This series on Channel 9 and YouTube are designed to help you, a developer, begin your journey through understanding data science and machine learning concepts, while providing you with a practical guide to your first data steps. Whether you want to learn how to actually apply data science and machine learning to the technical solutions you're building, or just want to be able to have more effective conversations with the data scientists or machine learning experts on your team, this series will give you that initial insight and hopefully spark your interest to learn more.
We do assume you are familiar with software development, however the majority of the code shown in this series is relatively straight-forward Python. So even if you're a novice developer, you will likely learn something for your level. We also assume that you have some familiarity with Azure.
If you're not familiar with Python, we recommend these video series:
If you're not familiar with Azure, we recommend checking out these Microsoft Learn modules:
Remember, Azure offers free trials:
- The basic data science lifecycle
- An overview of common machine learning algorithms
- How to setup your local and cloud developer environment to:
- Write Python in Jupyter notebooks in Visual Studio Code
- Connect an Azure Machine Learning resource to your local Visual Studio Code environment
- Write Python in Jupyter notebooks in Azure Machine Learning Studio
- How to import, clean, and manipulate data with Python
- How to split data and test machine learning algorithms in code
- Some software development experience: Built an app? Great! Completed a coding course or bootcamp? Perfect!
- Light experience with a programming language: Python, or really any other programming language
- Light experience with Azure: We recommend that you have some basic experience with Azure, but if you're brand new to it you will still be able to follow along!
If you want to follow along with the course without writing out all of the code (or try to find an error in your code), we have created this repository that contains all of the content that we showed in the course.
We have created a folder in this repo with all of the code and instructions on how to get started with this completed code both locally in Visual Studio Code and in the clour on Azure Machine Learning Studio. You can find this code in the project README.
These are all of the links from the Useful Resources slide in the first video of this course, as well as the links that are highlighted throughout other videos.
As the goal of this course is to help get you understand the basics of data science and machine learning, while being able to practically begin to explore those concepts in code. The next step after completing the videos is to start to explore other types of data, algorithms, and approches. One way to do that is to find additional tutorials to guide you through your discovery. We created a Microsoft Learn collection containing some of our favorite learning paths for furthering your exploration.
The Developer's Intro to Data Science Learn collection contains content around machine learning and data science specifically with Azure, as well as the UC Berkeley Data8 course. We also collated some AI learning paths that we find particularly interesting as further data exploration.
This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.
When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.