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

Course Overview

Course Details:
In this course students will 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.

Outcome: 
Pass the DP-100 Designing and Implementing a Data Science Solution on Azure exam to be awarded the Microsoft Certified: Azure Data Scientist Associate certification.
Students learn how to develop data models that solve business problems using Azure technologies.

Course Details
  • Duration: 3 Days
  • Level: 300

Who this course is designed for
  • Candidates for this course apply scientific rigor and data exploration techniques to gain actionable insights and communicate results to stakeholders. Candidates use machine learning techniques to train, evaluate, and deploy models to build AI solutions that satisfy business objectives. Candidates use applications that involve natural language processing, speech, computer vision, and predictive analytics.

What You Will Learn

  • 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

Prerequisites:

  • Knowledge of common statistical methods and data analysis best practices
  • Working knowledge of relational databases

Course Outline

Module 1: Define and prepare the development environment

In this module you will learn how to select development environment, set up development environment and quantify the business problem.

Module 2: Prepare data for modeling

In this module you will learn how to transform data into usable datasets, perform Exploratory Data Analysis (EDA), cleanse and transform data.

Module 3: Perform feature engineering

In this module you will learn how to perform feature extraction and perform feature selection.

Module 4: Develop models

In this module you will learn how to select an algorithmic approach, split datasets, identify data imbalances, train the model and evaluate model performance.;

Contact the experts at Opsgility to schedule this class at your location or to discuss a more comprehensive readiness solution for your organization. Contact us to enroll or book a class

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