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63982-01 - Lecture: A Practical Introduction to Data Science (4 CP)
| Semester | spring semester 2026 |
| Further events belonging to these CP |
63982-01 (Lecture) 63982-02 (Practical course) |
| Course frequency | Every spring sem. |
| Lecturers | Ivan Dokmanić (ivan.dokmanic@unibas.ch, Assessor) |
| Content | Data-driven research has established itself as the fourth scientific method, alongside theory, empirical research, and computational science. Data science is the new science of generating knowledge from data. To be precise, rather than a “pure” bona fide science, it is really an amalgam of tools, techniques, and processes from several disciplines—statistics, computer science, signal processing, machine learning, …—chosen to form a powerful toolbox and a set of best practices for modern data analysis. Success stories of data science range from molecular biology where it is used to understand single cell RNA sequencing datasets, over physics where it is used to detect new elementary particles, to governance and policymaking where it is used to visualize, understand, and predict global migration flows. “A Practical Introduction to Data Science” is a first data science course for a varied audience, which emphasizes concrete examples in Python. The prerequisite is that you have some experience with programming in Python. The course covers data visualization principles in Python, relevant fundamentals of statistics and probability with many computational examples, and fundamentals of machine learning and, time permitting, neural networks. |
| Learning objectives | - Use python packages to load and handle data in various formats - Understand principles of data visualization; get familiar with python packages for visualization - Gain intuition about the fundamentals of statistics - Randomness, distributions, sampling - Means, averages, deviations, interquartile ranges, … - The law of large numbers, the central limit theorem - The Monte Carlo idea (how do we try complicated things out computationally?) - Statistical inference: confidence intervals, hypothesis testing, p-values - Learn fundamentals of machine learning - Cross-validation - Logistic regression, generalized linear models - neural networks - Gain exposure to handling different data types - Numerical data in tables, images, text |
| Weblink | https://dmi.unibas.ch/de/studium/compute |
| Admission requirements | - A first course in Python programming such as those offered at the DMI (62035-01 - Einführung in die Programmierung) |
| Language of instruction | English |
| Use of digital media | No specific media used |
| Interval | Weekday | Time | Room |
|---|---|---|---|
| wöchentlich | Wednesday | 08.15-10.00 | Alte Universität, Hörsaal -101 |
Dates
| Date | Time | Room |
|---|---|---|
| Wednesday 18.02.2026 | 08.15-10.00 | Alte Universität, Hörsaal -101 |
| Wednesday 25.02.2026 | 08.15-10.00 | Fasnachtsferien |
| Wednesday 04.03.2026 | 08.15-10.00 | Alte Universität, Hörsaal -101 |
| Wednesday 11.03.2026 | 08.15-10.00 | Alte Universität, Hörsaal -101 |
| Wednesday 18.03.2026 | 08.15-10.00 | Alte Universität, Hörsaal -101 |
| Wednesday 25.03.2026 | 08.15-10.00 | Alte Universität, Hörsaal -101 |
| Wednesday 01.04.2026 | 08.15-10.00 | Alte Universität, Hörsaal -101 |
| Wednesday 08.04.2026 | 08.15-10.00 | Alte Universität, Hörsaal -101 |
| Wednesday 15.04.2026 | 08.15-10.00 | Alte Universität, Hörsaal -101 |
| Wednesday 22.04.2026 | 08.15-10.00 | Alte Universität, Hörsaal -101 |
| Wednesday 29.04.2026 | 08.15-10.00 | Alte Universität, Hörsaal -101 |
| Wednesday 06.05.2026 | 08.15-10.00 | Alte Universität, Hörsaal -101 |
| Wednesday 13.05.2026 | 08.15-10.00 | Alte Universität, Hörsaal -101 |
| Wednesday 20.05.2026 | 08.15-10.00 | Alte Universität, Hörsaal -101 |
| Wednesday 27.05.2026 | 08.15-10.00 | Alte Universität, Hörsaal -101 |
| Modules |
Electives Bachelor Mathematics: Recommendations (Bachelor's Studies: Mathematics) Electives Bachelor Nanosciences: Recommendations (Bachelor's Studies: Nanosciences) Electives Bachelor Physics (Bachelor's Studies: Physics) Electives Master Mathematics: Recommendations (Master's Studies: Mathematics) Modul: Humanities and Social Science Coding (Master's degree subject: Digital Humanities) Modul: Informatik (Bachelor's degree subject: Physics) Module: Applications and Related Topics (Bachelor's Studies: Computer Science) Module: Applications and Related Topics (Bachelor's degree subject: Computer Science) Module: Chemical Biology (Bachelor's Studies: Chemistry) Module: Chemistry (Bachelor's Studies: Chemistry) Module: Field Electives in Economics and Public Policy (Master's Studies: Economics and Public Policy) Module: Inorganic Chemistry and Analytics (Bachelor's Studies: Chemistry) Module: Interdisciplinary and Transfer of Knowledge (Master's Studies: Actuarial Science) Module: Specific Electives in Data Science and Computational Economics (Master's Studies: Business and Economics) Module: Specific Electives in Marketing and Strategic Management (Master's Studies: Business and Economics) Module: Technology Field (Master's Studies: Business and Technology) Wahlbereich Grundstudium Bachelor Physik: Empfehlungen (Bachelor's Studies: Physics) Wahlbereich Master Physik: Empfehlungen (Master's Studies: Physics) |
| Assessment format | continuous assessment |
| Assessment details | Grading will be based on a (computer-based) written exam at the end of the semester. Expected date: tba |
| Assessment registration/deregistration | Reg.: course registration, dereg: cancel course registration |
| Repeat examination | no repeat examination |
| Scale | 1-6 0,5 |
| Repeated registration | as often as necessary |
| Responsible faculty | Faculty of Science, studiendekanat-philnat@unibas.ch |
| Offered by | Fachbereich Informatik |