Why Azure Data Scientist Course?

Overall The Azure Data Scientist applies their knowledge of data science and machine learning to implement and run machine learning workloads on Azure; in particular, using Azure Machine Learning Service.

Part-Time

Fast forward your career in the IT industry with a part-time course at School of IT. In turn Part-time courses allow working professionals to transition into a new skill set while working. Moreover at School of IT we are agile and customize a course to the individual.

Full Time

Ready to start a career in IT? Use the Microsoft Azure platform as a full time student at School of IT. Thus beginning your career in Artificial Intelligence.

High School

All in all Learn about the Microsoft Azure platform and prepare for the future while you’re still in high school. Thus no matter where you are, we come to you! Thus giving you the analytical skills to pursue your dreams!

Corporate

Learn about AWS and up skill yourself or your company while you’re working. Thus no matter where you are, we come to you and give the tools to move up in your company.

Data Science Courses Objectives.

By the end of the data management course, students will have usable knowledge of the following:

• Overall apply statistical methods to data
• Be able to understand Python or programming in R to explore and transform data.
• Understand and create, validate machine learning models with Azure Machine Learning.
• Thoroughly Understand Python/Programming in R code to build machine learning models.
• Apply data science techniques to common scenarios.
• Introduction to compute targets.
• Acquire the skills needed to find structure in data and how to use the python libraries.
• Matplotlib.
• SciKit Learn.
• NumPy.
• NLTK,
• Implement a machine learning solution for a given data problem.
• Overall learn advanced Python or programming in R and Data Analysis to implement computer science algorithms.
• Thus design and Implement a Data Science Solution on Azure.
• Build AI solutions with Azure Machine Learning.
• Azure Machine Learning tools and interfaces.
• Train a machine learning model with Azure Machine Learning.
• Learn Azure Machine Learning estimators.
• In turn work with Data in Azure Machine Learning.
• Introduction to compute targets and environments.
• Orchestrate machine learning with pipelines.
• Pass data between pipeline steps.
• Furthermore deploy machine learning models with Azure Machine Learning.
• All in all Deploy models as a real-time service.
• Consume real-time inferencing service, including troubleshooting.
• In turn Automate machine learning model selection with Azure Machine Learning.
• Thus automate machine learning tasks and algorithms.
• Preprocessing and featurization.
• Thus Run automated machine learning experiments.