# Data Science in the Cloud  > Photo by [Jelleke Vanooteghem](https://unsplash.com/@ilumire) from [Unsplash](https://unsplash.com/s/photos/cloud?orientation=landscape) When working with big data in data science, the cloud can be a game changer. In the next three lessons, we will explore what the cloud is and why it can be incredibly useful. We will also analyze a heart failure dataset and build a model to estimate the likelihood of someone experiencing heart failure. Using the cloud's capabilities, we will train, deploy, and utilize the model in two different ways: one using only the user interface in a Low code/No code approach, and the other using the Azure Machine Learning Software Developer Kit (Azure ML SDK).  ### Topics 1. [Why use Cloud for Data Science?](17-Introduction/README.md) 2. [Data Science in the Cloud: The "Low code/No code" way](18-Low-Code/README.md) 3. [Data Science in the Cloud: The "Azure ML SDK" way](19-Azure/README.md) ### Credits These lessons were created with ☁️ and 💕 by [Maud Levy](https://twitter.com/maudstweets) and [Tiffany Souterre](https://twitter.com/TiffanySouterre). The data for the Heart Failure Prediction project comes from [ Larxel](https://www.kaggle.com/andrewmvd) on [Kaggle](https://www.kaggle.com/andrewmvd/heart-failure-clinical-data). It is licensed under the [Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/). --- **Disclaimer**: This document has been translated using the AI translation service [Co-op Translator](https://github.com/Azure/co-op-translator). While we aim for accuracy, please note that automated translations may include errors or inaccuracies. The original document in its native language should be regarded as the authoritative source. For critical information, professional human translation is advised. We are not responsible for any misunderstandings or misinterpretations resulting from the use of this translation.