Hey folks,

(Updated on 05 April 2025)
(Updated on 21 April 2025)

I have been meaning to write about this specialization for a while now, for various reasons. Firstly I just want to share my experience and all the things we learned, we got to do, the people I met, the connections I made and so on. Secondly I want it to be a good starting point for anyone who plans to take up this specialization and want to know what the spec entails. I am an MDI PGDM-IB student, who is here at ESCP-Berlin Campus on an exchange. So some parts of it is directed to future PGDM-IB cohort who are looking for some clarity or information on how the ESCP-Berlin spring semester is going to be.

As an IB student, if you want to know more about the IB program and the exchange year at ESCP Business School in particular, you can read my other article here. But this article you are reading will focus on the spring semester.

0. Init

My specialization is named “Leading in AI & Big Data for Business Innovation”[1]. It is a relatively new specialization offered at ESCP-Berlin. We are the 6th batch to take up this spec. I believe it comes under the purview of the department of Information and Operations Management. Prof. Markus Bick[2] heads this specialization along with Prof. David Lehmann[3] who I believe takes care of the specialization logistics.

The specialization has 4 courses:

Enterprise Systems and Future Trends by Prof. Markus Bick - 30 hours
Leading Digital Innovation Projects by Prof. David Lehmann - 30 hours
Big Data and Business Intelligence by Prof. Javier Amaya[4] - 30 hours
Artificial Intelligence and Machine Learning by Prof. Daniel Pesch[5] - 30 hours

Along with that, we have 3 mandatory core courses:

Business Law[6] by Prof. Peter Zaumseil[9] - 15 hours
Sustainability[7] by Prof. Carolin Waldner[10] - 15 hours
Advanced Organization and Management[8] by Prof. Thomas Gigant[11] - 30 hours

So in total 120 hours of specialization and 60 hours of core courses making it a total of 180 hours for the semester, that is the requirement. So at the time of choosing the spec, if you get a spec here in Berlin, then you are set for your 180 hours.

Officially, the first day at ESCP Berlin was 13 Jan 2025, Monday. But each specialization starts at possibly different dates. The Selling to Customers specialization started on 15 Jan 2025, the AI and Big Data spec this time started on 20 Jan 2025. But keeping 13 Jan 2025 as the start date, my suggestion would be to sort your home, anmeldung/registration, blocked account activation, SIM card - all these before your specialization starts. Plan your arrival in Berlin accordingly, that is only if you get your visa on time :P

1. What does this specialization entail?

It is a techno-managerial specialization. I personally think that this specialization works out best for folks with an a technology background who want to switch into technology-management (product management, IT project management, IT Management, IT Strategy, Enterprise Architecture Management, Automation, Business Analytics, Data Science and so on). With an engineering background, you will be in a better position to grasp whatever is being taught. If you are new to AI-ML, Enterprise Systems, product management - this course is an excellent primer.

I wanted to take up something like this as this is a good continuation of my background and professional experience.

In the coming sub-sections, I will give a short description of the specialization courses, the professors, syllabus and structure.

1.1 Artificial Intelligence and Machine Learning

A classic course by Prof. Daniel Pesch. The course entails theoretical introduction to machine learning. Out of the 10 sessions, 5 were regular lecture-style classes, 5 were practical programming sessions. The course has 10 sessions in total, and is completed in 5 days. Two sessions will be scheduled each day, one theory session where the professor will teach a particular topic, the second session is a practical one where you actually work on real datasets, build ML models and get a hang of it. We got a theoretical and practical introduction to supervised learning (session 1), unsupervised (session 2) and reinforcement learning (session 3) along with a detailed session on deep learning and artificial neural networks (session 4). The final session was fully focussed on the latest developments in AI and ML from an academic and a business point of view. I believe the idea is to use all of this to solve business problems. We worked on a variety of problems like credit card fraud detection, sales forecasting, inventory optimization and so on.

The evaluation is divided into a group assignment (50%) and a closed book written examination (50%). Both are geared towards testing your understanding of the subject and your ability to apply them to interesting problems.

1.2 Big Data and Business Intelligence

Great course by Prof. Javier Amaya. This is a highly practical course, it won’t be wrong if I say we apply a lot of what we learn in the AI & ML course here (and more). It focused on the entire pipeline - starting with some kind of big data, what kind of pre-processing needs to be done on what kind of data, what kind of model is helpful for what type of problems, where do you even store this data (a peek into database structure and systems), a lot of emphasis on AI-ethics, a pretty big topic these days (atleast inside EU). I feel most of Prof. Amaya’s classes are like this: He shows an example of how things are done, and we spend most of the time on a variety of examples experimenting and trying out things - this was until half way through the course, post which things got pretty serious. I believe first 2/3 sessions are devoted towards getting us acquianted to working with datasets, different ways of data preprocessing, using a variety of ML techniques on real datasets. The professor shifted gears in the 4th and the 5th session. The 4th was an actual deep-dive into Image Processing and Computer Vision, Convolutional Neural Networks, the latest updates in CV. The final session was fully devoted to Text Analytics, Transformers, Large Language Models, a peek into how they work. This course peaked in the final session because just by sitting through 3 hours of the last session, you could learn a good deal about the things that we see today (chatgpt, generative AI etc.,). Adding to that, we were given a real-world challenge on inventory management, so we teamed up and spend the entire session on it.

The course is tightly coupled with the AI & ML one. Just listen in class and ensure you understand all the professor has taught, you are good to go from an examination point of view. Suggestion is to not miss any class.

Evaluation is exactly same as the AI & ML course - a group assignment (50%) and a closed book written examination (50%).

1.3 Leading Digital Innovation Projects

This is the perfect course for people who want to get into product management, project management, leading digital businesses. This is what the 10 sessions vaguely consisted of:

First 4-5 sessions: Understanding the process of building an application; Coming up with an App Concept; The importance of Stakeholder mapping and User Personas; Building the Service Design for an application (FrontStage-BackStage processes etc.,), Entity Relationship model; Detailed practical sessions on Clickdummy and Wireframes for application.
Workshop day: The entire day is about you working on your app-concept (I will talk about this in a bit). This was very different the usual classes, especially the ones in India. Its a good experience. This constituted of 3 sessions.
Doubt Clarification session: This course is about working on your own app-concept. This session happens 2-3 weeks before the submission.
10th Session: Guest Session + Introduction to Agile Project Management

Evaluation consists of a group assignment (75%) and a individual submission of a reflection paper (25%).

1.3.1 Groupwork

Now a deep-dive into the group assignment (of all the 3 courses). The assignment is to come up with/choose an application concept and essentially do everything apart from actually implementing it:

Come up with a detailed app-concept.
Conduct interviews of different stakeholders and people to gather insights on how important your app can be, improvements, the need for an app like this and so on.
Design everything we were taught in the class: Stakeholder Map, User Personas, entity relationship model, a comprehensive clickdummy, wireframes and userflows, process-flows of the application.
(1), (2) and (3) is about the app-concept and the frontend of the application - which is the deliverable for the leading digital innovation projects course. What abou the backend? Now comes the AI-ML and Big-Data part.
Choose an app-concept in such a way that you can integrate a neat AI-ML backend system to it. It can be a simple system recommending vacation places to you based on your preferences.
The AI-ML and Big Data parts can get quite technical: Essentially detail the how you plan to get the data (data sources), the datasets, how you would store it, the kind of pre-processing you would do, data-biases, legal concerns, ethics and privacy. In the AI-ML part, it is about describing how you use the data to build the backend system, what ML model you use, its description and so on. Talk about their practical deployment details, cost implications etc.,
The end product is this huge and detailed design document of your application concept.

The deliverable, for us was one single document with content related to all the 3 subjects. Its a group project, and if you do it religiously, you end up learning quite a bit. Talking about our team and our deliverable, we put together a good submission I would say. I made the entire AI-ML bit, along with good parts of the Big-Data part, along wih making the Clickdummy, Entity-Relation-Model and working helping my teammates on wireframes, putting together the presentation and so on - it was a crazy lot of work. My german teammate and me were in hackathon mode in the last 2 weeks to the assignment deadline. Overall, it was great learning. Another thing I want to caution you is that it is a lot of work, you cannot chatgpt your way through easily, so choose good, dedicated teammates who take initiative and take up work voluntarily (and don’t expect people to keep following them up, people who just stop communicating or take up small bits of work just for the sake of formality). In this front, I did not have a good experience, so just giving you a heads-up. Group with people who are interested in doing the coursework well and who do not fall into the “all talk no work” category.

In short, the group project is a make or break deal, because it controls one course almost completely, and half of the other two courses.

Update: Myself and a german friend of mine contributed most to this group project. We have decided to take it to the next level, by actually implementing it and making it available to the public. I’ll keep you folks updated on this.

1.4 Enterprise Systems and Future Trends

The ES-Ft course by Prof. Markus Bick focuses on Enterprise Resource Planning, Business Process Modeling and Management, Process Automation, IT Strategy and Management and so on - basically a proper course for anyone interested in Management Information Systems. The classes are generally the Prof giving a lecture for about 1.5 hours, and then certain type of practicals are done. For example, he introduced us to ERP and we had a 2 hour practical session on SAP, you get a hands-on experience on SAP, you work on a neat case study and so on. Prof organizes a number of guest sessions (I think 3/10 were guest sessions) - from very interesting people - SAP, Celonis, Salesforce this time. We ended up looking at one of the latest things Salesforce is doing - their product called AgentForce where they use LLMs for Agentic Process Automation[9]. Apart from that, his classes are filled with interesting case studies. Lots of practical cases and hands-on stuff in the field of informations sytems.

Evaluation has a group assignment (50%) and a closed book examination (50%). The group assignment for us was a virtual company visit, where we actually talk to some company about their current state of ERP implementation and give them useful recommendation (essentially a toned-down version of IT consulting). It is great learning of religiously done.

In summary, there are courses on AI-Big Data, Product management and Enterprise Systems: The IT Trinity (if I may).

2. Schedule

This specialization, that too this year was scheduled in a weird manner I believe. Most of the specializations have classes 3-4 days a week, and that goes on till the first week of April (post which examinations start). But ours was not like that. The AI & ML and Big Data courses got over (30h + 30h) by 08 Feb 2025. We had the final examination for AI & ML course on 10 Feb 2025, and for Big Data course on 17 Feb 2025. Add to that 5 sessions of the leading digital projects course and 3 from the enterprise systems course, our specialization was pretty hectic till 23 Feb 2025. We had classes 6 days a week, 5 days of specialization-courses and 1 day of core courses. But post 23 Feb 2025, we hardly had any classes. The distribution of sessions was quite uneven in our case. This was in a way good because one could spend the entire second half of the semester on the group and other indivdual assignments etc., This schedule related information should not matter in any decision making, because it keeps changing every year, but thought of sharing for the sake of completeness.

3. On the core courses

As listed above, we had 3 core courses: Business Law, Sustainability and Advanced Organization & Management course.

Business Law: This course is all about different facets of German Business Law - the general idea of civil and common law, statutory law, legal structure of firms in Germany and EU in general, employment contracts, labor law, taxation, sales and service contracts, Intellectual property law - 8 sessions with each session on one of these topics. I particularly liked this course, because it was just paying attention to the professor, and it was helping in curing my screwed-up attention span problem. It has a simple group assignment (a very simple one, nothing to worry about) and a 2-hour closed-book examination - the only preparation needed is you listening in the class - I think you don’t even need to read through the gruelling number of slides if you pay attention in the class.
Sustainability: Different facets of sustainability - started with the environment angle, then about different stakeholder responsibilities and finally about businesses, and new business models compatible with sustainability and so on. Really interesting course. It has 3 components: One group presentation, a pre-quiz before every session and a 1.5 hour closed book examination. Again, pay attention and you are good to go.
Advanced Organization and Management: This course was focused on people, leadership and change management. Different types of people, different leadership theories, what and how to go about enforcing organizational changes. This course does not have an examination, instead it has groupwork (50%) and an individual submission (50%).

It is scheduled in such a way that classes for all the 3 courses happen on a single day (for us it was every Friday).

4. General suggestions

I plan to share some stuff that is more than just regular coursework. Just some observations, some personal suggestions.

On the specialization: I don’t know how choosing a specialization works for the regular ESCP MiM students, but for MDI-IB students, its pretty random. As much as possible, choose a good specialization, where you can actually learn something. People whose whole life revolves around the final placements declare nothing else is important apart from the placement. This starts with saying that the specialization you choose is not important, how you do your coursework(at MDI and at ESCP) does not matter, your GPA doesn’t matter (it does, if it goes below a certain point, it will affect your placements) and so on. I come from a different place. I paid my hard-earned money for the ESCP experience (and so have you or your parents). There is literally a statement that goes like “Take any specialization, anyway it does not matter for final placements” - this is kind of stuff one should refrain from, and run away from people who say shit like this. Make the best of the ESCP experience, don’t listen to the usual suspects - only a few of them are worth getting advice from. I can write another full post on all the things I learnt in my specialization - most of it is insanely relevant to today’s world of business and technology. So as much as possible, either in Berlin/Paris or any other campus, choose a good specialization and do it well. Add to that, it was my specialization that helped me get the attention of a startup founder/CEO, he was looking for skills in business process management and I had a semester-worth experience, so it helped. So you never know what will help you how and when, so please pay attention.
On Internships: I think it is fair to say that for the MDI cohort, we are programmed to think that an internship is an insanely important part of our ESCP experience and is the be-all end-all of the European experience. This mindset has bad consequences. I know for a fact that people simply don’t listen in class, or just don’t go to class and so on and keep trying to get an internship even during the 3-hour lecture. I agree it might have paid off for some, but my personal suggestion is to understand that it is a lot more than just getting an internship. Attend that induction programme, meet those new people, go for that specialization alumni meetup party, go to that hike organized by that cool prof of yours, attend those talks by bigshots that happen every now and then, give a self-satisfying submission on that group project - in short, have a healthy college life. That said, focus on internships too, but don’t revolve around it and panic (unfortunate but I see people in this mode). Some just left to India because they don’t see getting an internship here anytime soon. It is quite disheartening to see that.
On Master’s Thesis: As one can guess, no one gives a shit about Master’s Thesis in IB - this is what I understood from proper sources. The Thesis is generally due in January, so most people do some google-form based market research, write a bad report (or use ChatGPT to generate it these days) and submit it as “Thesis” in January. At ESCP (any campus), you have an option to not take that shitty default route and take a good route where you can learn something and produce something of meaning. You can approach any professor (literally ANY), say you are interested in writing a thesis under them and get started early.
You are an ESCP student: Probably because how we are programmed, MDI folks need to remember that we are not mere exchange students, we are formal MiM students at ESCP (but with certain restrictions because of the nature of this arrangement). We are M1 students in spring and M2 ones in fall. On passing the requirements, we get an MiM degree from ESCP (and not just a PGDM-IB degree from MDI). We even get to attend the convocation if we want to (hardly anyone does is what I have heard). We can do everything a regular ESCP MiM student can do. My request for future MDI-IB cohort to remember this fact.

5. Networking in Berlin/Germany

Now that the serious stuff is covered, I wanted to talk about the kind of opportunities available in Berlin, that you can explore and make use of. As you know by now, I specialized in AI-Big Data for Biz Innovation. I wanted a group with whom I could discuss stuff related to AI, where I could learn and so on. Fortunately, Berlin has a ton of such excellent student groups. BLISS[12] is probably the most serious, biggest student run society for AI and ML here in Berlin. They have a paper reading every week, they conduct an exceptional talk series every season (the summer series is going on now) with speakers from different universities, companies talk about AI/ML. They also conduct hackathons and such networking events as well.

Ofcourse, I am primarily interested in this, so I have found the AI ecosystem here. But Berlin is home for so many such groups and societies - for any topic/field you are interested in. Find the group, learn, make friends, build your network.

My repeated request to folks is not to limit yourself as MDI exchange students, or internship seekers who do not care about anything else apart from internship and final placement. You are here in Berlin/Germany for 8 good months, so make something out of it.

6. Conclusion

To conclude, this is how a typical spring semester is at ESCP Berlin, and particularly the AI and Big Data specialization. Focus is primarily on learning. Examinations are simple, the assignments are detailed and take some time but that is where the learning is. All 4 professors are actually big shots and one should be privileged to be taught by them and to have them in your network. So atleast respect that and do well if you take up this spec. Its a well-rounded specialization focused on different facets of technology & management.

Now a note for MDI-IB folks. At MDI, you’ll be given a list of specializations to choose from probably in the month of July (that is just a couple of weeks after you enter MDI). It was unfortunate that no one knew what each specialization entailed. Having a good description of each specialization would help students in decision making. But we did not have that. We were given a vague description of all the specs, which were of no use. I personally wanted to come to Berlin, and I anyway had a technical background and wanted to pursue something techno-managerial, so this course satisfied that condition. But I took it up just going by its name, without knowing in detail what the spec actually entailed. Obviously its not a good thing to do. I want folks to be well-informed about each spec before they choose it, and I believe I have done my bit towards that through this post. If you want to take this spec or any other, I would urge you to check for people on LinkedIn who have taken the spec you are interested in and talk with them about it.

Feel free to contact me if you have any questions about this spec, ESCP, ESCP-MDI or any queries in general. I will frequently update this article with time so that it has all the important information.

Cheers,
Adwaith

7. References

AI and Big Data spec official page: https://ent.escpeurope.eu/Syllabus/SylView/view/11767361/2
Prof. Markus Bick: https://www.linkedin.com/in/lehmanndavid
Prof. David Lehmann: https://www.linkedin.com/in/lehmanndavid
Prof. Javier Amaya: https://people.ucd.ie/javier.amayasilva
Prof. Daniel Pesch: https://danielpesch.com/
Business Law: https://ent.escpeurope.eu/Syllabus/SylView/view/11763322/2
Sustainability: https://ent.escpeurope.eu/Syllabus/SylView/view/11763321/2
Advanced Organization and Management - Managing Change: https://ent.escpeurope.eu/Syllabus/SylView/view/11763131/3
Prof. Peter Zaumseil: https://www.htw-berlin.de/hochschule/personen/person/?eid=3639
Prof. Carolin Waldner: https://escp.eu/waldner-carolin
Prof. Thomas Gigant: https://www.thomasgigant.com/
BLISS: https://bliss.berlin/