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Cloud Computing Careers and Certifications: First Steps

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From the course: Cloud Computing Careers and Certifications: First Steps

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  • Course details

    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.

    Instructor

    • Click here to view Lynn Langit’s instructor page

      Lynn Langit

      CEO Lynn Langit Consulting LLC

      Lynn 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

  • Moving towards the cloud

    - [Narrator] So along with the skills associated for data base programming, are skills that are needed for analytics around analyzing all of that data that's now up on the cloud. And this can come from people with either a traditional developer background or an analyst background. So this is also called predictive analytics, or sometimes I call it applied statistical learning. In particular, I've been doing quite a lot of work as I mentioned in a previous movie, with the deep neural network open sourced learning library called TensorFlow. And the reason for this is because it will perform very complex predictive analytics at scale and be able to address some computational problems that my customers previously either didn't have the expertise because they didn't have the TensorFlow library or didn't have the cloud scale services to address. Some of the problems for example are image analysis at scale, text analysis, natural language processing, very complex predictive analytics. Also…

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