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Nicole Hazzam

회원 가입일: 2021

Delivery Shadowing Earned 9월 8, 2022 EDT
Case Studies Earned 9월 8, 2022 EDT
Technology + Beyond the UI Earned 9월 7, 2022 EDT
Technology + Within the UI Earned 9월 7, 2022 EDT
Table Calculations, Pivots, and Visualizations Earned 9월 7, 2022 EDT
Building Reports in Looker Earned 9월 7, 2022 EDT
Liquid Templates and Parameters Earned 9월 7, 2022 EDT
Extends to Keep LookML DRY Earned 9월 5, 2022 EDT
Admin Roles and Folder Access Earned 9월 4, 2022 EDT
Version Control and Caching Earned 8월 29, 2022 EDT
The Modern Data Platform and LookML Earned 8월 29, 2022 EDT
Driving Data Culture and Designing Dashboards Earned 7월 19, 2022 EDT
Achieving Business Outcomes with Looker Earned 7월 18, 2022 EDT
Looker Explained Earned 7월 11, 2022 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud - 한국어 Earned 11월 30, 2021 EST
Google Cloud Big Data and Machine Learning Fundamentals - 한국어 Earned 10월 30, 2021 EDT

In this course, you shadow a series of client meetings led by a Looker Professional Services Consultant.

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By the end of this course, you should feel confident employing technical concepts to fulfill business requirements and be familiar with common complex design patterns.

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In this course you will discover additional tools for your toolbox for working with complex deployments, building robust solutions, and delivering even more value.

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Develop technical skills beyond LookML along with basic administration for optimizing Looker instances

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This course reviews the processes for creating table calculations, pivots and visualizations

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This course is designed for Looker users who want to create their own ad-hoc reports. It assumes experience of everything covered in our Get Started with Looker course (logging in, finding Looks & dashboards, adjusting filters, and sending data)

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In this course you will discover Liquid, the templating language invented by Shopify and explore how it can be used in Looker to create dynamic links, content, formatting, and more.

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Hands on course covering the main uses of extends and the three primary LookML objects extends are used on as well as some advanced usage of extends.

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This course is designed to teach you about roles, permission sets and model sets. These are areas that are used together to manage what users can do and what they can see in Looker.

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This course aims to introduce you to the basic concepts of Git: what it is and how it's used in Looker. You will also develop an in-depth knowledge of the caching process on the Looker platform, such as why they are used and why they work

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This course provides an introduction to databases and summarized the differences in the main database technologies. This course will also introduce you to Looker and how Looker scales as a modern data platform. In the lessons, you will build and maintain standard Looker data models and establish the foundation necessary to learn Looker's more advanced features.

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This course provides an iterative approach to plan, build, launch, and grow a modern, scalable, mature analytics ecosystem and data culture in an organization that consistently achieves established business outcomes. Users will also learn how to design and build a useful, easy-to-use dashboard in Looker. It assumes experience with everything covered in our Getting Started with Looker and Building Reports in Looker courses.

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In this course, we’ll show you how organizations are aligning their BI strategy to most effectively achieve business outcomes with Looker. We'll follow four iterative steps: Plan, Build, Launch, Grow, and provide resources to take into your own services delivery to build Looker with the goal of achieving business outcomes.

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By the end of this course, you should be able to articulate Looker's value propositions and what makes it different from other analytics tools in the market. You should also be able to explain how Looker works, and explain the standard components of successful service delivery.

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머신러닝을 데이터 파이프라인에 통합하면 데이터에서 더 많은 인사이트를 도출할 수 있습니다. 이 과정에서는 머신러닝을 Google Cloud의 데이터 파이프라인에 포함하는 방법을 알아봅니다. 맞춤설정이 거의 또는 전혀 필요 없는 경우에 적합한 AutoML에 대해 알아보고 맞춤형 머신러닝 기능이 필요한 경우를 위해 Notebooks 및 BigQuery 머신러닝(BigQuery ML)도 소개합니다. Vertex AI를 사용해 머신러닝 솔루션을 프로덕션화하는 방법도 다루어 보겠습니다.

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이 과정에서는 데이터-AI 수명 주기를 지원하는 Google Cloud 빅데이터 및 머신러닝 제품과 서비스를 소개합니다. Google Cloud에서 Vertex AI를 사용하여 빅데이터 파이프라인 및 머신러닝 모델을 빌드하는 프로세스, 문제점 및 이점을 살펴봅니다.

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