I’m Pablo Aguirre, a self-taught Data Scientist based in Mexico City.

I hold a BSc. in Chemical Engineering from Universidad Iberoamericana. Although my current professional approach is less towards chemistry & processes and more towards data, I’ve always been inspired by natural sciences and their different roles in our daily life.

Motivation

This journey started a few years back when two subsequent random events came into place in the most interesting way:

  • First Event: I broke my only computer.
  • Part I: A university colleague introduced me to Linux. I remember very well because that same day, I tried my first Arch Linux install on a MacBook Pro, and it went so wrong I was left with an unusable machine for a couple of days. Regardless, I wanted to keep digging, so I did (also, it wasn’t like I had many options). What a naive way to approach things, right?
  • Part 2: I’m left with an ugly, barebones, incomplete Arch Linux install with no way to roll back to my previous macOS. Now what? Well, let’s customize it, shall we? Let us use this as an opportunity to learn about Linux and see if my computer can be used as a daily driver for academic work. Well, it was a steep learning curve, but everything is possible with the right motivation (I had to make it work before the end of the weekend). I ended up loving Linux. I not only managed to use my battered computer as a daily driver, but its performance increased drastically. I had the fastest laptop in the block.
  • Second Event: I was given the opportunity to participate in an academic project involving programming.
  • All systems ready, captain, now let’s learn Python: I started consulting multiple resources and became increasingly absorbed in finding new libraries. I was amazed at what could be done with GeoPandas, Numpy, Seaborn, and all those fantastic modules. Six months went by, and my part of the project was complete. This was only the beginning, though, because now I knew what could be done.

Since then, I have become utterly obsessed with open source and using new ways of creating technology because, in the end, technology is inspiring if done right.

I love data transformation and analysis as a professional endeavor. Still, I also love aesthetics & design, which is why I decided to create a visually enthralling yet informative personal blog with all the knowledge I absorb every day.

Last but not least, thank you for giving yourself the time to visit my blog and read all this craziness. It means a lot.

I hope you enjoy it as much as I did building it. Happy coding.

Trajectory

In chronologically descending order.

École Polytechnique Fédérale de Lausanne, (2022 – Ongoing)

A graduate-level program consisting of 4 full-semester courses, and one final Capstone Module.

Some of the topics include principles of functional programming, design of immutable data structures, functional programs using recursion, pattern matching, and higher-order functions, combination of functional programming with objects and classes, writing of reactive applications, design of functional libraries and their APIs, reasoning techniques for programs that combine functions and state, parallel programming, data-parallelism, data structures for parallel computing, Scala + Spark, reduction operations, and partitioning & shuffling.

(Projected Early 2024)

(Projected Late 2023)

(Projected Late 2023)

(Completed on Jul 2023)

(Completed on May 2023)

Universidad Iberoamericana, (2014 – 2019)

Design and study of chemical processes from a technical & industrial perspective.

Data Scientist, New Product Design (2022 – Present)

Member of the Global Data Science team in charge of designing and implementing new methodologies.

Commercial Analyst, (2021 – 2022)

Focused on covering a wide range of wheat commercialization processes for a diverse number of national and global accounts.

Financial Analyst, (2018 – 2019)

R&D Lab Analyst, (2017 – 2018)

Academic

Professional

École Polytechnique Fédérale de Lausanne, (2022 - Ongoing)

A graduate-level program consisting of 4 full-semester courses, and one final Capstone Module.

Some of the topics include principles of functional programming, design of immutable data structures, functional programs using recursion, pattern matching, and higher-order functions, combination of functional programming with objects and classes, writing of reactive applications, design of functional libraries and their APIs, reasoning techniques for programs that combine functions and state, parallel programming, data-parallelism, data structures for parallel computing, Scala + Spark, reduction operations, and partitioning & shuffling.

(Projected Early 2024)

(Projected Late 2023)

(Projected Late 2023)

(Completed on Jul 2023)

(Completed on May 2023)

Data Scientist, New Product Design (2022 - Present)

Member of the Global Data Science team in charge of designing and implementing new methodologies.

Universidad Iberoamericana, (2014 - 2019)

Design and study of chemical processes from a technical & industrial perspective.

Commercial Analyst, (2021 - 2022)

Focused on covering a wide range of wheat commercialization processes for various national and global accounts.

Financial Analyst, (2018 - 2019)

R&D Lab Analyst, (2017 - 2018)

Stack

Certifications in chronologically descending order.

Coursera / EPFL (2023)

Applying design principles of functional programs, writing simple functional reactive applications, designing functional libraries and their APIs, and understanding reasoning techniques for programs that combine functions and state:

Coursera / University of Michigan (2023)

Utilizing psql and SQL commands to implement CRUD (Create, Read, Update, and Delete) operations for tables in a PostgreSQL database, building and differentiating between one-to-many and many-to-many relationships within PostgreSQL, identifying and utilizing the functions of primary, logical, and foreign keys within a database:

Coursera / EPFL (2023)

Understanding the foundations of Functional Programming, designing immutable data structures, writing purely functional programs, & combining FP with OOP:

MITx (2023)

Principles, design & application of Machine Learning algorithms such as classification, regression, clustering, and reinforcement learning:

Udemy (2023)

The foundations of regular expressions with examples in Python, JavaScript, Rust, Java, C#, C++, Swift, Google Sheets, Kotlin and Ruby

MITx (2022)

Introduction to probabilistic models, including random processes and the basic elements of statistical inference:

MITx (2022)

An introduction to using computation to understand real-world phenomena:

MITx (2022)

An introduction to computer science as a tool to solve real-world analytical problems using Python 3.5:

Julia Academy (2022)

An overview of the Julia Programming Language:

Udemy (2022)

An overview of Apache Spark using DataBricks, PySpark & SparkSQL:

Programming Languages

Technologies

IDEs / Editors

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