Data Analytics and Machine Learning with Databricks Course Overview

Data Analytics and Machine Learning with Databricks Course Overview

Unlock the power of Data Analytics and Machine Learning with our comprehensive Databricks course. Over four days (32 hours), you'll master the essentials of Apache Spark, including data ingestion, analysis, and visualization. Learn to create and manage Databricks workspaces, perform SQL queries, and develop effective machine learning models. Each session is designed to provide hands-on experience, with labs focusing on real-world applications. By the end of the course, participants will be equipped to apply their skills in Azure Databricks, facilitating advanced data management and facilitate automation tasks. Ideal for those familiar with programming languages like Python, SQL, and more. Join us and elevate your data analytics expertise!

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  • Live Training (Duration : 32 Hours)
  • Per Participant
  • Classroom Training price is on request

♱ Excluding VAT/GST

You can request classroom training in any city on any date by Requesting More Information

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Course Prerequisites

Prerequisites for Data Analytics and Machine Learning with Databricks Course

To ensure you have a successful learning experience in the Data Analytics and Machine Learning with Databricks course, we recommend that you possess the following minimum knowledge:


  • Familiarity with at least one programming language, such as Python, C++, SQL, Scala, or R.
  • Basic understanding of data structures and algorithms.
  • Preliminary knowledge of data analytics concepts and terminology.

This course is designed to be engaging and informative, providing you with the skills necessary to leverage Databricks for data analytics and machine learning. Don’t worry if you’re not an expert; as long as you have a foundational understanding in the areas mentioned above, you will be well-prepared to participate in the training.


Target Audience for Data Analytics and Machine Learning with Databricks

The "Data Analytics and Machine Learning with Databricks" course equips participants with the skills to utilize Apache Spark and the Databricks platform for effective data analysis and machine learning projects.


Target Audience and Job Roles:


  • Data Analysts
  • Data Scientists
  • Business Intelligence Analysts
  • Machine Learning Engineers
  • Big Data Engineers
  • Software Engineers/Developers
  • Data Engineers
  • Database Administrators
  • IT Professionals seeking data analytics skills
  • Research Scientists
  • Graduates in Computer Science or related fields
  • Cloud Practitioners working on Azure
  • Professionals transitioning into data science and analytics roles


Learning Objectives - What you will Learn in this Data Analytics and Machine Learning with Databricks?

Introduction

The Data Analytics and Machine Learning with Databricks course equips learners with essential skills in Apache Spark and Databricks to effectively manage big data, perform data analysis, and build machine learning models within the Azure environment.

Learning Objectives and Outcomes

  • Understand the fundamentals of Apache Spark and its role in big data processing.
  • Set up and configure a Databricks workspace and cluster on Azure.
  • Upload data, create tables, and manage columns and datatypes in Databricks.
  • Write SQL queries to perform data analysis, including aggregates and joins.
  • Create and interpret visualizations using DataFrames and different chart types.
  • Automate tasks by creating, scheduling, and managing jobs in Databricks.
  • Gain insights into Delta Lake and perform batch and streaming reads/writes.
  • Manage advanced data operations such as delete, update, and merge in Delta tables.
  • Implement and visualize machine learning models using Databricks.
  • Integrate various data sources and collaborate within Databricks for optimized workflows.

Target Audience for Data Analytics and Machine Learning with Databricks

The "Data Analytics and Machine Learning with Databricks" course equips participants with the skills to utilize Apache Spark and the Databricks platform for effective data analysis and machine learning projects.


Target Audience and Job Roles:


  • Data Analysts
  • Data Scientists
  • Business Intelligence Analysts
  • Machine Learning Engineers
  • Big Data Engineers
  • Software Engineers/Developers
  • Data Engineers
  • Database Administrators
  • IT Professionals seeking data analytics skills
  • Research Scientists
  • Graduates in Computer Science or related fields
  • Cloud Practitioners working on Azure
  • Professionals transitioning into data science and analytics roles


Learning Objectives - What you will Learn in this Data Analytics and Machine Learning with Databricks?

Introduction

The Data Analytics and Machine Learning with Databricks course equips learners with essential skills in Apache Spark and Databricks to effectively manage big data, perform data analysis, and build machine learning models within the Azure environment.

Learning Objectives and Outcomes

  • Understand the fundamentals of Apache Spark and its role in big data processing.
  • Set up and configure a Databricks workspace and cluster on Azure.
  • Upload data, create tables, and manage columns and datatypes in Databricks.
  • Write SQL queries to perform data analysis, including aggregates and joins.
  • Create and interpret visualizations using DataFrames and different chart types.
  • Automate tasks by creating, scheduling, and managing jobs in Databricks.
  • Gain insights into Delta Lake and perform batch and streaming reads/writes.
  • Manage advanced data operations such as delete, update, and merge in Delta tables.
  • Implement and visualize machine learning models using Databricks.
  • Integrate various data sources and collaborate within Databricks for optimized workflows.