Duration
1 day
Overview
Imagine you're working for a supermarket and you want to know how much bread you need to have in stock every week to meet customer demands while also avoiding food waste.
Or maybe you want to analyze your customers to understand how to best target them with personalized offers.
Whenever you want to make informed decisions within an organization, you can use data science to get insights from the data you have. Data science is a combination of mathematics, statistics, and computer engineering.
When you perform data science, you can analyze your data and identify complicated patterns that can provide you with meaningful insights for your organization. You can use data science to create artificial intelligence (AI) models that encompass the complicated patterns you find in your data. A common approach is to use data science to train machine learning models using libraries like scikit-learn in Python to achieve AI.
Taking a data science project from beginning to end can be a challenge. Microsoft Fabric offers one workspace to manage an end-to-end data science project.
In this module, you learn about a typical data science project. Additionally, you explore which Microsoft Fabric features you can use for each part of the data science process.
Outline
- Understand the data science process
- Explore common machine learning models
- Understand the data science process
- Explore and process data with Microsoft Fabric
- Ingest your data into Microsoft Fabric
- Explore and transform your data
- Prepare your data with the Data Wrangler
- Train and score models with Microsoft Fabric
- Understand experiments
- Start tracking metrics
- Understand models
- Use a model to generate insights
- Exercise - Explore data science in Microsoft Fabric
Summary
Microsoft Fabric offers one central workspace to perform data science from beginning to end.
To perform data science, you need to first define the problem. Then, you can identify the data you need and ingest it into Microsoft Fabric. When you've ingested your data, you can explore and prepare your data, using notebooks or the Data Wrangler.
To train machine learning models as part of your data science project, you can track your work with experiments. To use a model to generate insights, you can use the built-in PREDICT function.
Programme scope, delivery arrangements and suitability are confirmed during enquiry.