AWS Certified Data Analytics – Specialty (DAS-C01) Cert Prep: 1 Collection

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Noah Gift

1:22:58

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01 - Introduction

01 - Overview.mp4

03:01

02 - Technology prerequisites.mp4

03:00

03 - Using SageMaker Studio Lab.mp4

07:04

04 - Learn AWS CloudShell.mp4

11:59

05 - The cloud developer workspace advantage.mp4

04:10

06 - Prototyping AI APIs on AWS with CloudShell and Bash.mp4

12:39

07 - Using AWS Cloud9 with Amazon CodeWhisperer.mp4

09:17

02 - 1. Determine the Operational Characteristics of the Collection System

01 - Operationalizing a collection system.mp4

01:52

02 - Job styles Batch vs, streaming data.mp4

02:30

03 - 2. Select a Collection System That Handles the Frequency, Volume, and Source of Data

01 - Data ingestion pipelines.mp4

02:11

02 - AWS Batch demo.mp4

03:29

03 - AWS Step Functions demo.mp4

08:25

04 - Transform data in transit.mp4

02:02

05 - Handing map reduce for ML.mp4

01:55

04 - 3. Select a Collection System That Addresses the Key Properties of Data

01 - Installing a systems programming language Rust.mp4

02:43

02 - Using Rust to collect S3 objects and summarize metadata.mp4

06:41

Description

The AWS Certified Data Analytics credential helps organizations identify and develop talent with critical skills for implementing cloud initiatives. Earning AWS Certified Data Analytics – Specialty certification validates expertise in using AWS data lakes and analytics services to get insights from data. In this course, Noah Gift focuses on the first domain of the exam: Collection. Noah’s course maps to the exam topics: Determine the operational characteristics of the collection system; Select a collection system that handles the frequency, volume, and the source of data; and Select a collection system that addresses the key properties of data, such as order, format, and compression. If you’re considering taking the AWS Data Analytics (DAS-C01) exam, join Noah in the course to start your prep.

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Amazon AWS

Noah Gift

Instructor's Courses

Author of Practical MLOps, Enterprise MLOps, Developing on AWS with C#, Pragmatic AI, and Python for DevOps. Certified on Multiple MLOps certifications including Google-Professional Machine Learning Engineer and AWS Certified Machine Learning - Specialty. Adjunct Professor at Duke MIDS & Northwestern Graduate Data Science & AI. Held business roles including CTO, general manager, consulting CTO, and cloud architect. Consults with start-ups and other companies on machine learning and cloud architecture. AWS ML Hero, Python Software Foundation Fellow, AWS Subject Matter on Machine Learning, AWS Certified Solutions Architect, AWS Certified Machine Learning Specialist, AWS Certified Big Data Specialist, Google Certified Professional Architect, and AWS Academy Accredited Instructor. Published books, videos, and courses on cloud machine learning, DevOps, Python, Data Science, Big Data and AI. ★ Specialties ★ ° Cloud-native Machine Learning and AI ° Directly teaching cutting edge skills to students that lead to jobs ° Creating world-class content in all forms ° Building Companies ° Shipping new Products ° Leading and growing engineering teams ° Production Machine Learning, Deep Learning, Big Data, and AI ° Serverless Data Engineering ° Advising Early Stage Startups/Consulting CTO services ° Distributed Systems and Scalability

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