About this course
Data is one of the greatest strengths in the digital age. With the IU Online Bachelor Data Science you learn exactly how to deal properly with this strength. In this study programme you will acquire mathematical and statistical knowledge, expertise in different data processing technologies and an overview of different machine learning techniques. A wide range of electives allows you to expand your knowledge in application areas and to be an expert in this field at the end of your studies. With IU, you can study from the comfort of your home, 100% online.
General academic criteria:
- Higher Secondary School Leaving Certificate such as A-Levels or IB Diploma and your transcript of records.
- Proof of eligibility to study at a university in your home country.
Depending on your chosen program, academic level, and background, you might need to also take one of the following to make sure you are ready to study with us:
- IU Entrance Examination
- IU Pathway Programs
At IU, we teach in English to prepare you for the international market.
We therefore ask for proof of your English language skills *. If English is your native language or you graduated from an English-speaking school / university, you don't need to prove your English skills.
- TOEFL (min. 80 points) or
- IELTS (min. Level 6) or
- Duolingo English test (min. 95 points) or
- Cambridge Certificate (min. B grade overall) or
- Equivalent proof
* Proof must be provided before the start of the study and must not be older than two years.
- Introduction to Data Science
- Introduction to Academic Work
- Introduction to Programming with Python
- Collaborative work
- Statistics - Probability and Descriptive Statistics
- Mathematics: Linear Algebra
- Intercultural and Ethical Decision-Making
- Statistics - Inferential Statistics
- Object Oriented and Functional Programming with Python
- Database Modeling and Database Systems
- Project: Build a Data Mart in SQL
- Mathematics: Analysis
- Business intelligence
- Project: Business Intelligence
- Data science software engineering
- Project: From Model to Production
- Machine Learning - Unsupervised Learning and Feature Engineering
- Machine learning - supervised learning
- Agile project management
- Big Data Technologies
- Data Quality and Data Wrangling
- Explorative Data Analysis and Visualization
- Cloud computing
- Seminar: Ethical Considerations in Data Science
- Time Series Analysis
- Neural Nets and Deep Learning
- Electives A
- Electives B
- Electives C
- Introduction to Data Protection and IT Security
- Model engineering
- Bachelor thesis & colloquium