Get Started with SQL Analytics and BI on Databricks

This course is intended for complete beginners to Python to provide the basics of programmatically interacting with data. The course begins with a basic introduction to programming expressions, variables, and data types. It then progresses into conditional and control statements followed by an introduction to methods and functions. You will learn the basics of data structures, classes, and various string and utility functions. Lastly, you will gain experience using the Pandas library for data analysis and visualization as well as the fundamentals of cloud computing. Throughout the course, you will gain hands-on practice through lab exercises with additional resources to deepen your knowledge of programming after the class. Learning path This course stands alone and is not currently part of any Databricks Academy learning paths. Last course update September 2022

This Get Started with Databricks Free Edition workshop introduces new users to the Databricks environment through two guided notebooks:

01 - Databricks Workspace Tour

⇾ Walk through the main workspace navigation and settings

⇾ Run notebooks in both Python and SQL

⇾ Format notebook content using basic Markdown

02 - Get Started with Databricks Free Edition

⇾ Explore Unity Catalog objects (catalogs, schemas, tables, volumes)

⇾ Upload a local file into a Volume

⇾ Access and use data from Databricks Marketplace

⇾ Ingest and visualize data within a notebook

⇾ Get a high-level tour of the SQL Editor and dashboards

By the end of this workshop, you’ll have hands-on experience navigating Databricks, working with data, and building simple visualizations — all within Databricks Free Edition.

This course equips end users to get the most from their organization’s AI-powered assistants. Learners begin by understanding how the assistant works, its capabilities and limitations, and how to to create precise, business-relevant prompts by applying the COIE framework—Context, Outcome, Instruction, Example. They then progress to prompting techniques such as zero-shot, few-shot, chain-of-thought, self-ask, and meta-prompting to handle diverse content, reasoning, and workflow tasks. By the end, participants can structure complex requests, chain prompts effectively, and collaborate with their AI assistant as a reliable, reasoning partner that delivers accurate, actionable insights.