Daffa M.
Menjadi anggota sejak 2021
Bronze League
5790 poin
Menyiapkan Data untuk ML API di Google Cloud
Earned Sep 25, 2021 EDT
Menggunakan API Machine Learning di Google Cloud
Earned Sep 25, 2021 EDT
Membuat Model ML dengan BigQuery ML
Earned Sep 24, 2021 EDT
Scientific Data Processing
Earned Sep 24, 2021 EDT
Membangun Data Warehouse dengan BigQuery
Earned Sep 23, 2021 EDT
DEPRECATED Applied Data: Blockchain
Earned Sep 23, 2021 EDT
Membangun Pipeline Tanpa Kode di Cloud Data Fusion
Earned Sep 23, 2021 EDT
DEPRECATED BigQuery for Marketing Analysts
Earned Sep 22, 2021 EDT
Menyiapkan Data untuk Dasbor dan Laporan Looker
Earned Sep 21, 2021 EDT
Data Catalog Fundamentals
Earned Sep 21, 2021 EDT
NCAA® March Madness®: Bracketology with Google Cloud
Earned Sep 19, 2021 EDT
Mendapatkan Insight dari Data BigQuery
Earned Sep 19, 2021 EDT
DEPRECATED BigQuery Basics for Data Analysts
Earned Sep 19, 2021 EDT
Data Science on Google Cloud
Earned Sep 18, 2021 EDT
Selesaikan badge keahlian pengantar Menyiapkan Data untuk ML API di Google Cloud untuk menunjukkan keterampilan Anda dalam hal berikut: menghapus data dengan Dataprep by Trifacta, menjalankan pipeline data di Dataflow, membuat cluster dan menjalankan tugas Apache Spark di Managed Service for Apache Spark, dan memanggil beberapa ML API, termasuk Cloud Natural Language API, Google Cloud Speech-to-Text API, dan Video Intelligence API.
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Dapatkan badge keahlian tingkat lanjut dengan menyelesaikan kursus tentang Menggunakan API Machine Learning di Google Cloud yang membahas fitur dasar machine learning dan dan teknologi AI berikut: Cloud Vision API, Cloud Translation API, dan Cloud Natural Language API.
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Selesaikan badge keahlian tingkat menengah Membuat Model ML dengan BigQuery ML untuk menunjukkan keterampilan dalam hal berikut: membuat dan mengevaluasi model machine learning dengan BigQuery ML untuk membuat prediksi data.
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Big data, machine learning, and scientific data? It sounds like the perfect match. In this course, you will get hands-on practice with GCP services like BigQuery, Managed Service for Apache Spark, and Tensorflow by applying them to use cases that employ real-life, scientific data sets. By getting experience with tasks like earthquake data analysis and satellite image aggregation, Scientific Data Processing will expand your skill set in big data and machine learning so you can start tackling your own problems across a spectrum of scientific disciplines.
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Selesaikan badge keahlian tingkat menengah Membangun Data Warehouse dengan BigQuery untuk menunjukkan keterampilan Anda dalam hal berikut: menggabungkan data untuk membuat tabel baru, memecahkan masalah penggabungan, menambahkan data dengan union, membuat tabel berpartisi tanggal, serta menggunakan JSON, array, dan struct di BigQuery.
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Blockchain and related technologies, such as distributed ledger and distributed apps, are becoming new value drivers and solution priorities in many industries. In this course you will gain hands-on experience with distributed ledger and the exploration of blockchain datasets in Google Cloud. It brings the research and solution work of Google's Allen Day into self-paced labs for you to run and learn directly. Since this course uses advanced SQL in BigQuery, a SQL-in-BigQuery refresher lab is at the start.
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Kursus ini menawarkan praktik langsung dengan Cloud Data Fusion, platform integrasi data tanpa kode berbasis cloud. Developer ETL, Data Engineer, dan Analis dapat memperoleh manfaat besar dari transformasi dan konektor bawaan untuk membangun dan men-deploy pipeline mereka tanpa perlu menulis kode. Kursus ini dimulai dengan lab panduan memulai yang memperkenalkan UI Cloud Data Fusion kepada peserta. Peserta dapat mencoba menjalankan pipeline batch dan real time serta menggunakan plugin Wrangler untuk melakukan beberapa transformasi menarik pada data.
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Want to turn your marketing data into insights and build dashboards? Bring all of your data into one place for large-scale analysis and model building. Get repeatable, scalable, and valuable insights into your data by learning how to query it and using BigQuery. BigQuery is Google's fully managed, NoOps, low cost analytics database. With BigQuery you can query terabytes and terabytes of data without having any infrastructure to manage or needing a database administrator. BigQuery uses SQL and can take advantage of the pay-as-you-go model. BigQuery allows you to focus on analyzing data to find meaningful insights.
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Selesaikan badge keahlian pengantar Menyiapkan Data untuk Dasbor dan Laporan Looker untuk menunjukkan keterampilan dalam hal berikut: memfilter, mengurutkan, dan melakukan pivot pada data; menggabungkan hasil dari sejumlah Eksplorasi Looker; serta menggunakan fungsi dan operator untuk membangun dasbor dan laporan Looker untuk analisis dan visualisasi data.
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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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In this series of labs you will learn how to use BigQuery to analyze NCAA basketball data with SQL. Build a Machine Learning Model to predict the outcomes of NCAA March Madness basketball tournament games.
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Selesaikan badge keahlian pengantar Mendapatkan Insight dari Data BigQuery untuk menunjukkan keterampilan dalam hal berikut: menulis kueri SQL, membuat kueri tabel publik, memuat sampel data ke dalam BigQuery, memecahkan masalah error sintaksis umum dengan validator kueri di BigQuery, dan membuat laporan di Data Studio dengan menghubungkannya ke data BigQuery.
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Want to scale your data analysis efforts without managing database hardware? Learn the best practices for querying and getting insights from your data warehouse with this interactive series of BigQuery labs. BigQuery is Google's fully managed, NoOps, low cost analytics database. With BigQuery you can query terabytes and terabytes of data without having any infrastructure to manage or needing a database administrator. BigQuery uses SQL and can take advantage of the pay-as-you-go model. BigQuery allows you to focus on analyzing data to find meaningful insights.
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This is the first of two Quests of hands-on labs is derived from the exercises from the book Data Science on Google Cloud Platform, 2nd Edition by Valliappa Lakshmanan, published by O'Reilly Media, Inc. In this first Quest, covering up through chapter 8, you are given the opportunity to practice all aspects of ingestion, preparation, processing, querying, exploring and visualizing data sets using Google Cloud tools and services.
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