Microsoft
Perform data science with Azure Databricks
Microsoft

Perform data science with Azure Databricks

7,752 already enrolled

Gain insight into a topic and learn the fundamentals.
3.4

(58 reviews)

Intermediate level

Recommended experience

25 hours to complete
3 weeks at 8 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
3.4

(58 reviews)

Intermediate level

Recommended experience

25 hours to complete
3 weeks at 8 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Harness the power of Apache Spark and powerful clusters running on the Azure Databricks platform to run data science workloads.

  • Perform machine learning with Azure Databricks. Work with User-Defined Function (UDF) in Azure Databricks

  • Work with DataFrames in Azure Databricks. Use Azure Databricks and the Apache Spark notebook to process large amounts of data

  • Build and query a Delta Lake

Details to know

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Assessments

31 quizzes

Taught in English

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This course is part of the Microsoft Azure Data Scientist Associate (DP-100) Exam Prep Professional Certificate Professional Certificate
When you enroll in this course, you'll also be enrolled in this Professional Certificate.
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There are 6 modules in this course

In this module, you will discover the capabilities of Azure Databricks and the Apache Spark notebook for processing huge files. You will come to understand the Azure Databricks platform and identify the types of tasks well-suited for Apache Spark. You will also be introduced to the architecture of an Azure Databricks Spark Cluster and Spark Jobs.

What's included

7 videos5 readings4 quizzes1 discussion prompt

Azure Databricks supports day-to-day data-handling functions, such as reads, writes, and queries. In this module, you will work with large amounts of data from multiple sources in different raw formats. You will also learn to use the DataFrame Column Class Azure Databricks to apply column-level transformations, such as sorts, filters and aggregations. You will also use advanced DataFrame functions operations to manipulate data, apply aggregates, and perform date and time operations in Azure Databricks.

What's included

4 videos10 readings4 quizzes

Azure Databricks supports a range of built in SQL functions, however, sometimes you have to write custom function, known as User-Defined Function (UDF). In this module, you will learn how to register and invoke UDFs. You will also learn how to use Delta Lake to create, append, and upsert data to Apache Spark tables, taking advantage of built-in reliability and optimizations.

What's included

4 videos7 readings6 quizzes

In this module, you will learn how to use PySpark’s machine learning package to build key components of the machine learning workflows that include exploratory data analysis, model training, and model evaluation. You will also learn how to build pipelines for common data featurization tasks.

What's included

4 videos11 readings8 quizzes

In this module, you will learn how to use MLflow to track machine learning experiments and how to use modules from the Spark’s machine learning library for hyperparameter tuning and model selection.

What's included

4 videos5 readings5 quizzes

In this module, you will learn how to use the Uber’s Horovod framework along with the Petastorm library to run distributed, deep learning training jobs on Spark using training datasets in the Apache Parquet format. You will also learn how to use MLflow and Azure Machine Learning service register, package, and deploy a trained model to both Azure Container Instance, and Azure Kubernetes Service as a scoring web service.

What's included

5 videos6 readings4 quizzes1 discussion prompt

Instructor

Instructor ratings
2.8 (9 ratings)
 Microsoft
Microsoft
137 Courses954,485 learners

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Microsoft

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3.4

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AM
5

Reviewed on Aug 29, 2024

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5

Reviewed on Apr 24, 2024

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