Who benefits from the Bachelor Data Science at IU - Requirements, Target Audience & Suitability

With your B.Sc. in Data Science, you can work as a Data Engineer, Data Scientist, or Digital Analyst Consultant

The distance learning programme Data Science (B.Sc.) at IU is aimed at anyone looking to acquire or deepen competencies in the field of data analysis, programming, AI, and future-oriented IT. The programme is particularly suitable for professionals who want to further qualify themselves in the digital environment, as well as for school leavers with analytical thinking, an interest in mathematics, and IT. As a graduate, you could later work as a Data Engineer, Data Analyst, or AI Specialist - anywhere where large amounts of data need to be structured, analysed, and interpreted in a forward-looking manner.

What admission requirements apply?

For entry into the Bachelor's programme Data Science at IU International University, the following formal access criteria apply:

With (technical) Abitur: You can start without any further requirements if you have a general higher education entrance qualification (Abitur), a subject-related higher education entrance qualification, or a technical college entrance qualification.

With a foreign qualification: An international school leaving certificate (e.g. IB Diploma) can also be accepted - in this case, IU will assess the equivalence on a case-by-case basis in accordance with the requirements of German higher education legislation.

Studying without a school-leaving certificate: Entry is also possible without the classic university entrance qualification if you

• hold a master craftsman's certificate,
• have completed an advanced training course with an IHK qualification (e.g. as a specialist), or
• can demonstrate a recognized vocational training of at least two years plus at least three years of professional experience (full-time).

Depending on the focus of your completed training and the desired subject of study, direct entry or a trial study may be required.

English language proficiency certificate: For the exclusively English-language study programme, you must provide proof of English language skills at level B2 (CEFR), e.g. through

• TOEFL (at least 80 points),
• IELTS (at least 6.0),
• Duolingo English Test (at least 95 points), or
• Cambridge Certificate (at least grade B overall).

The certificate must not be more than 5 years old. If you have completed an English-language first degree or English is your native language, this proof is not required.

Tip: IU offers you a non-binding trial month in which you can flexibly test the programme. If you have any questions about the equivalence of qualifications or the recognition of professional achievements, the student advisory service provides individual support.

Possible personal requirements for the Bachelor Data Science

• Basic interest in mathematics, statistics, and IT topics - many modules are based on these skills.
• Analytical thinking: The ability to understand complex data structures.
• English language skills at B2 level are mandatory.
• Independent and organised work - distance learning requires discipline and good self-management skills.
• Motivation to learn with digital tools and blended learning concepts: Interactive books, the IU Learn app, online courses, and virtual events are an integral part.

Typical experience in programming or data analysis is helpful but not a mandatory requirement. IU will guide you step by step into the topic with practice-oriented basics. Much more important is the willingness to learn independently and engage with new digital technologies.

Who is the distance learning Data Science particularly suitable for?

• School leavers planning a career in the field of Data Science or the IT industry.
• Professionals from traditional industries looking to build digital competencies (e.g. engineering, business administration, natural sciences).
• Career changers with professional experience looking to switch or further develop in data analysis, AI, or Big Data.
• Individuals valuing flexibility and looking to combine studies, work, family, or social commitments.
• Anyone looking for a globally recognised and internationally valid qualification to start a career in Data Science worldwide.

Your roadmap through the Data Science distance learning programme at IU

In the Bachelor's programme in Data Science at IU International University you will develop core technical, mathematical and analytical skills that enable you to analyse, interpret and implement large data sets into practice. The programme is 100% in English and comprises a total of 180 ECTS credits, which you can earn either full-time or flexibly part-time. Practical and project work are just as important as cutting-edge technologies.

What content will you learn in the Data Science distance learning programme?

• Foundations and Programming: You will start with introductory courses in Data Science, Academic Work, Programming (Python, also object-oriented and functional) and mathematical subjects such as Analysis and Linear Algebra.
• Statistics and Data Analysis: You will learn descriptive and inferential statistics, methods of exploratory data analysis, and data wrangling.
• Databases and Infrastructure: Topics like database modelling, database systems, and projects for creating your own data marts in SQL form the technical backbone.
• Machine Learning and Artificial Intelligence: You will dive deep into machine learning (supervised and unsupervised learning), feature engineering, and deep learning/neural nets. This includes hands-on projects such as "From Model to Production".
• Big Data and Cloud: You will learn how to process large data streams (Big Data Technologies), use cloud applications, and design data infrastructures.
• Interdisciplinary Skills: Seminars on intercultural and ethical decision-making, agile project management, as well as a focus on data protection and IT security round off the education.
• Specialisations (Elective Subjects): Three individual specialisation areas are available, e.g. Data Engineer, Data Analyst, or AI Specialist. Additional elective modules offer application-oriented focuses, such as in marketing, supply chain management, automation/robotics, or international business topics.
• Thesis: Your studies culminate in the Bachelor's thesis - here you demonstrate how you solve complex real-world issues using data science methods.

Videos, podcasts, multimedia learning scripts (Interactive Books), and practical projects ensure a varied learning experience. Virtual seminars and optionally a practical experience or study abroad period are possible.

How your studies work

• Start anytime: You can start your studies at any time, without being tied to semesters.
• Individual Time Models: Choose full-time (36 months), part-time I (48 months), or part-time II (72 months). Changing between these options is possible at any time during your studies, in case your time budget changes.
• 100% digital: All materials are provided through the Online Campus and the IU Learn App, including digital scripts, video lectures, and interactive quiz formats.
• Exams: Take exams online (7 days a week, around the clock) or optionally on-site. Other types of assessments include essays, project reports, presentations, or open-book exams.
• Support and Community: With over 200 professors, 1,000 tutors, and study coaches, you have a strong support network. Interaction with fellow students is possible through forums and virtual study groups.
• Artificial Intelligence and digital tools: Make use of the study chatbot Syntea, online library, LinkedIn Learning, and other digital tools.
• Practical Relevance: Various projects help you build your own application portfolio and directly apply the theory you have learned.

In IU's study design, you can freely combine the majority of modules and projects and complete them in your preferred order. Study plans serve as guidance, not strict schedules.

Career Opportunities & Job Prospects after Data Science Studies

• Data Scientist: Performing complex analyses, identifying patterns, and developing data-driven solutions for companies.
• Data Analyst: Processing, visualising, and presenting results, creating reports - crucial interface between IT and departments.
• Data Engineer: Building, optimising, and maintaining data infrastructures and systems (data pipelines, databases, cloud architectures).
• AI Specialist: Developing, training, and implementing AI systems, machine learning, and deep learning in industrial or service-oriented contexts.
• Further Areas of Application: Consulting, business, logistics, healthcare, marketing, production, automotive industry, and many more.

After your studies, you are internationally deployable as the language of instruction is English. The degree is nationally recognised and accredited (FIBAA, AR seal, ZFU No. 1110120) - a requirement for future work in senior management or for further postgraduate studies such as the M.Sc. Data Science.

These costs arise and how you can finance them

• Full-time Model (36 months): from €399 per month, total costs approximately €15,063 (including graduation fee)
• Part-time I (48 months): €344 per month, total costs around €17,211
• Part-time II (72 months): €259 per month, total costs about €19,347
• Graduation fee: one-time approximately €699 at the end of the studies

Included Services: Use of all learning platforms, digital and printed materials, personal support, career and study coaching, access to online libraries, LinkedIn Learning, and office software.

• Discounts and Promotional Prices: Special promotions are often available, e.g. iPad, completion bonus, fee discounts...
• Financing: The studies can be financed through BAföG, educational funds, or student loans. Fees for part-time studies are often tax-deductible. Recognition of previously earned credits (ECTS) further reduces the fees.
• Free Extension: If you don't finish within the designated study period, an extension of up to 12 months is possible free of charge.
• Trial Month: You can test for one month without obligation - fees are only incurred if you continue the studies.