DP-100T01: Designing and Implementing a Data Science Solution on Azure

Overview

Designing and Implementing a Data Science Solution on Azure training course will help you to gain the necessary knowledge about how to use Azure services to develop, train, and deploy, machine learning solutions. The course starts with an overview of Azure services that support data science. From there, it focuses on using Azure's premier data science service, Azure Machine Learning service, to automate the data science pipeline. This course is focused on Azure and does not teach the student how to do data science. It is assumed students already know that.

Target Audience:

This course is aimed at data scientists and those with significant responsibilities in training and deploying machine learning models. This course is focused on Azure and does not teach the student how to do data science.

This course prepares you for Exam DP-100. Download Course Contents Test your current knowledge Qubits
Schedule & Prices
Course Details Schedule
Classroom Training*
Duration : 3 Days
Fee : Online : $2,200  (1-on-1), London : £1,700, Dubai : $2,500 , India : $1,900

March
16-18 (london)
July
20-22 (London)
Instructor-Led Online Training
Duration : 3 Days
Fee :  $2,200


March
16-18
July
20-22
Fly-Me-a-Trainer
Duration : 3 Days
Fee : On Request
Client's Location
As per mutual convenience

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Course Prerequisites

Before attending this course, students must have:

  • Azure Fundamentals
  • Basic Understanding of Statistics.
  • Understanding of data science including how to prepare data, train models, and evaluate competing models to select the best one.
  • How to program in the Python programming language and use the Python libraries: pandas, scikit-learn, matplotlib, and seaborn.


  • Select development environment
  • Set up development environment
  • Quantify the business problem
  • Transform data into usable datasets
  • Perform Exploratory Data Analysis (EDA)
  • Cleanse and transform data
  • Perform feature extraction
  • Perform feature selection
  • Select an algorithmic approach
  • Split datasets
  • Identify data imbalances
  • Train the model
  • Evaluate model performance
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