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Data science is a booming field, and big tech companies are clamoring to hire data scientists to help them gather, analyze and interpret the tremendous amount of data they have. It’s no surprise that the data science field is becoming increasingly crowded, and data science certifications can be a great way to stand out from the crowd.

Microsoft Data Science Certification is worth getting as it equips a data scientist with impressive cloud computing skills as they build on their knowledge of Python and Machine learning. The certificate validates their skills and proves their professionalism to hiring managers.

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What Will You Learn In Microsoft Data Science Certificate?

Microsoft offers several certification courses for data scientists at all levels. If you’re a beginner with zero knowledge of data science, you can start with the “Microsoft Certified Azure Fundamentals” and then advance to the associate-level certification.

The Azure Data Scientist Certification is a classic course structured with ten comprehensive modules to sufficiently equip the learner with skills to operate machine learning models at cloud scale in Microsoft Azure.

With a solid foundation in data science and proficiency in Python and ML models, you’ll build on that knowledge to master data preparation, model training, model evaluation and deployment, and ML solution monitoring using Microsoft Azure Machine learning.

Upon completion of the course, you’ll be required to take an exam that will test your understanding of the course and your ability to handle technical tasks in Azure.

The questions will test if you can create a workspace in Azure, train models, learn experiments, and optimize and deploy models.

Before enrolling for the course, ensure that you’re familiar with basic cloud computing concepts, data science, and ML algorithm techniques. You should be able to:

  • Create and use cloud computing services in Microsoft Azure.
  • Manipulate and visualize data using Python.
  • Use frameworks like TensorFlow and PyTorch to train and validate machine learning models.
  • Work with containers in Azure.

You can undertake pre-course training courses to polish these skills and prepare for the Data Science Certification course.

If you already have these skills up your sleeves, you can sign up for the course and access the 10 modules. Each of them includes a theory and a lab segment for practical implementation of the theoretical knowledge.

The modules are broken down as follows.

Module 1 – Getting Started With Azure Machine Learning

The module has two lessons: Introduction to Azure Machine Learning and Working with Azure Machine Learning.

The two lessons are well-detailed to help you familiarize yourself with the Azure Machine Learning workspace, its interface, and the tools available in the workspace.

The lab segment requires you to create an Azure ML workspace and use the available tools to code on the workspace.

Module 2 – No-code Machine Learning

This module has two modules: Automated machine Learning and Azure Machine Learning Designer. The two lessons train you to deploy ML models with no code.

The lab section requires you to use automated machine learning and Azure machine learning designer to train and execute machine learning models.

Module 3 – Running Experiments & Training Models

The lessons include:

  • Introduction to Experiments.
  • Training and Registering Models.

This module introduces you to data processing and coding for training machine learning models.

In the practical section, you’ll be required to run code-based experiments on the data and use them for training and registering models.

Module 4 – Working With Data

The module has two lessons on working with Datastores and Data sets so that you learn how to handle data in data stores and data sets in your Azure ML workspace.

The practical segment will require you to create data stores and data sets and then use them in your workspace to train models.

Module 5 – Working With Compute

The module has two lessons that teach how to create environments, run experiments, and work with compute targets. The lessons are:

  • Working with Environments
  • Working with Compute targets.

In the lab, you’ll create environments and compute targets, then use them to run experiments.

Module 6 – Operating Operations With Pipelines

The two lessons in this module introduce you to pipelines used to implement effective ML operationalization solutions in the Azure workspace and how to publish and run them.

For practice, you’ll create a pipeline for automated ML models, as well as publishing and running them.

Module 7 – Deploying And Consuming Models

This module has three lessons that teach on deploying models for batch inference, real-time inference, & implementing continuous integration and delivery.

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For your practical section, you’ll create both real-time and batch inferencing services and use them to publish models.

Module 8 – Training Optimal Models

In this module, you’ll focus on learning hyperparameter tuning in automated machine learning for your model to produce the best predictions from your data.

The lessons will teach you to use cloud-scale compute to identify the most suitable model to use on your data.

The practical lesson will see you tune and optimize hyperparameters and use Automated ML to identify relevant models for your data.

Module 9 – Responsible Machine Learning

This module consists of three lessons that teach responsible machine learning principles and how to apply them to ensure that data is handled responsibly. They include:

  • Fairness
  • Model Interpretability
  • Differential Privacy

In the practical lesson, you’ll explore differential privacy, apply it to data analysis, interpret ML models, and mitigate unfairness from models.

Module 10 – Monitoring Models

This module will discuss techniques to monitor modules with application insights and monitor data drift that can lead to model degradation.

In the practical lesson, you’ll use application insights to monitor a deployed model and detect any degradation in the model due to data drift.

How Long Will It Take To Finish Microsoft Data Science Certificate?

The Microsoft Azure Data Scientist Associate is self-paced, meaning that you can learn at your comfortable speed and practice a concept as much as you want before moving on to the next module.

It’s important to grasp all that the course entails before taking the certification exam. The exam is consists of 51 questions, completed within 180 minutes.

For a data scientist who’s already familiar with ML concepts and programming languages like Python, the time you take to complete the course should be much less compared to a beginner who has to familiarize themself with these tools first.

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How Much Is Microsoft Data Science Certificate?

The price for the certification varies for different countries. For example, those in the US may pay $165, while those in Turkey would pay $100. When enrolling for the course, you can find out the cost based on your country.

What Jobs Can You Get With Microsoft Data Science Certificate?

As a Microsoft Certified Azure Data Scientist, you have a higher probability of getting hired.

The certificate gives you an edge and validates your skills so that hiring managers consider you a professional in the data science field. A data scientist with this certification stands a better chance for the job than one without it.

The certificate also opens doors for a wider job selection, even for jobs higher in rank. You can find a job as a data analyst, a data scientist, delivery data scientist, data and applied scientist, and many others.

The practical knowledge gained during the course makes the certificate preferable in many sectors.

While completing the courses and getting certified is not enough, most hiring managers search for data scientists with practical knowledge and experience in the field. Experience with Microsoft Azure cloud computing services is a big plus for a professional data scientist.

With the Microsoft Data Science Certificate, you become eligible to undertake other advanced courses in the field and get certified.

More certifications and accreditations will raise your experience and professionalism, which is considered for higher executive and managerial positions.

Conclusion

We are living in an era where data science is taking over how companies and industries run their business and measure profitability.

This has led to an increase in qualifying data scientists. With the increasing talent, you’ll need to stand out from many for your skills to count.

A Microsoft data science certification separates you from the crowd and professionalizes your skills for better job opportunities.