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 |
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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 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.
• Assessment
Goals
• Get to know the data science lifecycle;
• Use Python as a programming language to perform data analysis tasks;
• Become familiar with the data manipulation process and how to achieve this in Python;
• Get introduced to basic machine learning algorithms and in their application
• Understand data interpretation and visualization tools
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
Course Coordinator
Dr. Visara Urovi