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mylena mahatma

Member since 2024

Gold League

69085 points
Create Image Captioning Models - בעברית Earned אוג 15, 2024 EDT
Applying Advanced LookML Concepts in Looker Earned יול 30, 2024 EDT
Transformer Models and BERT Model - בעברית Earned יול 26, 2024 EDT
BI Reporting: Looker Visualization on BigQuery Earned יול 3, 2024 EDT
Understanding LookML in Looker Earned יול 2, 2024 EDT
Prompt Design in Vertex AI Earned יול 2, 2024 EDT
Generative AI Fundamentals Earned יול 2, 2024 EDT
Encoder-Decoder Architecture - בעברית Earned יונ 4, 2024 EDT
Attention Mechanism - בעברית Earned מאי 31, 2024 EDT
Generative AI and ML with Vertex AI Earned מאי 28, 2024 EDT
Generative AI Fundamentals - בעברית Earned מאי 28, 2024 EDT
Generative AI for Business Leaders Earned מאי 28, 2024 EDT
Build a Data Mesh with Dataplex Earned מאי 24, 2024 EDT
Build a Data Warehouse with BigQuery Earned מאי 24, 2024 EDT
Prepare Data for ML APIs on Google Cloud Earned מאי 24, 2024 EDT
Generative AI Explorer : Vertex AI Earned מאי 23, 2024 EDT
Introduction to Image Generation - בעברית Earned מאי 23, 2024 EDT
Develop Advanced Enterprise Search and Conversation Applications Earned מאי 23, 2024 EDT
Text Prompt Engineering Techniques Earned מאי 20, 2024 EDT
Integrate Vertex AI Search and Conversation into Voice and Chat Apps Earned מאי 15, 2024 EDT
Responsible AI: Applying AI Principles with Google Cloud Earned מאי 10, 2024 EDT
Prepare Data for Looker Dashboards and Reports Earned מאי 7, 2024 EDT
Serverless Data Processing with Dataflow: Foundations Earned מאי 7, 2024 EDT
Introduction to Generative AI Studio - בעברית Earned מאי 6, 2024 EDT
Manage Data Models in Looker Earned אפר 15, 2024 EDT
Build LookML Objects in Looker Earned אפר 12, 2024 EDT
Data Catalog Fundamentals Earned אפר 11, 2024 EDT
Introduction to Responsible AI - בעברית Earned אפר 10, 2024 EDT
Create ML Models with BigQuery ML Earned אפר 9, 2024 EDT
Derive Insights from BigQuery Data Earned אפר 8, 2024 EDT
Building Reports in Looker Earned אפר 8, 2024 EDT
Introduction to Large Language Models - בעברית Earned אפר 3, 2024 EDT
Introduction to Generative AI - בעברית Earned אפר 3, 2024 EDT
Developing Data Models with LookML Earned אפר 2, 2024 EDT
Analyzing and Visualizing Data in Looker Earned אפר 2, 2024 EDT
Applying Machine Learning to your Data with Google Cloud Earned מרץ 26, 2024 EDT
Building Resilient Streaming Systems on Google Cloud Platform Earned מרץ 25, 2024 EDT
Achieving Advanced Insights with BigQuery Earned מרץ 22, 2024 EDT
Build Batch Data Pipelines on Google Cloud Earned מרץ 21, 2024 EDT
Delivery Shadowing Earned מרץ 19, 2024 EDT
Table Calculations, Pivots, and Visualizations Earned מרץ 18, 2024 EDT
Technology + Beyond the UI Earned מרץ 18, 2024 EDT
Technology + Within the UI Earned מרץ 18, 2024 EDT
Case Studies Earned מרץ 18, 2024 EDT
Liquid Templates and Parameters Earned מרץ 15, 2024 EDT
Admin Roles and Folder Access Earned מרץ 15, 2024 EDT
Creating New BigQuery Datasets and Visualizing Insights Earned מרץ 8, 2024 EST
Extends to Keep LookML DRY Earned מרץ 1, 2024 EST
Smart Analytics, Machine Learning, and AI on Google Cloud Earned פבר 29, 2024 EST
Exploring and Preparing your Data with BigQuery Earned פבר 29, 2024 EST
Build Data Lakes and Data Warehouses on Google Cloud Earned פבר 28, 2024 EST
Version Control and Caching Earned פבר 27, 2024 EST
The Modern Data Platform and LookML Earned פבר 23, 2024 EST
Driving Data Culture and Designing Dashboards Earned פבר 9, 2024 EST
Google Cloud Big Data and Machine Learning Fundamentals Earned פבר 8, 2024 EST
Achieving Business Outcomes with Looker Earned פבר 7, 2024 EST
Looker Explained Earned פבר 5, 2024 EST

בקורס הזה תלמדו איך ליצור מודל הוספת כיתוב לתמונה באמצעות למידה עמוקה (Deep Learning). אתם תלמדו על הרכיבים השונים במודל הוספת כיתוב לתמונה, כמו המקודד והמפענח, ואיך לאמן את המודל ולהעריך את הביצועים שלו. בסוף הקורס תוכלו ליצור מודלים להוספת כיתוב לתמונה ולהשתמש בהם כדי ליצור כיתובים לתמונות

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In this course, you will get hands-on experience applying advanced LookML concepts in Looker. You will learn how to use Liquid to customize and create dynamic dimensions and measures, create dynamic SQL derived tables and customized native derived tables, and use extends to modularize your LookML code.

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בקורס הזה נציג את הארכיטקטורה של טרנספורמרים ואת המודל של ייצוגים דו-כיווניים של מקודד מטרנספורמרים (BERT). תלמדו על החלקים השונים בארכיטקטורת הטרנספורמר, כמו מנגנון תשומת הלב, ועל התפקיד שלו בבניית מודל BERT. תלמדו גם על המשימות השונות שאפשר להשתמש ב-BERT כדי לבצע אותן, כמו סיווג טקסטים, מענה על שאלות והֶקֵּשׁ משפה טבעית. נדרשות כ-45 דקות כדי להשלים את הקורס הזה.

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This workload aims to upskill Google Cloud partners to perform specific tasks for modernization using LookML on BigQuery. A proof-of-concept will take learners through the process of creating LookML visualizations on BigQuery. During this course, learners will be guided specifically on how to write Looker modeling language, also known as LookML and create semantic data models, and learn how LookML constructs SQL queries against BigQuery. At a high level, this course will focus on basic LookML to create and access BigQuery objects, and optimize BigQuery objects with LookML.

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In this quest, you will get hands-on experience with LookML in Looker. You will learn how to write LookML code to create new dimensions and measures, create derived tables and join them to Explores, filter Explores, and define caching policies in LookML.

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Complete the introductory Prompt Design in Vertex AI skill badge to demonstrate skills in the following: prompt engineering, image analysis, and multimodal generative techniques, within Vertex AI. Discover how to craft effective prompts, guide generative AI output, and apply Gemini models to real-world marketing scenarios.

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Earn a skill badge by passing the final quiz, you'll demonstrate your understanding of foundational concepts in generative AI. A skill badge is a digital badge issued by Google Cloud in recognition of your knowledge of Google Cloud products and services. Share your skill badge by making your profile public and adding it to your social media profile.

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בקורס הזה לומדים בקצרה על ארכיטקטורת מקודד-מפענח, ארכיטקטורה עוצמתית ונפוצה ללמידת מכונה שמשתמשים בה במשימות של רצף לרצף, כמו תרגום אוטומטי, סיכום טקסט ומענה לשאלות. תלמדו על החלקים השונים בארכיטקטורת מקודד-מפענח, איך לאמן את המודלים האלה ואיך להשתמש בהם. בהדרכה המפורטת המשלימה בשיעור ה-Lab תקודדו ב-TensorFlow תרחיש שימוש פשוט בארכיטקטורת מקודד-מפענח: כתיבת שיר מאפס.

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בקורס נלמד על מנגנון תשומת הלב, שיטה טובה מאוד שמאפשרת לרשתות נוירונים להתמקד בחלקים ספציפיים ברצף הקלט. נלמד איך עובד העיקרון של תשומת הלב, ואיך אפשר להשתמש בו כדי לשפר את הביצועים במגוון משימות של למידת מכונה, כולל תרגום אוטומטי, סיכום טקסט ומענה לשאלות.

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Welcome Gamers! Train a model on your own image dataset, while having fun! Build, run, and get metadata for an end-to-end ML pipeline on Vertex Pipelines. You will compete to see who can finish the game with the highest score. Earn the points by completing the steps in the lab.... and get bonus points for speed! Be sure to click "End" when you're done with each lab to get the maximum points. All players will be awarded the game badge.

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רוצים לקבל תג מיומנות? אפשר להשלים את הקורסים Introduction to Generative AI, ‏Introduction to Large Language Models ו-Introduction to Responsible AI. מעבר של המבחן המסכם מוכיח שהבנתם את המושגים הבסיסיים בבינה מלאכותית גנרטיבית. 'תג מיומנות' הוא תג דיגיטלי ש-Google מנפיקה, שמוכיח שאתם מכירים את המוצרים והשירותים של Google Cloud. כדי לשתף את תג המיומנות אפשר להפוך את הפרופיל שלכם לגלוי לכולם ולהוסיף אותו לפרופיל שלכם ברשתות חברתיות.

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A Business Leader in Generative AI can articulate the capabilities of core cloud Generative AI products and services and understand how they benefit organizations. This course provides an overview of the types of opportunities and challenges that companies often encounter in their digital transformation journey and how they can leverage Google Cloud's generative AI products to overcome these challenges.

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Complete the introductory Build a Data Mesh with Dataplex skill badge to demonstrate skills in the following: building a data mesh with Dataplex to facilitate data security, governance, and discovery on Google Cloud. You practice and test your skills in tagging assets, assigning IAM roles, and assessing data quality in Dataplex.

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Complete the intermediate Build a Data Warehouse with BigQuery skill badge course to demonstrate skills in the following: joining data to create new tables, troubleshooting joins, appending data with unions, creating date-partitioned tables, and working with JSON, arrays, and structs in BigQuery.

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Complete the introductory Prepare Data for ML APIs on Google Cloud skill badge to demonstrate skills in the following: cleaning data with Dataprep by Trifacta, running data pipelines in Dataflow, creating clusters and running Apache Spark jobs in Dataproc, and calling ML APIs including the Cloud Natural Language API, Google Cloud Speech-to-Text API, and Video Intelligence API.

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This content is deprecated. Please see the latest version of the course, here.

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בקורס נלמד על מודלים של דיפוזיה, משפחת מודלים של למידת מכונה שיצרו הרבה ציפיות לאחרונה בתחום של יצירת תמונות. מודלים של דיפוזיה שואבים השראה מפיזיקה, וספציפית מתרמודינמיקה. בשנים האחרונות, מודלים של דיפוזיה הפכו לפופולריים גם בתחום המחקר וגם בתעשייה. מודלים של דיפוזיה עומדים מאחורי הרבה מהכלים והמודלים החדשניים ליצירת תמונות ב-Google Cloud. בקורס הזה נלמד על התיאוריה שמאחורי מודלים של דיפוזיה, ואיך לאמן ולפרוס אותם ב-Vertex AI.

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In this course, you'll use text embeddings for tasks like classification, outlier detection, text clustering and semantic search. You'll combine semantic search with the text generation capabilities of an LLM to build Retrieval Augmented Generation (RAG) solutions, such as for question-answering systems, using Google Cloud's Vertex AI and Google Cloud databases.

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Text Prompt Engineering Techniques introduces you to consider different strategic approaches & techniques to deploy when writing prompts for text-based generative AI tasks.

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This course on Integrate Vertex AI Search and Conversation into Voice and Chat Apps is composed of a set of labs to give you a hands on experience to interacting with new Generative AI technologies. You will learn how to create end-to-end search and conversational experiences by following examples. These technologies complement predefined intent-based chat experiences created in Dialogflow with LLM-based, generative answers that can be based on your own data. Also, they allow you to porvide enterprise-grade search experiences for internal and external websites to search documents, structure data and public websites.

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As the use of enterprise Artificial Intelligence and Machine Learning continues to grow, so too does the importance of building it responsibly. A challenge for many is that talking about responsible AI can be easier than putting it into practice. If you’re interested in learning how to operationalize responsible AI in your organization, this course is for you. In this course, you will learn how Google Cloud does this today, together with best practices and lessons learned, to serve as a framework for you to build your own responsible AI approach.

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Complete the introductory Prepare Data for Looker Dashboards and Reports skill badge course to demonstrate skills in the following: filtering, sorting, and pivoting data; merging results from different Looker Explores; and using functions and operators to build Looker dashboards and reports for data analysis and visualization.

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This course is part 1 of a 3-course series on Serverless Data Processing with Dataflow. In this first course, we start with a refresher of what Apache Beam is and its relationship with Dataflow. Next, we talk about the Apache Beam vision and the benefits of the Beam Portability framework. The Beam Portability framework achieves the vision that a developer can use their favorite programming language with their preferred execution backend. We then show you how Dataflow allows you to separate compute and storage while saving money, and how identity, access, and management tools interact with your Dataflow pipelines. Lastly, we look at how to implement the right security model for your use case on Dataflow.

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בקורס הזה נלמד על Generative AI Studio, מוצר ב-Vertex AI שעוזר ליצור אבות טיפוס למודלים של בינה מלאכותית גנרטיבית, כדי להשתמש בהם ולהתאים אותם לפי הצרכים שלכם. באמצעות הדגמה של המוצר עצמו, נלמד מהו Generative AI Studio, מהם הפיצ'רים והאפשרויות שלו, ואיך להשתמש בו. בסוף הקורס יהיה שיעור Lab מעשי לתרגול של מה שנלמד, ובוחן לבדיקת הידע.

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Complete the intermediate Manage Data Models in Looker skill badge course to demonstrate skills in the following: maintaining LookML project health; utilizing SQL runner for data validation; employing LookML best practices; optimizing queries and reports for performance; and implementing persistent derived tables and caching policies.

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Complete the introductory Build LookML Objects in Looker skill badge course to demonstrate skills in the following: building new dimensions and measures, views, and derived tables; setting measure filters and types based on requirements; updating dimensions and measures; building and refining Explores; joining views to existing Explores; and deciding which LookML objects to create based on business requirements.

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Data Catalog is deprecated and will be discontinued on January 30, 2026. You can still complete this course if you want to. For steps to transition your Data Catalog users, workloads, and content to Dataplex Catalog, see Transition from Data Catalog to Dataplex Catalog (https://cloud.google.com/dataplex/docs/transition-to-dataplex-catalog). Data Catalog is a fully managed and scalable metadata management service that empowers organizations to quickly discover, understand, and manage all of their data. In this quest you will start small by learning how to search and tag data assets and metadata with Data Catalog. After learning how to build your own tag templates that map to BigQuery table data, you will learn how to build MySQL, PostgreSQL, and SQLServer to Data Catalog Connectors.

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זהו קורס מבוא ממוקד שמטרתו להסביר מהי אתיקה של בינה מלאכותית, למה היא חשובה ואיך Google נוהגת לפי כללי האתיקה של הבינה המלאכותית במוצרים שלה. מוצגים בו גם 7 עקרונות ה-AI של Google.

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Complete the intermediate Create ML Models with BigQuery ML skill badge to demonstrate skills in creating and evaluating machine learning models with BigQuery ML to make data predictions.

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Complete the introductory Derive Insights from BigQuery Data skill badge course to demonstrate skills in the following: Write SQL queries.Query public tables.Load sample data into BigQuery.Troubleshoot common syntax errors with the query validator in BigQuery.Create reports in Looker Studio by connecting to BigQuery data.

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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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זהו קורס מבוא ממוקד שבוחן מהם מודלים גדולים של שפה (LLM), איך משתמשים בהם בתרחישים שונים לדוגמה ואיך אפשר לשפר את הביצועים שלהם באמצעות כוונון של הנחיות. הוא גם כולל הסבר על הכלים של Google שיעזרו לכם לפתח אפליקציות בינה מלאכותית גנרטיבית משלכם.

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זהו קורס מבוא ממוקד שמטרתו להסביר מהי בינה מלאכותית גנרטיבית, איך משתמשים בה ובמה היא שונה משיטות מסורתיות של למידת מכונה. הוא גם כולל הסבר על הכלים של Google שיעזרו לכם לפתח אפליקציות בינה מלאכותית גנרטיבית משלכם.

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This course empowers you to develop scalable, performant LookML (Looker Modeling Language) models that provide your business users with the standardized, ready-to-use data that they need to answer their questions. Upon completing this course, you will be able to start building and maintaining LookML models to curate and manage data in your organization’s Looker instance.

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In this course, you learn how to do the kind of data exploration and analysis in Looker that would formerly be done primarily by SQL developers or analysts. Upon completion of this course, you will be able to leverage Looker's modern analytics platform to find and explore relevant content in your organization’s Looker instance, ask questions of your data, create new metrics as needed, and build and share visualizations and dashboards to facilitate data-driven decision making.

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In this course, we define what machine learning is and how it can benefit your business. You'll see a few demos of ML in action and learn key ML terms like instances, features, and labels. In the interactive labs, you will practice invoking the pretrained ML APIs available as well as build your own Machine Learning models using just SQL with BigQuery ML.

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This 1-week, accelerated on-demand course builds upon Google Cloud Platform Big Data and Machine Learning Fundamentals. Through a combination of video lectures, demonstrations, and hands-on labs, you'll learn to build streaming data pipelines using Google cloud Pub/Sub and Dataflow to enable real-time decision making. You will also learn how to build dashboards to render tailored output for various stakeholder audiences.

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The third course in this course series is Achieving Advanced Insights with BigQuery. Here we will build on your growing knowledge of SQL as we dive into advanced functions and how to break apart a complex query into manageable steps. We will cover the internal architecture of BigQuery (column-based sharded storage) and advanced SQL topics like nested and repeated fields through the use of Arrays and Structs. Lastly we will dive into optimizing your queries for performance and how you can secure your data through authorized views. After completing this course, enroll in the Applying Machine Learning to your Data with Google Cloud course.

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In this intermediate course, you will learn to design, build, and optimize robust batch data pipelines on Google Cloud. Moving beyond fundamental data handling, you will explore large-scale data transformations and efficient workflow orchestration, essential for timely business intelligence and critical reporting. Get hands-on practice using Dataflow for Apache Beam and Serverless for Apache Spark (Dataproc Serverless) for implementation, and tackle crucial considerations for data quality, monitoring, and alerting to ensure pipeline reliability and operational excellence. A basic knowledge of data warehousing, ETL/ELT, SQL, Python, and Google Cloud concepts is recommended.

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

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

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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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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 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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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 is the second course in the Data to Insights course series. Here we will cover how to ingest new external datasets into BigQuery and visualize them with Looker Studio. We will also cover intermediate SQL concepts like multi-table JOINs and UNIONs which will allow you to analyze data across multiple data sources. Note: Even if you have a background in SQL, there are BigQuery specifics (like handling query cache and table wildcards) that may be new to you. After completing this course, enroll in the Achieving Advanced Insights with BigQuery course.

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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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Incorporating machine learning into data pipelines increases the ability to extract insights from data. This course covers ways machine learning can be included in data pipelines on Google Cloud. For little to no customization, this course covers AutoML. For more tailored machine learning capabilities, this course introduces Notebooks and BigQuery machine learning (BigQuery ML). Also, this course covers how to productionalize machine learning solutions by using Vertex AI.

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In this course, we see what the common challenges faced by data analysts are and how to solve them with the big data tools on Google Cloud. You’ll pick up some SQL along the way and become very familiar with using BigQuery and Dataprep to analyze and transform your datasets. This is the first course of the From Data to Insights with Google Cloud series. After completing this course, enroll in the Creating New BigQuery Datasets and Visualizing Insights course.

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While the traditional approaches of using data lakes and data warehouses can be effective, they have shortcomings, particularly in large enterprise environments. This course introduces the concept of a data lakehouse and the Google Cloud products used to create one. A lakehouse architecture uses open-standard data sources and combines the best features of data lakes and data warehouses, which addresses many of their shortcomings.

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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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This course introduces the Google Cloud big data and machine learning products and services that support the data-to-AI lifecycle. It explores the processes, challenges, and benefits of building a big data pipeline and machine learning models with Vertex AI on Google Cloud.

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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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