Candidates for this exam Microsoft certified azure data scientist 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. Candidates serve as part of a multi-disciplinary team that incorporates ethical, privacy, and governance considerations into the solution. Candidates typically have a background in mathematics, statistics, and computer science.
In this Microsoft certified azure data scientist training gain the necessary knowledge about how to use Azure services to develop, train, and deploy machine learning solutions. The course Microsoft certified azure data scientist associate 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 Microsoft certified azure data scientist associate is focused on Azure and does not teach the student how to do data science. It is assumed students already know that.
Designing and Implementing a Data Science Solution on Azure Certification Course Microsoft certified azure data scientist associate is aimed at data scientists and those with significant responsibilities in training and deploying machine learning models.
- Azure Fundamentals
- 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.
Module 1: Define and Prepare the Development Environment
- Select Development Environment
- Set up Development Environment
- Quantify the Business Problem
Module 2: Prepare Data for Modeling
- Transform Data Into Usable Datasets
- Perform Exploratory Data Analysis (EDA)
- Cleanse and Transform Data
Module 3: Perform Feature Engineering
- Perform Feature Extraction
- Perform Feature Selection
Module 4: Develop Models
- Select an Algorithmic Approach
- Split Datasets
- Identify Data Imbalances
- Train the Model
- Evaluate Model Performance
Fees & Schedule
|Delivery Mode||Course Duration||Fees|
|Live Virtual Training||4 Days||Ask for Quote|
|Onsite Classroom Training||4 Days||Ask for Quote|
|Customized Training||4 Days||Ask for Quote|