This course will teach you ways to use your live-streams and business data that is being collected by the IoT sensors and devices to their full potential for the betterment of your business.
Learning Style
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Course Duration
This course will teach you ways to use your live-streams and business data that is being collected by the IoT sensors and devices to their full potential for the betterment of your business.
The IoT Data Analytics and Storage course is an integral part of the Certification of Microsoft Professional Program in IoT.
Internet of Things can take your business by a storm, it promises infinite benefits and it can help you discover the true potential of your business data.
This course will teach you ways to use your live-streams and business data that is being collected by the IoT sensors and devices to their full potential for the betterment of your business.
At the end of the course, students will be able to:
The target audience for this course is:
Students should be familiar with the following before enrolling for the test:
| Brand | Microsoft |
|---|---|
| Subjects | Business Productivity, Cloud Computing |
| Lab Access | No |
| Technology | Microsoft |
| Learning Style | Self-Paced Learning |
| Learning Type | Course |
| Difficulty | Intermediate |
| Course Duration | 12 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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