Enroll Course: https://www.coursera.org/learn/perform-data-science-with-azure-databricks
Are you looking to elevate your data science skills and work with massive datasets in the cloud? The Coursera course, “Perform data science with Azure Databricks,” is an exceptional resource that dives deep into leveraging Apache Spark and the powerful Azure Databricks platform for cloud-based data science workloads. This course is the fourth in a five-part series designed to prepare you for the DP-100 certification exam, “Designing and Implementing a Data Science Solution on Azure,” which validates your expertise in managing machine learning solutions at a cloud scale.
The syllabus is meticulously crafted, starting with an **Introduction to Azure Databricks**, where you’ll grasp the platform’s capabilities, understand Apache Spark notebooks for processing large files, and learn about the architecture of Spark Clusters and Jobs. The course then moves into **Working with data in Azure Databricks**, equipping you with the skills to handle day-to-day data functions like reads, writes, and queries. You’ll learn to manipulate large datasets from various sources and apply column-level transformations using DataFrame functions.
**Processing data in Azure Databricks** delves into both built-in SQL functions and the creation of User-Defined Functions (UDFs). A significant highlight is the introduction to Delta Lake, where you’ll learn to create, append, and upsert data to Spark tables, benefiting from its inherent reliability and optimizations.
The course then transitions into the core machine learning aspects. **Get started with Databricks and machine learning** teaches you to build essential machine learning workflow components using PySpark, including exploratory data analysis, model training, and evaluation, along with creating data featurization pipelines.
**Manage machine learning lifecycles and fine tune models** focuses on practical MLOps with MLflow for tracking experiments and utilizing Spark’s machine learning library for hyperparameter tuning and model selection. Finally, **Train a distributed neural network and serve models with Azure Machine Learning** provides hands-on experience with distributed deep learning using Horovod and Petastorm, and crucially, covers how to register, package, and deploy trained models as scoring web services using MLflow and Azure Machine Learning.
Overall, “Perform data science with Azure Databricks” is a comprehensive and highly practical course. It provides a solid foundation for anyone looking to excel in cloud-based data science and machine learning, especially those aiming for the DP-100 certification. The hands-on approach and coverage of cutting-edge tools like Delta Lake and MLflow make it an invaluable learning experience.
Enroll Course: https://www.coursera.org/learn/perform-data-science-with-azure-databricks