Python for Applied Data Science is an introductory course that equips students with essential programming and data analysis skills using Python. Python is a multipurpose, flexible, and powerful programming language that is widely used across many fields, especially data science. Due to its clear syntax and readability, it is particularly well suited for beginners with no prior programming experience.

The course introduces students to the Python programming ecosystem and demonstrates how programming skills can be applied to explore, process, and analyze data. Students learn how Python can be used to work with complex and unstructured datasets, extract meaningful information, identify patterns, and support data-driven decision making.

A key focus of the course is the role of programming in applied data science. Modern data analysts and data scientists rely on programming tools to clean data, create visualizations, and build basic predictive or decision-making models. This course emphasizes the importance of combining domain knowledge with practical programming skills. Through hands-on exercises and assignments, students gain the confidence to write their own Python programs and apply data analysis techniques within their own domain.

Content and Program

The course is divided into two main parts.

OP1: Python Crash Course focuses on the fundamentals of Python programming that are essential for data science. Students learn core concepts such as control flow, loops, functions, data structures, object-oriented programming, and modular code organization. This part ensures that all students, regardless of background, acquire the necessary programming skills.

OP2: Applied Data Science builds on the programming foundation and introduces students to practical data science workflows. Students work with Jupyter Notebook and learn to use libraries such as NumPy and pandas for data manipulation and analysis. The course also covers data visualization, basic machine learning concepts, supervised learning techniques, and model evaluation.

Teaching Method & Exam
The course is taught using a theory–practice structure. Each topic is first introduced in a theory-based lecture, where key concepts and methods are explained. This is followed by practical sessions in which students work on exercises during class time to apply and reinforce the material covered in the theory lesson. Live coding, guided examples, and hands-on problem solving are central to the learning process.

Schedule

OP1: Python Crash Course
Week 1: Introduction to Python
Week 2: Conditionals and Control Flow
Week 3: Loops and Iteration
Week 4: Functions
Week 5: Lists and Tuples
Week 6: Dictionaries and Sets
Week 7: Object-Oriented Programming (OOP) Concepts
Week 8: Modules and Packages

OP2: Applied Data Science
Week 1: Introduction to Jupyter Notebook, NumPy, and Pandas
Week 2: Working with DataFrames
Week 3: Data Manipulation and Preparation with Pandas
Week 4: Data Visualization with Matplotlib and Seaborn
Week 5: Machine Learning Fundamentals
Week 6: Supervised Learning
Week 7: Model Training, Evaluation, and Validation

For this course a laptop is required. 

1. you have successfully completed your internship. 
2. You have obtained a minimum of 90 credit points from the main phase of your study programme. 

This course is designed for students of all backgrounds who want to apply programming skills and functions to projects in their own fields. However, this course is NOT designed for computer science students. Or any program that includes a programming course in
its curriculum. For example, students in programs such as Applied Data Science & Artificial Intelligence, Creative Media and Game Technologies, Informatica, and Technical Informatica can't take this course.

Assessment in this course is assignment-based; there is no written exam. The course is evaluated on a Pass/Fail basis and consists of two separate parts (OP1 and OP2), each of which must be passed to successfully complete the course.

For each OP, students complete a set of weekly assignments and a final assignment or project. A minimum number of weekly assignments must be submitted and completed correctly by the deadlines. These assignments are designed to support continuous learning and practical application of course topics.

Each OP concludes with a final assignment or project, which includes a presentation and an oral examination. The oral examination is used to assess students’ understanding of the work, the underlying concepts, and—where applicable—their individual contribution to group work.

Evaluation will be based on assignments without exams.

These are the key points of the course evaluation:

1. For each OP, students will have:
   - Weekly assignments.
   - A final assignment at the end of the period.
2. Each period will be evaluated separately based on the overall evaluation of the weekly and final assignments.
3. Students must pass both periods to pass the course.

GENERAL

• For this course a laptop is required.
• Students will work in groups.
• Teaching materials will be provided in the form of slides by the instructor.
• For more information, contact the course coordinator.

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Minor Choice Week | 2–5 March 2026

Minor fair
This minor will be present at the minor fair. Will you join us? View the programme and register here.

Date: Thursday, 5 March 2026
Time: 4:00–7:30 p.m.
Location: Kralingse Zoom

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APPLICATION PROCESS (KOM)

Are you a student from another educational institution and would you like to follow a minor at Rotterdam University of Applied Sciences? If so, you must apply in two steps:

Step 1

• Register for the minor of your choice via the blue Apply button. You can find this button at the top right of the minor’s page.
• Download the learning agreement and complete it.
• Submit this learning agreement to the examination board of your study programme.

Once the examination board has granted approval, register for the minor in Step 2 no later than 01-07-2026 at 9:00 a.m.

Step 2
After approval, register via OSIRIS Application of Rotterdam University of Applied Sciences using the link below (first create an account).

Apply

Part of the application process is uploading the following documents:

• The learning agreement, signed by you and by your institution;
• A scan or photo of your passport or ID card.

You will be informed by Rotterdam University of Applied Sciences whether your application has been approved.

In OSIRIS Application, you must also upload the Proof of Paid Tuition Fee (BBC) for the academic year in which you wish to follow the minor. This can be done from 01 May 2026 onwards. You can request the BBC from your institution after you have signed or issued an authorization for the payment of the tuition fee for the relevant academic year. You may also choose the option for your institution to send the BBC directly to collegegeld@hr.nl.

You will receive a notification from Rotterdam University of Applied Sciences once your application has been approved.