|Study location||Netherlands, Maastricht|
|Type||Summer Course, full-time|
|Nominal duration||2 week (4 ECTS)|
|Tuition fee||€799 per programme|
Enrolled as an Undergraduate student or Undergraduate diploma
Familiarity with datasets (e.g., in Excel) and student should be Tech-savvy
The entry qualification documents are accepted in the following languages: English.
Often you can get a suitable transcript from your school. If this is not the case, you will need official translations along with verified copies of the original.
This course will provide an introduction to data science by covering the basics methods and practices of a data science project. The course is designed around the data science lifecycle 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.
By following this course you will have knowledge and insight into: i) differentiating the steps of the data science life cycle; ii) formulating a data research question iii) using Python as the programming language, how data can be analysed; iv) know how basic data analysis algorithms work; v) draw conclusions with regard to the data question/hypothesis by interpreting the 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 the student 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 students will be shown 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.
Course Duration and Dates
This is a two week course running from the 14th of June until the 25th of June, 2021. The assignment will take place on Monday 28 and Tuesday 29 June. The course will take place online.
The number of credits earned after successfully concluding this course is the equivalent of 4 ECTS according to Maastricht University’s guidelines. For further information see the MSS terms and conditions
• Getting 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.
Dr. Visara Urovi together with: Dr. Linda Rieswijk, Thales Bertaglia and Dr Alfonso de la Vega.
• 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