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MBA in AI & Big Data

For the top 1% business man in AI/BigData

Most cost effective and business-oriented MBA for deep AI/Data Science 

Nowadays a variety of industries and companies claim their adoption in AI, but the reality is still far behind what AI optimists have promised. The program, by reasonable depth of theoretic understanding in elementary scientific tools, helps students to uncover exaggerated claims in the AI/BigData industry, to look through what the necessary skill sets are to achieve goals in AI application, and to fit expectations to reality. Unlike regular MBA programs elsewhere, it is a more hard skill-based, yet AI business-oriented specialization.

Beaware that SIAI’s AI MBA does not provide ordinary MBA’s biz courses. Most courses are shared with PreMSc AI/Data Science, which is nearly identical to US top research school's junior to senior year undergraduate programs in STEM. Between AI MBA and PreMSc, 10 out 12 course contents are identical, but MBA students are given different sets of grading/evaluation. If wanted, MBA students choose Technical track to be under MSc’s grading policy.

Technical track

The track is nearly identical to Pre-MSc AI/Data Science, which is highly similar to junior to senior year BSc programs in the top-notch research schools. As MBA, we add more AI business case courses, if to mention any difference.

Upon graduation, students are qualified to attempt a variety of daily business problems with sufficiently deep enough technical tools. Mathematical requirements for this track are as good as first few courses in scientific Bachelor programs in top research schools. Technically not prepared students are unfortunately discouraged to pursue this track.

Successful candidates later rejoin to school for MSc AI/Data Science, as with Pre-MSc track students. Unsuccessful students are encouraged to switch to Business track

Business track

The business track is designed to explain learned concepts in plain English. List of classes required to finish are identical to the technical track. i.e. Students are given the same class materials.

However, unlike technical track, students have options to write essays instead of math and code-based examinations. For the essay, the course covers use-cases of previous exams, which are mostly concise summary of live events in real field, so as to apply conceptual understanding to students’ personal business environments.

Such re-interpretation has proven to strengthen one’s deeper understanding and more clear translation of mathematically written idea to live business events.

Lecture Note

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SIAI MBA Note
Math & Stat for MBA I
SIAI MBA Note
Math & Stat for MBA II
SIAI MBA Note
Scientific Programm
SIAI MBA Note
Machine Learning
SIAI MBA Note
AI in Digital Marketing
SIAI MBA Note
Deep Learning
SIAI MBA Note
AI in Business Cases
SIAI MBA Note
Reinforcement Learning
SIAI MBA Note
Investment Case
SIAI MBA Note
Data Visualization
SIAI MBA Note
Data Management
SIAI MBA Note
Data-based Decision Making
MBA in AI BigData

at a glance

HIGH QUALITY ONLINE CLASSES

ONLINE TA SESSIONS

12 COURSES & 1 DISSERTATION

8 WEEKS FOR TWO COURSES

2 COURSES PER WEEK

1 YEAR PROGRAM

Learning Outcomes

DATA ANALYTIC TRAINING FOR ABSTRACT THEORY TO REAL APPLICATION

CASE STUDIES FOR APPLYING COMPUTATIONAL SCIENCE TO BUSINESS

MBA in AI & Big Data

Program Structure
How classes work
  • 3-hour Video-recorded classes per week (Required)
  • 1-hour support sessions per week  (Selective)
  • Final exam / term paper 1-week after the end of the course
  • Total 12 courses, 2 courses for 1 term
  • 1 term for 8 weeks
Admission
Admission

Class module : Online only

Credit : 90 ECTS / (Level / EQF 7)

Required documents 

  • Bachelor diploma and transcript (mandatory)
  • Graduate school diploma and transcript (if applicable)
  • Statement of Purpose
Course Curriculum
Course Curriculum

1 Year for 12 courses

1 Term for 8 weeks with 2 courses

Prep classes are available

  • LaTeX for assignments and paper writing
  • Programming prep for Python

Requirement for graduation

  • Coursework: 60 ECTS (12 courses)
  • Dissertation: 30 ECTS
    • Technical track: 20,000 words or above and technical interpretation of the topic
    • Business track: Case study equivalent to tech track's dissertation

Core Course Description

Tuition Fees
Tuition Fees
  • Application fee : CHF 200.- (Non-refundable)
  • Administration fee : CHF 1,000.- (Non-refundable)
  • Courses : CHF 1,650 per course
    – 2 courses per term (Bi-monthly payment)
    – 1 course for 5 ECTS*

Graduation requirements

  • Coursework – 60 ECTS* wth average 60% or above
  • Dissertation – 30 ECTS*
    • Tech track: 20,000 words academic essay or equivalent mathematical/programing application
    • Biz track: Case study equivalent to tech track's dissertation
    • 6 months support course (CHF 2,000)

*ECTS – European Credit Transfer and Accumulation System

Scholarship

  • If 70% or above in admission examination
  • RA/TA opportunities
Admission Examination
Admission Examination
  • No official admission examination
  • Following documents will be thoroughly reviewed
    • Statement of Purpose
    • Undergraduate transcript

Please note that if you have not done any STEM education during your undergraduate or even in graduate studies, we recommend you to try MBA AI programs' business track. Even if you have applied for tech track, if no record of mathematical training is found, the offer letter will be given to biz track.

English requirement
For non-native English speakers, should meet one of the following criteria

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 iBT 100/120 or above (with each section at least 21/30)
  • IELTS 7 or above (with each section at least 6.5/9)
  • Pass grade from SIAI’s internal English course

 

Internal English course

  • Course fee: CHF800
  • Course schedule
    • July~Aug (8 weeks)
    • Live session
    • 3 hours per week (usually weekend)

Q&A

The technical track shares most of undergraduate STEM education required for AI/Data Science, which is also taught at PreMSc AI/Data Science. This is the most chosen program by students who look for a quick study for AI/DS without grad school level Math & Stat. Course materials are mostly at Junior to Senior years of undergraduate programs in the US top research schools. Many parts of our lecture notes are publicly shared (check above 'Lecture Note' link) so as to help students to understand what we teach and what are the examination questions. For business track, since we do not assume students to be well-equipped to STEM's math, we replace examinations with essays. The biz track is for students to look through what real AI/Data Science field is running, and be conversant to data scientists.
Students must earn 50% or above on average in coursework and should pass dissertation. Grading scale of the school is available from school's regulation. Students must finish the coursework within two years and should pass dissertation in the following year
Students can choose to write dissertation during the 2nd semester or after the coursework. If to finish the dissertation by the end of 2nd semester, the graduation will be September of the year, otherwise September of the following year. The dissertation support course offers total 6 meetings with students. Students are to meet instructors via online meeting and all conversations will be recorded for personal and peer review purposes.
We do not expect students to be software engineers. We want students to be a data/research scientist with scientific programming skills. How fast you type codelines or how long code have you debugged, for example, are irrelevant experience for us. We want students to convert mathematical reasoning to program code to materialize thought experiments. Most courses provide sample codes for assignments and examinations.
There only are two course differences between two programs. BUS502 and BUS503 are focused on BigData-based model optimization, which are typical in IT sectors. In-depth discussions of recommendation engine, multi-touch attribution models are well-known examples that are shared between AI Marketing and Data Science, for example. BUS504 and BUS505 are for Finance track courses that covers recently adopted financial models in Corporate Financd and Financial Investments. It is not like we help you to create an AI model that beats fund managers. We focus more on comparing traditional financial theories with new approaches that are, in fact, only an extension of old models. In the end, AI/Data Science is just computationally heavy statistics. We help students to understand that.
AI Engineers from technologically 2nd-tier institutions think running AI code libraries is AI/Data Science. We want our students to pre-check the data to identify potential correlations that may affect denseNet's performance, thus propose a revised model to either re-design the NeuralNet or change the data structure accordingly. At the end of the day, all good models are DGP (Data-Generating-Process) optimised variations of existing models, not a copy of other company's working code. This is our version of 'Critical Thinking'
Computer programming indeed is essential in every sub-discipline at SIAI, but students are given ample amount of study materials. For starters, Issues in Computer Programming (OPT101) , a short prep course given to all incoming students, covers a number of programming issues that are basics for later courses. In addition to that, for every problem set and term paper, a set of guideline code lines are given so as to provide right direction.
Unfortunately, as an online business school, we do not provide any offline support for learning. However, students are free to ask to the course forum. Professors and TAs are available within a short reach of forum posting.

SIAI Experience Stories 

Review

Sungsu Han (MBA AI/BigData, Class of 2024)

Previous school: BS in Electronic Engineering, KyungHee University

I took it while working at the company and taking SIAI school classes in parallel.
It was a near-death schedule that put me through a hard time, and it taught me a lot.
One of my favorite stories says: Go beyond your limits. I was able to gain that experience while working at a company and attending SIAI.
I learned that I can achieve greater growth when I push myself to my breaking point.
Many SIAI students besides me are having similar experiences.
It has been a great asset in my life to be able to meet friends with majors in various fields with whom I can share suffering and grow together as school colleagues, teachers, and friends.
I am currently enrolled in SIAI MBA AI/BigData and am about to graduate.
While writing my graduation thesis, I learned through SIAI's educational philosophy how to find solutions that only I can create to solve problems.

MBA, but not the same MBA

MBA in AI & BigData is not a regular MBA. It is specifically focused on data analysis in business environment.

In the program, elementary math, stat, and scientific programming courses which are often regarded as research and hard theory courses are translated into business manner. Unlike vocational schools for programming or MBAs for soft-skill jobs, the courses require students to train abstracting simple cases so as to generalize and customize to a variety of differing situations. Such training help students seeing theory in a more practical perspective, which is a foundation of this program.

After initial training in elementary scientific knowledge, in Digital Marketing, students are presented how online IT business leverages AI & BigData, which works as a guidance for practical understanding of theories. Similarly, in AI Business cases, newly emerging business (ex. Robo-advisors, SNS analysis) applications of AI will be discussed in the context of math and stat in response to the recent advent of computational approaches. In addition, legal issues, mostly privacy protection issues, are provided in conjunction with Law & Economics and IT business specific concerns in Law and AI.

In the final term, both data visualization and data management help students not only build a sample database (DB) and read it to programming language, but they will also use the DB to apply learned theory to real-life data set. Graduates will experience that, together with mathematical intuition, data visualization can be extended to solve a myriad of business problems.

The program concludes with a MBA-level dissertation (tech track) or a case study (business track).

AI MBA balances theory and practice to foster top-tier business man in AI/BigData.
Be one of them!
Course Curriculum
Department Contact Info

Master of Science Program

[email protected]

Mon – Fri 9:00 A.M. – 6:00 P.M.

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