I made a package, pyplot-themes, that helps make it easier to: 1. have decent looking matplotlib/pandas plots 2. have some decent color palettes 3. create your plot themes https://pypi.org/project/pyplot-themes/
We will talk to you about Nielsen's Connect Platform, our global, unified, open data ecosystem powered by Microsoft Azure and how we're building platform components using Python. Specifically, we'll deep dive into object-oriented data flows. As more and more data scientists write software beyond statistical models, object thinking from the field of programming can help them write test-able, maintainable and reusable components.
Machine Learning is something you'll see referenced very frequently now in everything from marketing materials to sales pitches, and job postings. With so much hype it can be hard to distinguish what people mean when they say Machine Learning. In this talk we will demystify Machine Learning by understanding its core concepts and applying that knowledge to real world examples. We'll explain basic concepts like linear algebra and loss functions, figure out when to use machine learning and build an ML model that we'll be able to use in real world apps. Here’s an in-depth list of what we'll cover: * What Machine Learning is and where it’s being used * How to recognize when machine learning is necessary * Math 101 * Linear Regression * Live Coding Session Salary Estimator * Q & A
Come learn about the new features in Python 3.8!
A chance for our community to remember and celebrate the life of Tanya Schlusser. Tanya has a long history at ChiPy and beyond. She was a mentor, a speaker, a writer, an education advocate, a loving daughter, and much much more. Members of the community will be invited to share their memories of Tanya.
Python is a growing choice for business applications processing sensitive user data and performing mission-critical tasks. That makes it vital for programmers to be aware of common security vulnerabilities that can undermine the Confidentiality, Integrity, and Accessibility of these Python applications. Fortunately, many of these risks can be managed with patterns for safe handling of user input as well as tools for dependency monitoring and static code analysis.
We will be preparing the famous XOR example or one of the staples of non-linearly separable feature spaces. We will use the classic techniques like tensorflow and keras. We will also check out some of the newer examples like caffe, mxnet, pytorch, deeplearning4j, and many others. Aprons will be provided. No prior experience cooking necessary.
Here we will go through my own personal saga of adding documentation to the Python Man pages.
We are all on a Python learning path somewhere. For some, finding the right path to set out on is hard; others have been on the trail so long we forget to look around at our surroundings. In all cases, we should be mindful about how and what we learn In this talk we'll cover tips and tools for each stage in your development as a Python programmer including some live examples. How to set goals, how to find mentorship experiences, how to grow technically and how to grow interpersonally. Something for everyone!
I first encountered SQLAlchemy several years ago. I didn't get it. It seemed every line I attempted to write would drop me into 50 tabs of labarynthine documentation. Why do we have the ORM *and* Core? Should I build my tables as `Table` instances or should I be extending `Base`? How is `Base` more declarative than a function that returns `Table`s?? Can I please just write SQL??? :sob: I'm still hesitant to peek too far behind the curtain, but I do think I've finally wrapped my head around the philosophical underpinnings of the library and the different problems SQLAlchemy allows us to solve. After all, who among us works with databases that aren't problems in and of themselves?
Katie Simmons, a data engineer at ActiveCampaign, will speak about the challenges and benefits of using Airflow for ETL at a rapidly growing company. ActiveCampaign has many thousands of databases - some including tables with up to a trillion rows - several APIs and new source requests coming in every week. This lightning talk will be an overview of using Airflow to extract, load and transform that data into our data lake so that it can be used for Business Intelligence and Data Science.
One of the most common languages used by Python developers is some shell script (sh, bash, cmd.exe, or PowerShell), but why torture yourself with poor design decisions from the 70s when you know Python?