Mon-Fri 9am to 6pm CST

Data Science Certification path: Microsoft Azure
Self-Paced
Learning Style
Microsoft
Provider
Intermediate
Difficulty
Varies
Course Duration
Certificate
Course Information
About this Learning Path:
Microsoft Azure Fundamentals (AZ-900)
This course will provide foundational level knowledge of cloud services and how those services are provided with Microsoft Azure. The course can be taken as an optional first step in learning about cloud services and Microsoft Azure, before taking further Microsoft Azure or Microsoft cloud services courses.
The course will cover general cloud computing concepts as well as general cloud computing models and services such as Public, Private and Hybrid cloud and Infrastructure-as-a-Service (IaaS), Platform-as-a-Service(PaaS) and Software-as-a-Service (SaaS).
It will also cover some core Azure services and solutions, as well as key Azure pillar services concerning security, privacy, compliance and trust. It will finally cover pricing and support services available with Azure.
Designing and Implementing a Data Science Solution on Azure (DP-100.1)
This course teaches about the data science process and how Microsoft Azure services support it. A high level overview of Azure data science related services is provided followed by a deep dive on the premier data science service, Azure Machine Learning service, which supports automation of machine learning model training and deployment
Although some basic data science concepts are presented, it is assumed the student has an understanding a data science and machine learning prior to taking this course. The course teaches how to bring your data science work to Azure.
Designing and Implementing an Azure AI Solution (AI-100.1)
The course assumes that you already have Azure fundamental skills and understand how to navigate the Azure portal and to create services there.It also assumes that you have some familiarity with the Azure data storage technologies but does not require you to know how to implement
these data storage technologies at an expert level.
Implementing an Azure Data Solution (DP-200.1)
In this course, the students will implement various data platform technologies into solutions that are in line with business and technical requirements including on-premises, cloud, and hybrid data scenarios incorporating both relational and No-SQL data. They will also learn how to process data using a range of technologies and languages for both streaming and batch data.
The students will also explore how to implement data security including authentication, authorization, data policies and standards. They will also define and implement data solution monitoring for both the data storage and data processing activities. Finally, they will manage and troubleshoot Azure data solutions which includes the optimization and disaster recovery of big data, batch processing and streaming data solutions.
Designing an Azure Data Solution (DP-201.1)
In this course, the students will implement various data platform technologies into solutions that are in line with business and technical requirements including on-premises, cloud, and hybrid data scenarios incorporating both relational and No-SQL data. They will also learn how to process data using a range of technologies and languages for both streaming and batch data
The students will also explore how to implement data security including authentication, authorization, data policies and standards. They will also define and implement data solution monitoring for both the data storage and data processing activities. Finally, they will manage and troubleshoot Azure data solutions which includes the optimization and disaster recovery of big data, batch processing and streaming data solutions
Career & Salary Insight
Outline
More Information
Brand | Microsoft |
---|---|
Subjects | Big Data |
Lab Access | No |
Technology | Microsoft |
Learning Style | Self-Paced Learning |
Learning Type | Learning Path |
Difficulty | Intermediate |
Course Duration | Varies |
Language | English |
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Tom Robertson
(Data Science Enthusiast)
Tom is an innovator first, and then a Data Scientist & Software Architect. He has integrated expertise in business, product, technology and management. Tom has been involved in creating category defining new products in AI and big data for different industries, which generated more than hundred million revenue cumulatively, and served more than 10 million users.
As a Data Scientist and Software Architect Tom has extensive experience in data science, engineering, architecture and software development. To date Tom has accumulated over a decade of experience in R, Python & Linux Shell programming.
Tom has expertise on Python, SQL, and Spark. He has worked on several libraries including but not limited to Scikit-learn, Pandas, NumPy, Matplotlib, Seaborn, SciPy, NLTK, Keras, and Tensorflow.