MBA AI/BigData Program Intro

Welcome to the introduciton of SIAI's lecture notes for MBA AI/BigData Program.

Program Intro

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.

Admission examination

There is no admission examination for this pre-training program, but we recommend students carefully go through open lecture materials for MBA AI.

Most incoming students think they are well qualfied for MSc AI/Data Science, but often, it turns out that they are not even able to do the first problem set assignment in MBA's first course, STA501: Data-based Decision Making.

The admission committee issues warnings to the students, and mostly recommend to do MBA AI's business track, instead of technical track. For the difference, please take a look at the MBA program introduction on SIAI's homepage.

Although the school is open for the first quarter for STA501: Data-based Decision Making and STA502: Math & Stat for MBA I, students earned below 50% in examinations will not be admitted to PreMSc AI/Data Science and AI MBA's technical track.

Graduation requirement

The program is consisted of total 12 courses and 1 final dissertation, each carrying 5 ECTS and 30 ECTS, respectively. Students are given to finish the course work (60 ECTS in total) within 12 months, as two coureses are simultaneously given in every 2 months. The school allows additional 12 months of extension to successfuly finish the coursework requirements. Minimum pass grade for each course is 50% or C- in letter grade. Students with lower than 50% must re-take the course in the following year.

Regarding dissertation, each student can choose to write the academic paper during the coursework period or after all courses, depending on one's own schedule. Together with the coursework, students must finish all 90 ECTS requirements by the end of 4th year. The school does not issue the certificate of graduation after that.