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Databricks Fundamentals: Getting Started with Data, AI, and Administration Course Overview

Databricks Fundamentals: Getting Started with Data, AI, and Administration Course Overview

Discover the Databricks Fundamentals: Getting Started with Data, AI, and Administration course at Koenig Solutions. Over 40 hours (5 days), you'll master the Databricks Architecture, navigate the workspace, manage data and resources, and implement machine learning models. You’ll also learn data governance, user management, and platform security. Practical labs using Azure will enhance your learning, offering hands-on experience in key areas such as data analysis, data engineering, machine learning, and administration. This course equips you with the essential skills to leverage Databricks for robust data management and advanced AI solutions.

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Course Prerequisites

Prerequisites for Databricks Fundamentals: Getting Started with Data, AI, and Administration

To ensure a smooth learning experience in the "Databricks Fundamentals: Getting Started with Data, AI, and Administration" course, we recommend that participants have the following foundational knowledge:


  • Basic Knowledge of SQL: Familiarity with basic SQL queries and data manipulation concepts.
  • Understanding of Data Concepts: A general understanding of data structures, databases, and data storage mechanisms.
  • Fundamental Knowledge of Machine Learning: Basic awareness of machine learning principles and workflows.
  • Familiarity with Cloud Platforms: Basic understanding of cloud computing concepts; experience with Azure would be an added advantage.
  • Introductory Programming Skills: Some experience with programming or scripting languages like Python or R.

These prerequisites help ensure you can effectively engage with the course content and make the most out of the training provided. If you meet these criteria, you'll be well-prepared to dive into the world of Databricks and unlock valuable skills in data analysis, machine learning, and administration.


Target Audience for Databricks Fundamentals: Getting Started with Data, AI, and Administration

1. The Databricks Fundamentals course equips professionals with essential skills in data, AI, and platform administration, making it ideal for individuals looking to leverage Databricks in their career.


2.


  • Data Engineers
  • Data Scientists
  • Machine Learning Engineers
  • Data Analysts
  • IT Administrators
  • Data Architects
  • Business Intelligence Analysts
  • Database Administrators
  • Data Governance Professionals
  • AI Engineers
  • Big Data Specialists
  • Cloud Solutions Architects
  • DevOps Engineers
  • IT Managers
  • Technology Consultants


Learning Objectives - What you will Learn in this Databricks Fundamentals: Getting Started with Data, AI, and Administration?

Introduction

The Databricks Fundamentals course provides a comprehensive overview of the Databricks platform, covering core functionalities in data analysis, machine learning, AI, and administrative capabilities. Participants will gain essential skills to effectively leverage Databricks for diverse IT functions.

Learning Objectives and Outcomes

  • Understand Databricks Architecture

    • Gain a deep understanding of the Databricks platform's core components and its structured architecture.

  • Navigate Databricks Workspace

    • Develop the skills to use the workspace for data analysis and seamless collaboration.

  • Manage Data and Resources

    • Learn best practices for data storage, management, and compute resource allocation within Databricks.

  • Implement Machine Learning

    • Explore the building, training, and deployment of machine learning models on the Databricks platform.

  • Administer and Govern

    • Understand critical aspects of data governance, user management, and platform security.

  • Data Analysis on Databricks

    • Utilize Databricks SQL to conduct data analysis and administrative tasks effectively.

  • Data Engineering Tasks

    • Master the use of notebooks, compute resources, and repo collaborations for data engineering.

  • Machine Learning Workflow

    • Conduct comprehensive end-to-end machine learning experiments using AutoML on

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