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

Course Overview

In this course student will learn how to define and prepare the development environment, prepare data for modeling, perform feature engineering and develop models. Azure Data Scientists apply Azure’s machine learning techniques to train, evaluate, and deploy models that solve business problems.

This course will prepare you for DP-100: Designing and Implementing a Data Science Solution on Azure exam.

Course Details
  • Duration: 4 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.;

Expert Training

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