PyCode Conference is a conference devoted to the Python language.
It is the meeting place for both experts and beginners.
The aim of the conference is to raise qualifications of participants and highlight the latest trends in Python. It is also important for us to spread good programming practices.
The conference is addressed to all Python programmers. It opens up to the community at every level of experience which means it is intended for advanced specialists as well as for beginners, including students.
Do you want to support our conference?
Want to be a sponsor, partner, speaker or to help with the conference organisation?
Please contact us: askamruk@pycode-conference.org
MVP Factory specialise in partnering with businesses, of all sizes, to explore digital business models and products, validating and designing industry-leading user experiences.
MVP Factory are experts in managing and executing full product builds, or specific components of a roadmap that need to be outsourced - always with a tech agnostic & agile approach.
MVP Factory can work in close collaboration with your internal product teams, or independently, with clear delivery milestones and a tailored approach to best suit the project.
Originally born and raised in France, moved to the UK to study Physics and Astronomy at the University of Sheffield. After working as a support astronomer at the Isaac Newton Group in La Palma for a year, she obtained her Masters of Physics in 2015. She subsequently started a PhD studying the 3D shape of Core Collapse Supernovae, and earned her title in Spring 2019. She joined the University of Auckland as a Research Fellow to research the evolution of massive stars to better understand how they die and produce Supernovae and Kilonovae.
Started public outreach work during her doctorate studies, in early 2016, and have not stopped since.
One of the great powers of Python, and one of the reasons it has become the most used language in astronomy, is that it is open source. A large ecosystem of mathematical and data analysis packages already exist, but some science needs simply cannot be filled by big packages with large audiences. But just because your application is niche does not mean you should shy away from sharing it with the world - especially in a job where reproducibility is everything. As a computational astrophysicisit who uses and develops some of the nichest python packages, I will present my perspective on the fundamental role that open source has to play in making better science.
Abigail is an IT professional from Accra in Ghana who loves programming and the power that it can put in people's hands - especially those people who have less power of their own to begin with. She wants to help do more of that, and is confident that the Python community can help achieve it.
Abigail is the lead of PyLadies in Ghana, a recipient of a PSF Community Service Award and she has served on the organising teams of both PyCon Ghana and PyCon Africa. She believes that women, technology and education need to come together as strongly as possible, and will do whatever she can to help create a future in which they do.
Python in Africa has brought a lot of enthusiasts and pythonistas together. People are either working on a project or giving back to society by helping other members of their community. In this talk, I will touch on how Python has brought us together to work on a common goal; Building one of the most amazing Developer Communities in the world.
I'm a PhD Scholar at the Indian Institute of Technology (IIT), Kanpur, India. My interested domains are Cyber Security and AI/ML. I have accomplished MTech specialized in cyber security from Defence Institute of Advanced Technology, Pune, India and BTech in computer science from the Biju Patnaik University of Technology, Orissa, India. I have two Springer publications in the cybersecurity domain, i.e. First paper entitled "A Survey on Machine Learning-based Ransomware Detection" is in the process of publication, and the Second paper entitled "Leveraging Machine Learning for Ransomware Detection" has been accepted for publication.
This talk will discuss offensive and defensive parts of cybersecurity and how Python is helping both sides of the coin, i.e. attackers to make high-level, sophisticated attacks and defenders to develop more robust and flexible solutions. Python has numerous libraries, which helps attackers and defenders to achieve their target, and we'll discuss some of these libraries in this talk. Attackers heavily use Python to achieve their targets like execution, persistence, privilege escalation, and defenders use for their targets such as decoys, system, and network monitoring.
Saket Upadhyay is a security research intern at Uptycs and a cybersecurity and digital forensics student at VIT University. He has worked in the field of penetration testing and vulnerability assessment with reputed firms and won multiple international awards and holds the "Best Paper Award" from IEEE Big Data Conference 2019 for his research contribution on the framework for malware detection with multiple ML models. His current research area includes malware reverse engineering, threat analysis and adaptive cyber defence strategies.
Wanna see how we can make life easier by automating malware and threat analysis using python? In this presentation, we will discuss automating memory-based malicious pattern scanning using Yara in the Linux process. We will learn how we can logically achieve similar automation and how to distribute the processing load/overhead using multithreading in Python3. After this discussion, you should be able to use Yara, threading, Queue and pathlib libraries in Python3 to help you in your next automation project!
Jade Abbott is the Machine Learning Lead at Retro Rabbit in South Africa. She has an MSc Computer Science from the University of Pretoria and works as a software engineer across Africa in every field from fintech, to NGOs, to startups. Currently, she trains and deploys deep learning systems to perform a variety of tasks for real world systems. In 2019, she co-founded Masakhane, an open research grassroots natural language processing initiative for Africans, by Africans, which aims to spur research into NLP for African languages, currently boasting over 400 members, from 38 African countries, and 13 affiliated publications.
93% of data science projects never make it into production. Many of us in the field of AI and Data Science feel the impact of this — the sheer futility of all our efforts.
First I talked about how we ended up in this mess. Spoiler Alert: We were misled. Next, I provided my opinion on how to get OUT of it. It's time to put down those neural networks, and pick up some data governance tools. It's time to stop hiring data scientists and instead hire data engineers.
Join me on a journey to go back and rework some of the foundations so that our Data Science efforts can begin to make an impact. This talk is complete with Wardley maps, sparkle GIFs, and Ru Paul memes.
What you get out of it:
Claudia Comito got her PhD in astrophysics in 2003, bounced around from high-altitude telescopes to space missions for 15+years, and finally settled at the Jülich Supercomputing Center in 2018. She co-develops the Helmholtz Analytics Framework (Heat) library, and supports scientists coming to grips with their data having grown "too big to numpy".
Python is the most widely used programming language both in natural sciences and in data science. This supposedly shared skill has led to a whole new set of career paths for scientists seeking to venture out of fundamental research, but the switch from "science-science" (warning: irony) to a computer-science-dominated field is not without pitfalls.
Through the lens of my own Python Pivot from astrophysics to developing Heat, I'll attempt to reconcile two quite different mindsets, reflect on under-the-radar hurdles, and hopefully convince you that "cutting yourself some slack" is as fundamental a skill as "problem-solving" or "analytical thinking".
I'm equally interested in music and software engineering, as a classically-trained pianist and a long-time contributor to the Python programming language.
I build software for musical instruments. I make music as RPLKTR. I'm also the Release Manager for Python 3.8 and 3.9. I created the popular code auto-formatter Black.
Ex-Facebook. Currently based in Poznań.
Jeeta is a quantum enthusiast and has a background in Physics. She has completed her Undergraduate and Master's degree from Jadavpur University. Being passionate about programming, she is planning to pursue her research in the field of Quantum Computing for which she has been taking up courses like the QBronze diploma. With her ardent love for teaching, she mentors high school students in Physics, Mathematics, and Computer Science as a freelancer.
We will be building a random number generator using variational quantum circuit. While a very basic random number generator can be created using a simple quantum circuit, it turns out it has some serious limitations, like the very limited number of unique random numbers generated, and the qubits requirement. So we propose a variational quantum RNG, where we have tried to use a varying parameter to generate a list of unique random numbers. We found that with the variational RNG, we were able to generate the required list of unique random numbers, and with a lower qubit requirement, executing the circuit with only two qubits.
This talk will be based on a team project: Jeeta Jaishikhaa Ghosh (Speaker), Adrija Goswami, Harshdeep Singh.
After spending 5 years doing computational research in Physics, Cheuk has transferred her analytical and logical skills in natural science and built a career in data science. Cheuk has been a Data Scientist in various companies which demands high numerical and programmatical skills, especially in Python. To follow her passion for the tech community, now Cheuk is the Developer Relations Lead at TerminusDB - an open-source graph database. Cheuk maintains its Python client and engages with its user community daily.
Besides her work, Cheuk enjoys talking about Python in personal streaming platform and MidMeetPy podcast. Cheuk has also been a guest speaker at Universities and various conferences. On top of speaking at conferences, Cheuk also participates as organizers. Conferences that Cheuk has organized include EuroPython(which she is a board member of), PyData Global and Pyjamas Conf. Believing in gender equality, Cheuk constantly organizes workshops and mentored sprints to support Tech Diversity and Inclusion. In 2021, Cheuk has become a Python Software Foundation fellow.
Storing data in a tabular format is not always ideal. Taking advantage of strong data in knowledge graphs can make handling complex data structure possible and data visualization easier. In this workshop, you will get all the basics to start modelling data in the terms of triples and building schemas of a knowledge graph.
By the end of the workshop, you will be able to think like a knowledge graph expert and construct a proper schema to store your data in a knowledge graph format. You will acquire the skills that you need to build knowledge graphs in TerminusDB - an open-source graph database that enables revisional control and collaborations.
Nitesh Funde received the B.E. degree in information technology and M.Tech. in computer science and engineering from Nagpur University, Nagpur, India, in 2011 and 2014 respectively and Ph.D. degree in computer science and engineering from, Visvesvaraya National Institute of Technology (VNIT), in 2020. He is currently a Postdoctoral Researcher with the Soongsil University, Foundation of University-Industry Cooperation, Seoul, South Korea. He is associated with VIT-AP University as an Assistant Professor. He was working as a Lab Engineer with the Centre of Excellence for Commbedded Systems, Department of Electronics and Communication Engineering, VNIT where he was involved in research projects of national importance. His research interests include Smart Grids, Energy Management, Data Analytics and Internet of Things. He uses Python and MATLAB for his research.
Advanced Process Monitor (APM) is optimization software for mixed-integer and differential algebraic equations. It is coupled with large-scale solvers for linear, quadratic, nonlinear, and mixed integer programming (LP, QP, NLP, MILP, MINLP). Modes of operation include data reconciliation, moving horizon estimation, real-time optimization, dynamic simulation, and nonlinear predictive control with solution capabilities for high-index differential and algebraic (DAE) equations. The APMonitor modeling language is a high-level abstraction of mathematical optimization problems. Values in the models are defined by Constants, Parameters, and Variables. The values are related to each other by Intermediates or Equations. Objective functions are defined to maximize or minimize certain values. Objects are built-in collections of values (constants, parameters, and variables) and relationships (intermediates, equations, and objective functions).
I learned Python and Linux administration by myself. I work with gigabytes of data and often use Python to make sense of it. I enjoy writing code myself, allowing to look closer on any dataset to better know it, to properly maintain it and to safely upgrade it. I'm SeeM on liberachat. If you have a minute, I don't mind a little talk.
Python offers quick and mostly easy conversions between typical data structures: xml, csv, json ans sql. In my daily work, I often have to analyze barely readable datasets, preferably without mistakes. I turn Bash script output into json, xml into csv, or huge ElasticSearch results merged with csv exports from public databases into one, coherent set of data. Here is how I do it.
Currently working as Strategy Consultant for SOYNET | MBA from Seoul National University | B.E (Electronics and Telecommunication) University of Mumbai | Technical Lead at Microsoft (2016-2020) | Experience in Software consulting| Areas of interest revolves: Artificial intelligence especially deep learning in computer vision.
The purpose of this talk is to inform the participants about the industry problems and their experiences with the ML models developed by AI engineers. Technologies & solutions used currently to bring the AI into production with optimum accuracy. Present used cases by our company using different techniques to bring up the speed and consume less memory by ML models in real time.
Currently enrolled in the regular PhD program at Centre of Computational and Data Sciences (CCDS) in Indian Institute of Technology, Kharagpur, WB, India; under the supervision of Prof. Sabyashachi Mishra and Prof. Sonjoy Majumder. Area of research: broadly Quantum Computation and Variational Quantum Algorithms (VQAs). Harshdeep Singh is highly interested in Quantum Mechanics and Quantum Chemistry. He is technically from a Physics background, having done his BSc honours and MSc in Physics from Jadavpur University, Kolkata, India.
Quantum Computation is all the hype right now in the research world, with its applications in various fields like data science, machine learning, finance, chemistry, optimisation and many more. But before we can dive into the advanced and complex topics in quantum computation, we need to understand the basics. In this tutorial, I will present the basics of quantum computation, starting from the description of qubits, and how quantum computation differs from the classical computation by considering the example of a coin toss, a 'classical' coin and a 'quantum' coin. I would then discuss some other concepts from quantum mechanics that form the basis of the quantum computation in general, while using the Qiskit package in Python side by side to give this tutorial a more hands-on outlook. I will use Qiskit to demonstrate all these concepts like the coin toss, quantum states superposition, the application of quantum gates on qubits, ending with a brief introduction to quantum entaglement; teleportation, which for many of us still seems as a fancy concept used in sci-fi movies; and superdense coding.
Shashwat Raj has completed his graduation this year in B.Sc (Hons.) Computer Science from Sri Sri University. He is a Data Science enthusiast and is working as a developer in a reputed IT firm.
Shashwat has keen interest in Computational Intelligence, Cognitive Science and Data Science. He is presently working on ONIST tools development for Threat Intelligence Analysis and Visualization.
Ever wondered, a program imagining and generating new things?? Seems like a science fiction movie but its the reality now. We are already in that phase where this is possible and happening. There are Neural Networks that generate images, audio, ideas and simulations.
In this session I will discuss about Neural Network and its working. The mathematical concepts behind it and its implementation in Python3 using Numpy and PyTorch. From there we'll take a leap into the Generative Modeling and demystifying how these Neural Network can generate or imagine new things. We will also be creating a hands on project on GANs using PyTorch.
Malayaja Chutani is a PhD student in the Department of Physics at the Indian Institute of Technology Madras. Her research is focussed on studying networks and dynamics on networks by considering higher-order links and interactions. She is currently racing to finish her thesis. Apart from research, she likes teaching new tricks to her dog and watching movie reviews. If you have cool ideas that integrate data science and network analysis, she would love to talk to you!
Networks are all around us, like networks of friends on Facebook or professional contacts on LinkedIn, networks of roadways or flight routes, and networks of neurons in our brain. Networks are collections of a large number of agents (like people, cities, or cells) that are connected to each other in interesting ways. The structure of and dynamics on networks can tell us a lot about the overall system, like who to tell a juicy piece of gossip if you want it to spread fast, or which citizens to vaccinate first in the event of a pandemic, or what collective synchronization of brain cells can mean for our health.
This workshop will introduce the theory behind networks and network models, with the help of NetworkX, a Python package used for network analysis. We will understand important network metrics, and finally analyze a few real world networks.
A graduate of the Koszalin University of Technology in the field of Computer Science. The owner of the Code Squat company where she works as an organizer and programmer.
She also works in the Association of the Polish Python User Group, where he is involved, among others, in workshops for children and adults on operating Micro: bit devices.
Software engineer since 2009. Experienced Symfony and Ruby on Rails apps developer. OSes, robotics, new technologies, space architecture and exploration passionate.
IT events organizer, community builder, technical speaker & mentor since 2013. Professional ICF (in the making) career change coach. Women in Technology Cracow 2014 founder.
Dr. Ajit Kumar is a Post-doctoral researcher in Soongsil University, Seoul, South Korea. He has completed his Ph.D. from the Department of Computer Science, Pondicherry University in 2018. His Ph.D. thesis titled "A Framework for Malware Detection with Static Features using Machine Learning Algorithms" focused on Malware detection using machine learning. He is working with Python since 2012 for his research work and other development work. He is also interested in web development, Information security, and Data Science. Python is his language of choice for all the programming related tasks. He has been motivating and training students to adopt Python as his programming language. He loves to write and share the article about Python and its applications.
First-year Ph.D. student in applied mathematics from the School of Fundamental Sciences, Massey University. His research is on dynamical systems and robust chaos. Indranil has a master's in Physics from Jadavpur University. He is mostly interested in dynamical systems, computational mathematics, optimization, soccer data analysis, etc. He is very fascinated with open source software development and write codes mostly in Python and R, and sometimes Fortran. Maintains an open source book about Mathematical Optimization using Python.
Nowadays data is the key to solving challenges in most fields, say astronomy, applied mathematics, health care, or even sports. Data is everywhere and it is super beneficial to leverage these data and build models for making sense out of them and telling stories. The field of sports, especially football (soccer), is enriched by data analysis that makes us understand the game better and predict outcomes. Many people want to delve deep into football (soccer) data analysis and get their hands dirty. This workshop of around one hour is to help them do so by pedagogically introducing them to the introductory analytical methodologies to overcome the initial barriers of the field and start working with data analyses and visualizations on football data hands-on to deduce interesting results.