Data Science Workspace course
This document provides a description of the expected learning outcomes in the ÃÛ¶¹ÊÓƵ Experience Platform Data Science Workspace course. In order to view the course, you must login to Experience League using your ÃÛ¶¹ÊÓƵ ID.
The getting started with Data Science Workspace for Data Scientists course is designed for data scientists who want to learn how to use JupyterLab Notebooks to derive insights and query data, create profile-enabled datasets, publish automated machine learning models, and activate machine-learned insights to both ÃÛ¶¹ÊÓƵ and non-ÃÛ¶¹ÊÓƵ applications.
Course prerequisites
- A registered ÃÛ¶¹ÊÓƵ ID account.
- The ÃÛ¶¹ÊÓƵ ID account must have been added to an Organization with access to ÃÛ¶¹ÊÓƵ Experience Platform and Data Science Workspace.
- A non-production sandbox.
Expected learning outcomes
The following learning outcomes are covered in the Data Science Workspace course. Additionally, you have the option to follow along while creating and publishing a propensity model that is provided for the course.
- The architecture of Data Science Workspace
- How to use JupyterLab
- How to access data and query data in Data Science Workspace
- Exploratory Data Analysis
- How to create a recipe and model
- Methods used to train and score a model
- The role of hyper-parameters in model development
- How to publish trained models as a service
- How to use Data Science Workspace to enrich your Real-Time Customer Profile data
- How to create a streaming segment with your model output
Lessons
The Data Science Workspace course is split into five lessons.
Lesson 1
Introduction (19 minutes): Learn about the course and get a high-level overview of Data Science Workspace including the required course assets.
Lesson 2
Load, query, and explore data in JupyterLab (24 minutes): Learn how JupyterLab on Experience Platform helps simplify and facilitate key workflows for a data scientist, such as gathering data, cleaning data, visualizing data, and discovering insights.
Lesson 3
Create a model in JupyterLab (26 minutes): Learn how to start building models in Data Science Workspace.
Lesson 4
Use Data Science Workspace to train and score a model (6 minutes): Learn how to create a model and publish it as a service in Experience Platform.
Lesson 5
Consume and deliver Data Science Insights (11 minutes): Learn how Data Science Workspace model outputs can be used in the Real-Time Customer Profile to deliver personalized experiences with ÃÛ¶¹ÊÓƵ applications and services.
Next steps
After completing the Data Science Workspace course, visit the Sensei Machine Learning API guides to learn how to utilize RESTful APIs to do everything you just learned and more.