This course is specifically designed to help Data Analyst and IT Professionals understand the core features and functionalities of Azure Data Catalog.
Learning Style
Provider
Difficulty
Course Duration
This course is specifically designed to help Data Analyst and IT Professionals understand the core features and functionalities of Azure Data Catalog.
This course is specifically designed to help Data Analyst and IT Professionals understand the core features and functionalities of Azure Data Catalog. This course covers the key concepts of data and information assets integration, discovery, and applications. The primary objective of this course is to nurture business intelligence and data analytical skills of professionals working with viable business data.
This course helps professionals gain practical experience in managing and operating the Azure Data Catalog and will assist them in fully utilizing its core functionalities. This comprehensive training program proves an in-depth overview of the Azure Data Catalog before moving onto advanced services and capabilities. Through this course, candidates will learn the art of developing and adopting an Azure Data Catalog and will also gain substantial knowledge of tasks relating to Data Sources Registration, Assets Discovery, Business Glossary, and Azure Catalog Administration.
The core objective of this course is to help professionals gain a better knowledge and understanding of the following key concepts:
This course is specifically tailored for the following group of professionals and interested candidates:
Professionals planning to enroll in the Enabling Data Source Discovery with Azure Data Catalog Course must comply with the following prerequisites:
| Brand | Microsoft |
|---|---|
| Subjects | Cloud Computing, DevOps |
| Lab Access | No |
| Technology | Microsoft |
| Learning Style | Self-Paced Learning |
| Learning Type | Course |
| Difficulty | Intermediate |
| Course Duration | 15 Hours |
| Language | English |
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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.
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