Google Cloud Platform Big Data and Machine Learning Fundamentals auf Deutsch from Coursera

In diesem einwöchigen On-Demand-Intensivkurs erhalten die Teilnehmer eine Einführung in die Funktionen der Google Cloud Platform (GCP) für Big Data und maschinelles Lernen. Dabei wird ein kurzer Überblick über die Google Cloud Platform geboten, während die Funktionen für die Datenverarbeitung eingehender behandelt werden. Nach Abschluss dieses Kurses sind die Teilnehmer in der Lage: • den Zweck und den Nutzen der wichtigsten Produkte für Big Data und maschinelles Lernen in der Google Cloud Platform zu beschreiben • vorhandene MySQL- und Hadoop-/Pig-/Spark-/Hive-Arbeitslasten mit Cloud SQL und Cloud Dataproc zur Google Cloud Platform zu migrieren • mit BigQuery und Cloud Datalab interaktive Datenanalysen vorzunehmen • zwischen Cloud SQL, Bigtable und Datastore zu wählen • mit TensorFlow ein neuronales Netzwerk zu trainieren und zu verwenden • eine Auswahl zwischen verschiedenen Datenverarbeitungsprodukten in der Google Cloud Platform zu treffen Wenn Sie sich zu diesem Kurs anmelden möchten, sollten Sie ungefähr ein (1) Jahr Erfahrung in einem oder mehreren der folgenden Bereiche haben: • Gängige Abfragesprachen, z. B. SQL • Extraktions-, Transformations-, Ladeaktivitäten • Datenmodellierung • Maschinelles Lernen und/oder Statistik • Programmierung in Python Hinweise zum Google-Konto: • In China stehen die Dienste von Google derzeit nicht zur Verfügung

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Provides an introduction to core Big Data and Machine Learning features in the Google Cloud Platform (GCP)

Suitable for learners with existing experience in common query languages (e.g., SQL), data extraction, transformation, and loading (ETL) activities, data modeling, machine learning, and/or statistics

Covers data processing capabilities of GCP, including migration options for existing workloads

Hands-on labs and interactive materials enhance the learning experience

Taught by Google Cloud Training, known for its expertise in cloud computing

Reviews summary

Google cloud fundamentals: big data and machine learning

This course offers a useful introduction to the Google Cloud Platform for Big Data and Machine Learning. Students with experience in SQL, ETL, data modeling, machine learning, statistics, and Python programming will find this course accessible. However, some students have reported issues with assessments and labs.

Assessments are quiz-based.

"I would've like to see more creativity in the assessments."

Some labs had issues opening.

"Alles gut, außer dass sich ein Lab lange nicht öffnen ließ."

Materials and organization were deceptive.

"This course materials and in particular its organization were very deceptive."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Google Cloud Platform Big Data and Machine Learning Fundamentals auf Deutsch with these activities:

Review MySQL fundamentals

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Begin this course with a strong foundation in MySQL to minimize friction in using Cloud SQL.

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Review syntax for creating and querying databases
Review how to perform DDL and DML operations

Review Hadoop ecosystem

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Begin this course with a strong foundation in the Hadoop ecosystem to minimize friction in using Cloud Dataproc.

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Review how to use Pig, Hive, Spark, and Hadoop

Practice data analysis with BigQuery

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Improve your ability to analyze data efficiently by practicing SQL queries and data manipulation techniques using BigQuery.

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Import a dataset into BigQuery.
Write SQL queries to explore and analyze the data.
Create visualizations to present your findings.

Eight other activities

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Follow a tutorial on deploying a machine learning model

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Reinforce your understanding of machine learning deployment by following a step-by-step guide that takes you through the process of building, deploying, and monitoring a machine learning model.

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Choose a tutorial that aligns with your skill level and interests.
Set up your development environment and gather the necessary resources.
Follow the tutorial step-by-step, building and deploying your machine learning model.
Test and evaluate the performance of your deployed model.
Monitor your deployed model and make any necessary adjustments.

Follow tutorials on BigQuery

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Following these tutorials will give you a practical understanding of the features of BigQuery.

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Complete 5 tutorials from the BigQuery documentation

Practice using Cloud Datalab

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You can use Cloud Datalab to better understand how to make use of BigQuery.

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Complete 10 drills using Cloud Datalab

Design and build a data pipeline

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Develop your ability to design and implement a data pipeline in the cloud by gathering data, cleaning it, and processing it.

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Gather and clean data from various sources.
Design and implement a data transformation pipeline.
Store and manage the transformed data.
Monitor and maintain the data pipeline.

Write a blog post on a big data topic

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Enhance your understanding of big data concepts by researching and writing a blog post on a topic that interests you.

Browse courses on Big Data

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Choose a big data topic that you are passionate about and have some knowledge of.
Research the topic thoroughly, gathering information from reputable sources.
Organize your thoughts and ideas into a coherent outline.
Write the blog post, ensuring it is well-written, informative, and engaging.
Publish your blog post on a platform where it can reach a wider audience.

Create a presentation on TensorFlow

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Creating a presentation will let you demonstrate your ability to understand how to train and use a neural network in TensorFlow.

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Choose a real-world dataset
Train a neural network using TensorFlow
Create a presentation that explains your methods
Present your work to peers

Read 'Hands-On Machine Learning with TensorFlow'

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This reference text includes insights into the methods for getting started using TensorFlow.

View Hands-On Machine Learning with Scikit-Learn,... on Amazon

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Read and take notes on 5 chapters
Complete 3 hands-on projects

Create a compilation of learning resources on Google Cloud Platform Data Processing

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This compilation will be a valuable resource for your future reference on this topic.

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Find 10 tutorials on Google Cloud Platform - Data Processing
Find 10 blog posts on Google Cloud Platform - Data Processing
Find 10 videos on Google Cloud Platform - Data Processing
Find 10 case studies on Google Cloud Platform - Data Processing
Save them all to a dedicated compilation portfolio

Career center

Learners who complete Google Cloud Platform Big Data and Machine Learning Fundamentals auf Deutsch will develop knowledge and skills that may be useful to these careers:

Data Analyst

Data Analysts are responsible for collecting, cleaning, and interpreting data to help businesses make better decisions. This course may be helpful for Data Analysts who want to learn how to use Google Cloud Platform (GCP) for Big Data and machine learning. GCP is a powerful platform that can help Data Analysts to manage and analyze large datasets, and to build and train machine learning models.

See salaries and explore the career path for Data Analyst

Data Scientist

Data Scientists are responsible for using data to solve business problems. This course may be helpful for Data Scientists who want to learn how to use GCP for Big Data and machine learning. GCP is a powerful platform that can help Data Scientists to manage and analyze large datasets, and to build and train machine learning models.

See salaries and explore the career path for Data Scientist

Data Engineer

Data Engineers are responsible for designing, building, and maintaining data pipelines. This course may be helpful for Data Engineers who want to learn how to use GCP for Big Data and machine learning. GCP is a powerful platform that can help Data Engineers to manage and analyze large datasets, and to build and train machine learning models.

See salaries and explore the career path for Data Engineer

Cloud Architect

Cloud Architects are responsible for designing and deploying cloud-based solutions. This course may be helpful for Cloud Architects who want to learn how to use GCP for Big Data and machine learning. GCP is a powerful platform that can help Cloud Architects to design and deploy cloud-based solutions that can handle large datasets and machine learning workloads.

See salaries and explore the career path for Cloud Architect

Machine Learning Engineer

Machine Learning Engineers are responsible for designing, building, and deploying machine learning models. This course may be helpful for Machine Learning Engineers who want to learn how to use GCP for Big Data and machine learning. GCP is a powerful platform that can help Machine Learning Engineers to manage and analyze large datasets, and to build and train machine learning models.

See salaries and explore the career path for Machine Learning Engineer

Database Administrator

Database Administrators are responsible for managing and maintaining databases. This course may be helpful for Database Administrators who want to learn how to use GCP for Big Data and machine learning. GCP is a powerful platform that can help Database Administrators to manage and maintain databases that can handle large datasets and machine learning workloads.

See salaries and explore the career path for Database Administrator

Software Engineer

Software Engineers are responsible for designing, building, and testing software applications. This course may be helpful for Software Engineers who want to learn how to use GCP for Big Data and machine learning. GCP is a powerful platform that can help Software Engineers to build and test software applications that can handle large datasets and machine learning workloads.

See salaries and explore the career path for Software Engineer