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Do you want to start a career in cloud computing, but don't know where to begin? If so, this course can help by providing an ideal foundational mapping of the career paths and certifications that IT professionals should consider if they're interested in transitioning to cloud computing. Instructor and cloud architect Lynn Langit covers a variety of career paths, including IT pro, developer, analyst, and architect roles. Throughout the course, she focuses on the major cloud platforms—AWS, Microsoft Azure, Google, and Alibaba—discusses certifications available for each, and explores the future of cloud computing careers.
CEO Lynn Langit Consulting LLCLynn Langit is a cloud architect who works with Amazon Web Services and Google Cloud Platform.
Lynn specializes in big data projects. She has worked with AWS Athena, Aurora, Redshift, Kinesis, and the IoT. She has also done production work with Databricks for Apache Spark and Google Cloud Dataproc, Bigtable, BigQuery, and Cloud Spanner.
Lynn is also the cofounder of Teaching Kids Programming. She has spoken on data and cloud technologies in North and South America, Europe, Africa, Asia, and Australia.
Skills covered in this course
For each course you will get
- Exercise files and quizzes
- Certificate of completion from LinkedIn
- Offline and audio-only options
Moving towards the cloud“
- In this section we're going to take a look at mapping developer skills into cloud based roles. 2 broad categories. A category called programming which is, I think kind of a narrow definition of developer skills, application or API based development, and the cloud-hosted relational database programming but it can include other types of data stores as well and this is a really growing category so I want to take a minute to talk about it in this section. This of course includes other type of data-store programming. Now, in addition to the first two categories, I've added a new one as I've been working more and more in the area of machine learning, and really this is kind of a name change for what I previously thought of as data science. It includes working with predictive analytics which is applied statistics in a lot of ways but also analysis of large amounts of data via probabilistic algorithms such as logistic regression, open source deep neural network framework TensorFlow, more…
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