Data Preparation for Machine Learning

This course focuses on the fundamentals of preparing data for machine learning using Databricks. Participants will learn essential skills for exploring, cleaning, and organizing data tailored for traditional machine learning applications. Key topics include data visualization, feature engineering, and optimal feature storage strategies. Through practical exercises, participants will gain hands-on experience in efficiently preparing data sets for machine learning within the Databricks. This course is designed for associate-level data scientists and machine learning practitioners. and individuals seeking to enhance their proficiency in data preparation, ensuring a solid foundation for successful machine learning model deployment.

Skill Level

Associate

Duration

Prerequisites

Familiarity with Databricks workspace and notebooks
Familiarity with Delta Lake and Lakehouse
Intermediate-level knowledge of Python

Self-Paced

Custom-fit learning paths for data, analytics, and AI roles and career paths through on-demand videos

See all our registration options

Registration options

Databricks has a delivery method for wherever you are on your learning journey

Self-Paced

Custom-fit learning paths for data, analytics, and AI roles and career paths through on-demand videos

Instructor-Led

Public and private courses taught by expert instructors across half-day to two-day courses

Blended Learning

Self-paced and weekly instructor-led sessions for every style of learner to optimize course completion and knowledge retention. Go to Subscriptions Catalog tab to purchase

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Skills@Scale

Comprehensive training offering for large scale customers that includes learning elements for every style of learning. Inquire with your account executive for details

Upcoming Public Classes

Platform Administrator

Get Started with Databricks Platform Administration

In this course, you will learn the basics of platform administration on the Databricks Data Intelligence Platform. It offers a comprehensive overview of the Unity Catalog, a vital component for effective data governance within Databricks environments. Divided into five modules, it begins with a detailed introduction to Databricks infrastructure and its data intelligence platform, including an in-depth walkthrough of the Databricks Workspace. You will explore data governance principles within Unity Catalog, covering its key concepts, architecture, and roles. The course further emphasizes managing Unity Catalog metastores and compute resources, including clusters and SQL warehouses. Finally, you'll master data access control by learning about privileges, fine-grained access, and how to govern data objects. By the end, you will be equipped with essential skills to administer the Unity Catalog to implement effective data governance, optimize compute resources, and enforce robust data security strategies. With the purchase of a Databricks Labs subscription, the course also closes out with a comprehensive lab exercise to practice what you’ve learned in a live Databricks Workspace environment.

Get Started with SQL Analytics and BI on Databricks

Please note: This course was formerly titled 'Get Started with Databricks for Data Analysis,' but the course content remains unchanged.

In this course, you will learn basic skills that will allow you to use the Databricks Data Intelligence Platform to perform a simple data analytics workflow and support data warehousing endeavors. You will be given a tour of the workspace and be shown how to work with data objects in Databricks such as catalogs, schemas, tables, compute clusters, notebooks, and dashboards. You will then follow a basic data analytics workflow to perform tasks such as manipulating data using Databricks SQL, leveraging Delta Lake version logs to time travel, creating dashboards within the platform, and creating Genie Spaces for data exploration using natural language prompts. You will also learn how Databricks supports data warehousing needs through the use of Databricks SQL, Delta Live Tables, and Unity Catalog. With the purchase of a Databricks Labs subscription, the course also closes out with a comprehensive lab exercise to practice what you’ve learned in a live Databricks Workspace environment.

Deploy Workloads with Databricks Workflows

By scheduling tasks with Databricks Jobs, applications can be run automatically to keep tables in the Lakehouse fresh. Using Databricks SQL to schedule updates to queries and dashboards allows quick insights using the newest data. In this course, students will be introduced to task orchestration using the Databricks Workflow Jobs UI. Optionally, they will configure and schedule dashboards and alerts to reflect updates to production data pipelines.

In this course, you’ll learn how to orchestrate data pipelines with Databricks Workflow Jobs and schedule dashboard updates to keep analytics up-to-date. We’ll cover topics like getting started with Databricks Workflows, how to use Databricks SQL for on-demand queries, and how to configure and schedule dashboards and alerts to reflect updates to production data pipelines.

Note: This course is part of the 'Data Engineering with Databricks' course series.

Paid & Subscription

Lab

Associate