Microsoft Certified Azure Data Scientist Associate Courses

23rd Sep 2020
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Data Scientist is one of the top paying IT jobs in the market, and Azure is one of the big three cloud service providers along with AWS and Google Cloud. This makes Microsoft Certified Azure Data Scientist Associate Courses highly relevant to aspiring Data Scientists and professionals. This post discusses the data science as a discipline, courses, career opportunities, and eligibility to pursue the Microsoft Certified Azure Data Scientist Associate certification.

Data Science: Beginning of the Discipline

Educational institutions running Microsoft Certified Azure Data Scientist Associate Classes often brand Data Scientist as one of the newly created hottest IT jobs in the market, but it’s incorrect. The discipline Data Science existed much before the advent of the Internet in the age when Barter Economy (goods exchange model) was there in absence of currency. It’s basically the assessment of a targeted market, and it demands to produce or create more relevant products or services.

Data Science and Data Scientist: Present Context

The only difference that we observe today is the abundance of data. The volume of data is increasing exponentially with such a p pace that Bit (b), Byte (B), kilobyte (KB), Megabyte (MB), Gigabyte (GB), and Terabyte (TB) – all the units have become redundant. The Web Economic Forum has projected the data volume to reach 44 zettabytes by 2020.

A day in data

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Microsoft Certified Azure Data Scientist Associate Courses: What Does it Cover

After completing Microsoft Certified Azure Administrator Associate Training Classes, you get acquainted with the knowledge of designing and implementing a Data Science Solution on Azure. You learn to operate machine learning solutions at cloud scale using Azure Machine Learning. Here is a break-up of the skills you acquire to successfully complete your Microsoft Certified Azure Data Scientist Associate Certification.

I. Set up an Azure Machine Learning Workspace (30-35%)

a) Create an Azure Machine Learning workspace

Create an Azure Machine Learning workspace

Configure workspace settings

Manage a workspace by using Azure Machine Learning studio

b) Manage data objects in an Azure Machine Learning workspace

Register and maintain data stores

Create and manage datasets

c) Manage experiment compute contexts

Create a compute instance

Determine appropriate compute specifications for a training workload

Create compute targets for experiments and training

II. Run Experiments and Train Models (25-30%)

a. Create models by using Azure Machine Learning Designer

  • create a training pipeline by using Azure Machine Learning designer
  • ingest data in a designer pipeline
  • use designer modules to define a pipeline data flow
  • use custom code modules in designer

b. Run training scripts in an Azure Machine Learning workspace

  • create and run an experiment by using the Azure Machine Learning SDK
  • consume data from a data store in an experiment by using the Azure Machine Learning
  • SDK
  • choose an estimator for a training experiment

c. Generate metrics from an experiment run

  • log metrics from an experiment run
  • retrieve and view experiment outputs
  • use logs to troubleshoot experiment run errors

d. Automate the model training process

  • create a pipeline by using the SDK
  • pass data between steps in a pipeline
  • run a pipeline
  • monitor pipeline runs

III. Optimize and Manage Models (20-25%)

a. Use Automated ML to create optimal models

  • use the Automated ML interface in Azure Machine Learning studio
  • use Automated ML from the Azure Machine Learning SDK
  • select scaling functions and pre-processing options
  • determine algorithms to be searched
  • define a primary metric
  • get data for an Automated ML run
  • retrieve the best model

b. Use Hyperdrive to tune hyperparameters

  • select a sampling method
  • define the search space
  • define the primary metric
  • define early termination options
  • find the model that has optimal hyperparameter values
  • Use model explainers to interpret models
  • select a model interpreter
  • generate feature importance data

c. Manage models

  • register a trained model
  • monitor model history
  • monitor data drift

IV. Deploy and Consume Models (20-25%)

a. Create production compute targets

  • consider security for deployed services
  • evaluate compute options for deployment

b. Deploy a model as a service

  • configure deployment settings
  • consume a deployed service
  • troubleshoot deployment container issues

c. Create a pipeline for batch inferencing

  • publish a batch inferencing pipeline
  • run a batch inferencing pipeline and obtain outputs

d. Publish a designer pipeline as a web service

  • create a target compute resource
  • configure an Inference pipeline
  • consume a deployed endpoint

Microsoft Certified Azure Data Scientist Associate Courses: Who Shall Take This Course

This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud.

Average Data Scientist with Microsoft Azure Skills Salary

The demand for data scientist is in every space from retail marketing to healthcare, agriculture, manufacturing, real-estate, and beyond. Candidates with Azure Data Scientist Associate Certification earn as much as $97,855 per year, according to PayScale.com, a leading salary assessment company that tracks the compensation paid in the US economy. Candidates with additional skills of Natural Language Processing (NLP), Algorithm Development, etc. manage to get a higher compensation than their peers.

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