Unlock your future potential with this Data Science minor, often described as the "sexiest job of the 21st century" by Harvard Business Review. Data scientists are the modern-day explorers, navigating vast seas of data to make groundbreaking discoveries. Proficient in the digital realm, they bring structure to oceans of unorganized data, making analysis possible.

For three consecutive years, data scientist has held the top position as the most sought-after job in the U.S., and the demand extends to the Netherlands and the rest of Europe. In a world where businesses and non-profit organizations increasingly rely on data-driven decision-making and machine learning, the ability to extract meaningful insights from the deluge of data is a formidable challenge.

No matter your field, expertise in data science techniques can significantly elevate your career prospects. This minor equips you with the skills to add value to businesses by integrating data sources, applying descriptive and predictive analytics, and leveraging tools like Python. You'll gain an introduction to Machine Learning applications and become adept at advising businesses on enhancing their operations through data-driven insights.

Throughout this program, you'll experience the complete data project lifecycle, from (1) identifying information needs, (2) sourcing relevant data, and (3) enhancing data quality, to (4) conducting data analysis with Machine Learning algorithms and (5) creating insightful visual reports that guide decision-making.

If you're eager to pursue a Data Science project to boost your career or if you're interested in joining or advancing in a Machine Learning project at your workplace, this tailored minor program is ideal for you. We're here to guide your learning journey and provide comprehensive support.

Even if you lack your own dataset, we can connect you with external companies and real-world cases, enabling you to work on impactful projects. This minor offers a unique chance to gain practical experience and enhance your Data Science skills.

 

Students learn how to create added value for the business through combining data sources, applying descriptive and predictive analytical methods and tooling including Python. The students are introduced to Machine Learning applications and are able to give advice on decisions businesses have to take to improve their operations.

1. Students can determine the concrete information need in a given context

2. Students can identify different kinds of data sources

3. Students can assess and improve data quality

4. Students can apply elementary predictive data analysis techniques and machine learning algorithms using current tooling such as Python libraries

5. Students can create visual reports that represent the outcomes of the analysis in a proper and convincing waying current tooling such as Python libraries 

1. Students can create visual reports that represent the outcomes of the analysis in a proper and convincing way 
2. Students can express the most important ethical and legal aspects of a data science project 

Students know basic regulations and ethical aspects concerning data (storage and use) and can make their own, motivated, decisions on relevant issues. 

The minor is designed to accommodate students in business-related programs as well as those in IT, engineering, and mathematical programs. To be eligible for the minor, students must meet the following requirements:

• Possess intermediate-level proficiency in English.

• Demonstrate successful completion (passing grades) in their first-year and second-year statistics courses as part of their respective programs.

• Alternatively, provide evidence of certification from the following Coursera course: Basic

These requirements ensure that students have a foundational understanding of statistics before enrolling in the minor program.

Basic programming skills are helpful but not required since we’ll start the minor with a bootcamp on Python programming for data science.

Open Educational Resources (OER) will be used exclusively. E.g.: Kaggle, PY4E, KOIOS and Datacamp. 

The first term of the minor (9 weeks) consists of a programming/ML and project classes. The project classes are aligned with the subjects covered in the bootcamp and projects.

Bootcamp: 12 hours (on average) a week during the first 7 weeks

Project classes: 5 hours per week (mandatory)

During the second term of the minor, spanning 10 weeks, students will be assigned a project from an external company. Whether you're currently involved in a Machine Learning project at your workplace or aspire to participate in one, we will provide active guidance and support.

Even if you don't have your own dataset, there's no need to fret. We have the resources to connect you with external companies and real-world cases, ensuring that you can actively engage in impactful projects, even when data isn't readily available.

To facilitate this process, each group is assigned a dedicated coach, and in weekly plenary meetings, students come together to share their learning experiences.

Coaching: 2 hours per week (fixed day)

Plenary meetings and guest lectures: 6 hours per week (fixed day)

Term 1: 17 contact hours per week (3 days per week)

Term 2: 6 contact hours per week ( 1 day per week mandatory))

A minimum of a 5.5 is required for each of the 4 components:

• Written exam on programming (cumulative testing in week 3, 5 and 8 and resit in week 10): 5 ECT

• Written exam on data structures (cumulative testing in week 3, 5 and 8 and resit in week 10): 5 ECT

• Report and presentation on group project (including visualizations and ethical component): 5 ECT

• Report and presentation on external assignment (including guest lectures, visualizations and ethical / legal component): 15 ECT

Written exams take place in week 3, 5, 8 and 10 of the first term.

For any questions contact s.xanthoulis@hhs.nl Instructor/Coordinator minor: Stamatios Xanthoulis