Introduction to Data Science
Netherlands, Maastricht
| Study location | Netherlands, Maastricht |
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| Type | Summer Courses, Full-time |
| Nominal duration | 2 weeks (3 ECTS) |
| Study language | English |
| Course code | MSS2001 |
| Tuition fee | €899 one-time 720€ for students of Maastricht University and ‘‘Women in Data Science’‘ participants upon proof. |
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| Entry qualification | 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. |
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| Language requirements | English The language of the course is English, so we expect a fluent level and the ability to follow and participate in class. |
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| More information |
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Overview
Course Description
This is an introductory course to data science. Via a combination of hands-on workshops and lectures, the course covers the basics methods and practices of data science projects. The course is designed around the data science lifecycle to show the techniques for handling data and data analysis. 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 to analyse data;
iv) know how basic data analysis algorithms work;
v) draw conclusions with regard to the data question/hypothesis by interpreting the data.
Goals
• Becoming familiar with the data science lifecycle.
• Use Python as a programming language to perform data analysis tasks.
• Becoming familiar with the data manipulation process and how to achieve this in Python.
• Understand basic machine learning algorithms and their application.
• Understand data interpretation and visualization tools.
• Understanding responsible principles in data science projects.
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, Attendance
Course Coordinator
Dr. Visara Urovi
Programme structure
This course provides lectures and workshops (in the form of 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 of how to perform Logistic Regression and how to use simple binary Classifiers.
• Lecture: Responsible Data Science.
• Practical: Responsible Data Science: This session will cover how to apply the responsible principles to create data analysis processes that are well-structured, that can be replicated, and that treat sensitive data appropriately. During this session you will also be introduced to the application of Deep learning techniques.
• Assessment (In the form of a Take-Home exam).