This is a course for beginners to master database design, relational databases, and SQL to fully exploit the advantages of a modern database system.

Learn about the power of Databricks Lakehouse and help you scale up your data engineering and machine learning skills.

This course will teach you advanced parts of this library. When you’re finished with this course, you’ll have the skills and knowledge of DataFrames and Pandas needed to analyze your data with Pandas.

This course provides hands-on experience dealing with imbalanced data in machine learning, which is critical for machine learning algorithms.

Learn how to identify, analyze, remove and impute missing data in Python.

Make it easy to visualize, explore, and impute missing data with naniar, a tidyverse friendly approach to missing data.

This course provides a comprehensive approach to understanding and managing a database management system and acing database internals.

Power BI is Microsoft's business intelligence tool for creating interactive and stunning visualizations. This course will teach you how to create reports & dashboards and publish them in the cloud as well on the Power BI App.

Configure virtual machine sizing, storage, and networking options to ensure adequate performance for your database workloads. Choose and configure appropriate high availability options.

Provision and deploy Azure SQL Database and Azure SQL managed instance. Select the appropriate options when performing a migration to the SQL PaaS platform.

Deploying workloads with Azure Databricks Workflows involves orchestrating and automating complex data processing pipelines, machine learning workflows, and analytics tasks. In this module, you learn how to deploy workloads with Databricks Workflows.

Plan an appropriate high availability and disaster recovery strategy based on recovery time objective and recovery point objective. Choose the best solution for IaaS or PaaS deployments or hybrid workloads.