Top-up BSc in Data Science
BSc in Data Science (Top-up)
For young enthusiasts without relevant training
BSc in Data Science (Top-up) is a customized 1.5-year-long program for recent college graduates or near graduation, but without enough training to start MSc AI/Data Science or kick-off one’s business career as a trained data scientist. For those with over 2 year experience in relevant fields, we recommend MBA in AI/BigData program.
The structure of BSc DS (Top-up) is upper 50% of full BSc DS program. The top-up program assumes that students already have some training in basic mathematics and statistics, such as elementary linear algebra, differential equations, and basic statistics. Although the field of Data Science does not require rigorous mathematics, some prior exposure to such topics is critical for survival in the program. We believe students from statistics are the most suited, and mathematics, physics, engineering, economics are also expected to have relevant basic training in their first mathematics and statistics undergraduate courses. Though we do not expect anywhere beyond, basic statistical understanding will be accessed during the admission interview.
This second half of BSc DS overlaps with MBA in AI/BigData, MBA in AI/Finance, and some additional courses that are pivotal preparations for graduate academic studies. Students are given to choose either AI/BigData or AI/Finance track courses for the first academic year, taking courses together with MBA students, and in the last semester, more mathematical and rigorous training will help BSc DS students to deepen theoretic scope.
Upon graduation, successful candidates should be well-balanced between business application and theoretic understanding.
BSc in Data Science
at a glance
HIGH QUALITY ONLINE CLASSES
ONLINE TA SESSIONS
17 COURSES & 1 PROJECT
8 WEEKS PER COURSE
2 COURSES PER WEEK
1.5 YEAR PROGRAM
Learning Outcomes
Global top class AI experts
Strengthen your ability in computational science projects
How classes work
- Online live/recorded classes from 7pm-10pm on weekdays (Required)
- Online live TA sessions on weekends (Selective)
– Mostly 1-2 hours in day time on Sat - Final exam 1-week after the end of the course
- Total 12 courses, 2 courses for 1 term
- 1 term for 8 weeks
One 5 ECTS* course is consisted of 8 lectures, 7 TA sessions, and 1 final exam. Total teaching hour is 33-40 hours and together with self-study hour, per course required hour is 125.
*ECTS – European Credit Transfer and Accumulation System
Admission
- Bachelor diploma in all majors
- or 90 ECTS in relevant majors (such as statistics, math, data science, or related)
Class module: Online only
Credit: 100 ECTS / (Level / EQF 7)
Required documents
- Bachelor diploma and transcript (mandatory)
– or Bachelor transcript fulfilling admission requirement - Statement of Purpose / Curriculum Vitae (or Resume)
1 Year for 12 courses & additional 5 courses in the extra semester
1 Term for 8 weeks with 2 courses
Prep classes are available
- LaTeX for assignments and paper writing
- Programming prep for Python
- Application fee: CHF 200.- (Non-refundable)
- Administration fee: CHF 1,000.- (Non-refundable)
- Courses: CHF 1,700
– 2 courses per term
– 1 course for 5 ECTS*
Graduation requirements
- Coursework – 85 ECTS*
- Research project – 15 ECTS*
*ECTS – European Credit Transfer and Accumulation System
Scholarship
For non-native English speakers, should meet one of the following criteria by graduation
- High school or University level diploma from an all-English program
- TOEFL 100 or above (with each section at least 21/30)
- B- or above from SIAI’s internal English course
– Tuition fee: CHF 1,000
For non-native English speakers, should meet one of the following criteria by graduation
- High school or University level diploma from an all-English program
- TOEFL 100 or above (with each section at least 21/30)
- B- or above from SIAI’s internal English course
– Tuition fee: CHF 1,000
BSc Data Science (Top-up) assumes candidates have over 90 ECTS (or equivalent) coursework from an renowned university (the credit of which should be approved by the admission committee). Graduation requirements consist of 85 ECTS coursework and 15 ECTS research project. The details of research project will be introduced after finishing 60 ECTS coursework.
Global top quality
What is known as Artificial Intelligence (AI) is a business application of computational science. For that, every course is dedicated for deeper mathematical and statistical theory that are basis for a number of computational disciplines. The curriculum is designed to expand coverage in a variety of computational studies, all of which meet for the ultimate goal of this program. Understanding AI scientifically so that it can be applicable to real world problems.
Target Every non-linear modeling is accessed in comparison of traditional statistical approaches and recently highlighted machine learning models in order for students to understand computationally heavy models are not always the most relevant choice of AI applications. are
In term 3 to 5, the curriculum centers on the relevant use of deep neural network and reinforcement learning, which are nothing more than nested non-linear factor analysis and multi-stage dynamic optimization. The coursework concludes by extension of such models to multi-agent cases, one of which is applied to a project for self-driving algorithm’s defensive driving strategy, in the context of game theory and Bayesian statistics.
Upon graduation, students will be able to learn not only the necessary mathematical and statistical theory in the background of AI models, but they are also capable of applying such advanced knowledge into real world businesses.
Fundamental Math and Stat
are critical for “Real World Applications”
in every quantitative discipline
Target for Research Institutions
AI is not about programming, as often argued by many engineering departments. It is all about how to leverage mathematical modeling and statistical intuition in a way to best use of computational science. Our course projects will help you learning them by doing them.
As students absorb SIAI style training and apply it to projects, students will gradually become knowledgeable in which computational strategy to choose for what problem in every context, for which literature to review for deepening their understanding of models, and by which technique a problem can be solved.
We are not a vocational program for practicing coding language, but an academic program that help students to apply hard theory to change our daily life.
Mathematics is only a hobby, if one doesn’t know where to apply. We make math not a hobby.