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.
• Assessment

Goals

• 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.

ECTS
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

Coordinator
Dr. Visara Urovi

Prerequisites
• Familiarity with datasets (e.g. in Excel)
• Being tech-savvy

Recommended literature
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.

Teaching methods
• Assignments
• Lectures
• Skills
• Work in groups

Assessment methods
• Assignment and Attendance