APDS: Intro to Advanced Python for MLOps and Data Science
APDS: Intro to Advanced Python for MLOps and Data Science, Introduction: Code as Data.
Course Description
This is Advanced Python for Data Science!
Today, most people enter the world of Data Science through the buzz and allure of “AI.” We tackle Kaggle challenges, voraciously consume Stack Overflow, and eat, live, and breathe through the Jupyter Notebook. Python, along with its “killer app” of Machine Learning, has done nothing short of revolutionize the way we “do data science,” and the world is a more interesting place because of it!
Most of the time, your impact as a Data Scientist is limited by your ability to enact your ideas – not by the ideas themselves. You can train a model on ‘clean’ data using Scikit Learn or FastAI, or run an ANOVA, in a notebook. Enacting that idea means getting to the data in the first place. It means knowing how to store it. It means processing your data at scale. It means running your processing script, reliably, every day on fresh data. It means testing that script. It means collaborating on that script with a coworker – or 10 – as the project scales. It means curating a library and building tools to solve the same problem for 5 new projects. It means packaging a model up for distribution – sharing with another data scientist, or deploying it as a service.
It means changing the way you think about problems by adopting new paradigms that accelerate you – and your work – across your organization. It means building an approach to data science within the broader python ecosystem.
This is a course about how to be an Advanced Data Science Programmer, leveraging tools and techniques from the broader Python ecosystem. In this introductory course of the Advanced Python for MLOps and Data Science series, we will introduce you to the HEROS concept, and focus on Higher-Levels of coding.
You will learn how to treat your code as data, which is one of the most important ways to leverage Higher Levels of coding, and understand why repetitive code is a good opportunity to refactor into two parts – the purely “logical” part of your code, and the details – or “data”. We will then deep dive into real-world examples and see how we can produce many different outcomes, whether there are reports, analysis or models – with just few lines of logical code.
So, are you ready to take your next step in the MLOps and Advanced Python for Data Science Journey?
Buckle up, and let’s get coding.
