aoml/other/Electives Day Presentation.md

- On 16/11/22, there is going to be an "Electives Day" where we will present our electives to the MSc students so they are better informed before they can make their choices.
- It's my time to shine. There are few students, so if not enough of them enroll in the course, they will cancel my course.


Agenda to cover:
- Present myself
	- Education
	- Experience
	- The weird bits
	- Open source my evaluation
- Present the course
	- Funny example
	- Contents
	- Purpose
	- Methodology
- Provide contact
	- QR code to my email
	- QR code to my evaluations

---


# Message for the students

Hi, my name is Pablo and I'm the teacher in charge of the course Practical Data Science for Operations. The goal of this text is to seduce you into taking my course, Practical Data Science for Operations Management. 

Let me begin by reminding you that I'll be very happy to answer any questions you have. Just send them to me through an email. I sincerely believe it's much more interesting for me to answer your doubts than to simply provide a generic course description. Nevertheless, you can find some info below to give you some starting details and hopefully spark your curiosity.

## About the Course

Imagine you are the Chief Operations Officer of Beanie Limited, a coffee roaster and dealer. Your company operates in Europe. Your supply-chain looks roughly like this:
	- You import coffee beans from Brasil and Colombia. Your two procurement managers send the raw beans by ship from those locations to different docks in Europe.
	- The coffee beans land in your European Distribution Centers. Some of them will be sent to several of your Regional Distribution Centers, and some of them will be sent to your Roasting Facilities. 
	- In your Roasting Facilities, the beans will be roasted and blended into different possible consumer facing mixes and sent again to Distribution Centers.
	- All of your Distribution Centers serve orders of your customers, which include retailers, horeca chains and other smaller regional coffee roasters. Different types of products are served (wholesale raw coffee beans, wholesale roasted coffee beans, different brands of retail coffee SKUs, etc).

With all of this footprint, there is *a lot* to manage:
- How do you transport the goods between the different levels of your network? When, how much, and to where do you send your goods?
- What should be your stocking policy at each layer of the network? And at each node? Should they be managed independently or should you have a common strategy?
- How will your organize your limited Roasting infrastructure? Today, should you roast coffee A or coffee B? How much of it?
- Should you open or close locations in your network? What impact would that have on cost and performance of your supply chain?

This course is a practical trip throughout solving this kind of problems in realistic contexts. I like to think your theoretical-oriented courses in Mathematics, Statistics, Operations have been your driving theory classes and this course is going to be your first actual driving practice. Once you graduate, you will get to actually drive on the road on your own.

The course is mainly driven by a series of cases where several operations challenges will be presented to you. You will act like consulting teams that need to address them, with both business and technical lenses on. The lectures bring you the theory and methodologies you need to solve them.

The course is dramatically practical, as the title implies. We will simulate consulting scenarios in real companies. You will be tasked with realistic challenges and will have to report back to simulated personas with your results. You should expect practical work every week. It's a course where you learn mostly by solving problems and a bit by listening to me. The course is also quite technical. That means a lot of new stuff related to coding, and probably a bit of refreshing your statistics courses. We will have a mini-course in the second semester for anyone that has never coded to get up to level. You should expect a lot of Python coding, which is great because it's a skill that you will find very helpful out there. For the actual contents: we will cover interesting topics such as Machine Learning, Optimization, Metaheuristics or Simulation. From a functional point of view, we will use these techniques in contexts such as demand forecasting, inventory planning, production scheduling or last-mile logistics optimization. 

You can also expect a lot of tips and stories about how doing all of this in industry is like. I can also provide your first hand information about the job market for this field, as well as a few ideas on potential companies to land at in Barcelona.


## About myself

If I'm supposed to teach you, I think you have all the right to know what my credentials are.

I'm an adjunct professor here at UPF. That means I'm not in an academic path, but rather I work in industry, and I only provide courses in UPF in my area of specialization. This affects a lot my teaching method, and you will probably notice I do things a bit differently than most teachers. An example is taking a practical first approach.

Currently, I work at a startup called Lola Market as a Data Engineer, where I build infrastructure to create data-driven products within the company. Our business is delivering groceries to your home, and my biggest fetiche is field operations management where we try to make our shoppers (the people who do the shopping and driving for you) as efficient as possible. Before Lola, I have held a variety of positions in consulting, research and private companies. A couple of important highlights are Accenture, where I was a Data Scientist specialized in Supply Chain and Operations, and the City of Amsterdam, where I performed Data Science research in the area of public parking.

As for my education, I hold a MSc in Data Science from the University of Amsterdam, and a Bachelor's Degree in Business & IT from the Autonomous University of Barcelona. I also did course work in the Technical University of Munich during my Bachelor's.

Additionally, I have several others areas of interest. These include:
- Open source software. You can check my [Github repository](https://github.com/pmartincalvo/).
- Bitcoin and Austrian Economics. I have my own Bitcoin node at home.
- Electronics and micro controllers. I'm currently trying to build an automated smart watering system for my plants at home.

Finally, you can find how students have evaluated me in my last course in [this file](![[qr_github_upf_evaluation.png]]). These scores are usually kept private by the teachers but I'm more than happy sharing them with you so you can transparently know how other students have judged me.

Looking forward to your questions. Thanks and best regards,

Pablo


Now, that was my brief introduction. I expect you, being an curious and smart bunch, to have plenty of questions. Who wants to start?



Thanks for coming. Please, don't hesitate to get in touch if more questions comes to your mind later and I hope to see at the course.




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# Agenda for the presentation

My agenda above has been completely nuked by the fact that I will only have 5 minutes to present the course. Here is my hypercompressed version that I will do with 

- Hi guys. 5 minutes is very little time to explain a course properly, so I won't do that. 
- Instead, please begin by opening your phones and getting ready to read a QR code.
- Instead, and first of all, this is my email. Please, if you have more curiosity about this course, just send me an email saying hi and I'll forward you a written text that explains more to help you decided.


- About the course:
	- The course is dramatically practical, as the title implies. We will simulate consulting scenarios in real companies. You will be tasked with realistic challenges and will have to report back to simulated personas with your results. You should expect practical work every week. It's a course where you learn mostly by solving problems and a bit by listening to me.
	- The code is partly technical. That means a lot of new stuff coding, a bit of refreshing your statistics courses. We will have a mini course in the second semester for anyone that has never coded to get up to date. You should expect a lot of Python coding, which is great because it's a skill that you will find very helpful out there.
	- For the actual contents: we will cover interesting topics such as Machine Learning, Optimization, Metaheuristics or Simulation. From a functional point of view, we will use these techniques in contexts such as demand forecasting, inventory planning, production scheduling or last-mile logistics optimization. 

- Nevertheless, since I still have 4 minutes, let me explain you a few highlights on the course:

- About me in 5 cents:
	- I'm Pablo. I'm an adjunct professor. For those not familiar with the naming, that means that my main job is not to be a professor. I work in industry in private companies. The stuff I teach you in class is stuff I do in my job for a living. I currently work as a Data Engineer in a startup called Lola Market. Previously, I was Data Scientist specialized in the area of Supply Chain and Manufacturing at Accenture. I hold a MSc in Data Science from the University and a Bache
- And we are out of time. Again, if you have any curiosity at all about the course, please just shoot me an email saying hi so I can send you my little text. You will make my day. Thanks for your time, good luck with your ongoing courses and see you soon.



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# QR codes

Email ![[qr_upf_email.png]]


My last course evaluation ![[qr_github_upf_evaluation.png]]