will be able to perform simple data processing in Python.
- Lecture: Introduction to data, data manipulation and visualization.
- Practical: Data manipulation and visualization: During this session, the steps of data exploration, selection, cleaning and transformation will be performed via a hands-on assignment.
- Lecture: Introduction to Machine Learning.
- Practical Machine Learning: In this session, you will be ushown hands on examples on how to perform a Logistic Regression and how to use Naive Bayes Classifiers.
- Lecture: Responsible Data Science.
- Practical: Responsible Data Science: This session will cover how to apply the principles presented in the associated lecture to create data analysis processes that are well-structured, that can be replicated, and that treat sensitive data appropriately.
- Becoming familiar with the data science lifecycle
- Using Python as a programming language to perform data analysis tasks
- Becoming familiar with the data manipulation process and how to achieve this in Python
- Getting introduced to basic machine learning algorithms and in their application
- Understanding data interpretation and visualization tools
- Understanding responsibly principles in data science projects.
Course Duration and Dates
This is a two week course running from 11 January to 22 January, 2021. The courses will be scheduled in Central European Time [CET], so it is possible that some time slots are not ideal for people in certain time zones. However, when scheduling the courses, we will try to take into account the different time zones as much as we can.
The number of credits earned after successfully concluding this course is the equivalent of 3 ECTS according to Maastricht University’s guidelines. For further information, see the MSS terms and conditions
Dr. Visara Urovi
• Familiarity with datasets (e.g. in Excel)
• Being tech-savvy
The course is entirely self-contained. Slides and Python Notebooks will be provided. To make use of Python Notebooks, you will be instructed how to set up Python in your device.
• Work in groups
• Assignment and Attendance