Modern Analytics Academy - Data Science¶Updated • 1 contributor
Data Science explores topics related to data enrichment and getting value from data, historically referred to as “business intelligence.” This often overlaps with integration and modeling, and often machine learning algorithms. However, the data cleansing, enrichment, and modeling techniques are often unique to data science when compared to other business analytics. For more in-depth AI and ML related topics, see our AI and ML Academy.
“Vignette” Sessions¶
Our ongoing Modern Analytics Academy series, which we’ve called “vignettes”, dive into topical areas related to analytics. For a complete list of sessions, visit the vignette page. The following vignettes are related to acquisition and storage:
As a follow up to Part 1, this video walks through how to leverage the ML model by generating predictions, storing the data in the lakehouse, and consuming both real-time data and predictions in Power BI reports.
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Looking to get started with Data Science in Microsoft Fabric? In this session, we'll walk through how to get started by creating a Jupyter notebook, download sample data from our lab, create a ML forecast with Prophet, and cross-validate and evaluate the model. We'll then store the model in MLflow and see how to use it both in the notebook and in the Fabric workspace.
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Operationalizing Machine Learning and Artificaal Intelligence can be a challenge for data engineers. In this session we explore how SynapseML simplifies the process of leveraging Azure Cognitive Services as part of a data engineering process in Synapse.
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