Ever wondered how hardware is made, or curious about making your own? 

We share our experiences manufacturing a programmable gamepad for use in IoT/MicroPython workshops. 

We will cover the entire production process, including:

- Designing the PCB (Printed Circuit Board)
- Choosing microcontroller and parts
- Finding, ordering and assembling components
- Pulling together firmware, drivers and software

Mistakes were indeed made along the way. Let's turn them into valuable lessons!

Ever wondered how hardware is made, or curious about making your own? In this talk we go all the way!

Sebastian Roll

From circuit board design to finished product: the hobbyist’s guide to hardware manufacturing

A lightning talk (LT) is a short presentation that must not be longer than five minutes.

To sign up for a lightning talk, you can put your name on the information board during the conference before the second coffee break. For our online participants, we will set up a separate form or Google sheet for you to put your name and topic in - similar to how we run this at the in-person conference. 

We will announce the same every day both online and in person.

Lightning Talk Lightning Talk Lightning Talk Lightning Talk Lightning Talk Lightning Talk Lightning Talk Lightning Talk Lightning Talk

Signing up is on a first-come-first-served basis. The queue is reset every day in the morning.
You may talk about / present (almost) everything with certain boundaries:
- no promotion for products of companies, no call for 'we are hiring' (but you may name your employer).
- conference announcements are limited to one minute only
- one LT per person per conference policy: everyone who has not given an LT at this year's conference, yet, is ahead of you in the queue
- keep it short, the shorter the presentations, the more people can give a talk

See you there and have fun!

Friday Lightning Talks

Computational methods particularly those involving data analytics are now taking root in various humanities disciplines. However, students and researchers working in these disciplines lack the necessary programming proficiency and coding experience . The need then arises to make Python-based computational methods accessible – we present case-studies of how to do this via various Python modules being taught at College of Business in Technological University Dublin and by means of walkthrough of an interdisciplinary social good project called InEire. It comes down to complementing existing quantitative and qualitative methods with methods based on analysis of various types of data specific to the social science problem being solved. We essentially go through the process of building curiosity-driven exploration in social science students via a theoretically driven research question rather than the Python technique itself, and then focusing on the various steps involved in solving that question; and finally boiling it down to a concrete Python-based data analytics methodology. This project-based teaching methodology helped us develop Python skills in newbies eventually leading to a Python-based data analytic skills in students of disciplines other than Computer Science.

Join us for an interactive step-by-step approach to teaching Python for arts, humanities and social sciences in an attempt to bridge divide between data scientists and policymakers

The various areas within humanities and social sciences such as political science, sociology, psychology, economics etc. have evolved to a point where they have been complementing existing qualitative and quantitative methods with methods rooted in data science. This shift in paradigm is primarily driven by real-world, publicly available data sets that cover a variety of scholarly domains and have the potential to solve fundamental research questions in these intriguing fields. There is however a huge bottleneck to be overcome before realizing the full potential of data science in arts, humanities and social sciences; and that bottleneck relates to a fear of programming in students/researchers within these disciplines. Our talk presents some tips and tricks from course modules being taught in Technological University Dublin; the fundamental idea is to present an overview of data science toolkit and how it relates to problem solving in the real world. 

First we will present ways to make the data science lifecycle being made easy via tools such as Google colab and Jupyter notebooks followed by explaining how showing students the big picture and the workflow lifecycle of a data science technique helps grasps concepts in a very effective manner. We will present examples from exploratory data analysis and classification using data-driven research questions; and look into elegant solutions that can easily be plugged into a social scientist’s skillset. Examples of continuous assessment projects based on different Python libraries are also presented with a view to further establish use of Python as a valuable tool for arts, humanities, and social sciences.

Finally, we will give an overview of an Irish research council funded project emanating from a combination of STEM and HUMANIITIES disciplines that aims to perform an economic assessment of anti-immigrant sentiment in Ireland. Various phases of the project will be explained emphasizing particularly those where Python tools play a major role in interpretion of research outcomes. These research outcomes obtained through smart use of Python data analytic tools play a key role in building relationships between data scientists and policy makers in both government and not-for-profit sector.

Arjumand Younus

Dr. Muhammad Atif Qureshi

Python for Arts, Humanities and Social Sciences

Diving into the CPython source code can feel daunting. Whether you want to start contributing or just want to get a better understanding of Python by exploring its source code, it’s often difficult to know where to start or what you’re missing.

In my talk, I will show you around the CPython source code by implementing a new operator, a pipe operator. While doing so, I will discuss core parts of the internals, such as Python’s grammar, its syntax trees, and the underlying logic that will perform the operation. By the end, you will have a good idea of the moving parts involved in core language features.

I will also take you through the steps necessary to make it all work. I’ll show you how I obtained a copy of the source code, regenerated the parser and token files, and how I compiled my modified version of CPython. I will also write and run tests to help me implement my changes. This should give you a mental framework that helps you while diving into more comprehensive resources, like the excellent [Python Developer’s Guide](https://devguide.python.org/).

My talk is aimed at everyone who wants to explore CPython’s internals. You don’t have to be an expert in Python, although some affinity with Python helps with understanding the internals. I will also use C to implement some of the operator logic, but knowledge of C is by no means required. In short, if you’re interested in diving into the CPython source code, this talk is for you.

Always wanted to dive into Python's source code, but felt overwhelmed? Join @SebastiaanZeeff as he takes you through CPython's Internals by implementing a new operator.

Sebastiaan Zeeff

Demystifying Python’s Internals: Diving into CPython by implementing a pipe operator

Have you heard of the critically acclaimed MMORPG Final Fantasy XIV?

As an active player since 2015, I've used my "problem-solving programmer brain" to analyze my experiences in the world of Eorzea and apply them into important software lessons. From finding solutions to a housing crisis, to tracking cheaters, to networking with the president of Square Enix and applying the principles of (Y)MINASWAN, there's a lot to be learned through triumphs and failures as an MMO gamer. I will also talk about my experiences in the software community as a neurodivergent developer, and how gaming helped me break down barriers.

The journey of a neurodivergent developer who used gaming to help her navigate the software industry

This talk will have special meaning for Final Fantasy fans, but anyone with nerdy non-coding hobbies should be able to enjoy it. The intended outcome is for attendees to see their own hobbies in a new light, where they can find their own abstract lessons.

MMO gaming is becoming a more mainstream hobby, and as a lifelong gamer, I have plenty of experiences and stories to share on how the genre helped my transformation from a shy bundle of nerves to a confident professional. I also have experience applying my problem solving skills from software development into solutions for the community to combat problems that arose within the game.

Valerie Shoskes

How a popular MMORPG made me a better developer

In my talk, we explore our usage data requested according to GDPR and leverage it - together with Spotify’s Web API - to build a personalized playlist recommender system with Python.

In 2018, the General Data Protection Regulation (GDPR) became effective in the EU. It sometimes causes data scientists great headaches. But from a consumer and Pythonista point of view this can also be interesting data for exploration. It is very useful for building personalization technology, in particular recommender systems. And there are almost endless ways to use Python for it.
So, let’s request and use our own data to build a playlist recommender system which infers our music taste from our streaming history and uses it to retrieve songs from our favorites in a new way. We will call it “Your Rediscover Past”, a personalized playlist based on your streaming history and saved songs.

In my talk, we explore our usage data requested according to GDPR and leverage it - together with Spotify’s Web API - to build a personalized playlist recommender system with Python.

Personalized Playlist Recommendations on Spotify are great – some of them let us discover new songs, some others help us to rediscover songs. However, rediscovery seems to be limited on the more recent past, i.e. going only a month backwards. This is a problem if you like to rediscover some of your favorite songs you might have listened to a longer while ago. Sometimes we add them to our "liked songs" where they likely fade away. However, you once explicitly declared those tracks as favorites. So, what is it that we can do about this missing piece in personalized playlist recommendations?

Well, the first thing we do is to request our personal usage data from Spotify according to GDPR. Second, we analyze and enrich it with track audio features offered by Spotify’s rich Web API. We derive the music taste profile of ourselves from 12 months of streaming history and use this taste profile to retrieve favorite songs we haven’t listened to for more than a year. In my talk, I present you the Python package I build for this purpose, possible extensions and enable you to create your own personalized playlist to rediscover your past!

Marcel Kurovski

Build your own Playlist Recommender System with Python using your GDPR Data

Things rarely go as planned, especially in the world of programming. Errors are the bane of a programmer’s existence. You write an awesome piece of code, are ready to execute it and build a powerful machine learning model, and then poof. Python throws up an unexpected error, ending your hope of quick code execution.

Things rarely go as planned, especially in the world of programming. Errors are the bane of a programmer’s existence.  So, lets look at a graceful way to handle them.

In the process of programming, we are always going to encounter various errors. 
Things rarely go as planned, especially in the world of programming. Errors are unavoidable when writing code, which can be frustrating at times. Every single one of us has faced this issue and emerged from it a better programmer. Dealing with bugs and errors is what builds our confidence in the long run and teaches us valuable lessons along the way.
So, in this talk, we'll discuss different ways of handling errors and making our lives a little better. 
We'll talk about how code written with effective exception handling strategies can help us to catch bugs early in the software developmental cycle.

From circuit board design to finished product: the hobbyist’s guide to hardware manufacturing

Abstract

The speaker