This event was canceled
Data enables us to understand the world around us. Whether we're gathering data about our natural world to understand how it is changing, or analyzing patterns in how societies grow and change to ensure we're supporting all people, data is what drives the conversation. In this one-day workshop, we invite you to take the first step to learning how to understand data. With the power of Python, you will be able to explore data more quickly and develop more complex learnings from that data with just a few lines of code. Step 1: Learn the basics of Python coding and understand how it can be used to digest large data sets.
You do not need any prior experience with data science to attend this workshop. You are likely someone who is interested in data science, maybe have coded a bit before in any programming language, and are looking to understand the basics of the Python programming language and the libraries that help with getting data prepped for analysis.
You should bring your own laptop (Windows or Mac) with an Internet browser. You will be using Azure Notebooks, a cloud-based Jupyter Notebooks instance. All you will need is a Microsoft Account, which only requires an email address and for which you can sign up for at the event.
15min Introduction to Data Science
1.5hr Introduction to Python
45min NumPy: Your Local Data Friend
1.25hr Pandas are more than bears: How to import, clean, and store data
45min Data Science 1:1 Getting your Data Ready
30min Wrap Up and Next Steps
Susan Ibach, aka, HockeyGeekGirl is a high energy presenter. Her career has taken her from software development to database administration, from COBOL to . NET to Python, from app development to machine learning, from technical training at a startup to technical evangelism at Microsoft. Two common threads tie together her career: a fascination with data, and a passion to help others master new tools and technologies. She currently works as a consultant, but you might catch her presenting a session on machine learning at a conference, or in video tutorials such as Python for Beginners (https://channel9.msdn.com/Series/Intro-to-Python-Development).