Data Architecture for Data Scientists

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Price: 24.99$

Machine learning models are only as good as the data they are trained on, which is why understanding data architecture is critical for data scientists building machine learning models. This course will teach you: The fundamentals of data architecture A refresher on data types, including structured, unstructured, and semi-structured data Data Warehouse Fundamentals Data Lake Fundamentals The differences between data warehouses and data lakes Data Lakehouse Fundamentals Data Mesh fundamentals for decentralized governance of data The challenges of incorporating streaming data in data science Some machine learning-specific data infrastructure, such as feature stores and vector databases The course will help you: Make informed decisions about the architecture of your data infrastructure to improve the accuracy and effectiveness of your models Adopt modern technologies and practices to improve workflows Develop a better understanding and empathy for data engineers Improve your reputation as an all-around data scientist Think of data architecture as the framework that supports the construction of a machine learning model. Just as a building needs a strong framework to support its structure, a machine learning model needs a solid data architecture to support its accuracy and effectiveness. Without a strong framework, the building is at risk of collapsing, and without a strong data architecture, machine learning models are at risk of producing inaccurate or biased results. By understanding the principles of data architecture, data scientists can ensure that their data infrastructure is robust, reliable, and capable of supporting the training and deployment of accurate and effective machine learning models. By the end of this course, you’ll have the knowledge to help guide your team and organization in creating the right data architecture for deploying data science use cases.