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所在平台: Coursera专项课程 |
课程主页: https://www.coursera.org/specializations/hands-on-data-science-machine-learning
课程评论：没有评论
课程名称：使用Google Cloud Labs的实用数据科学和机器学习基础 概述：本课程将指导您学习和实践使用Google Cloud工具进行数据集的采集、准备、处理、查询、探索和可视化的所有方面。 课程内容与技能： - 学习并掌握BigQuery、数据分析、SQL、数据管理、数据管道、Cloud Data Fusion、数据采集、数据处理、数据可视化（DataViz）、TensorFlow及机器学习等技能。 关于该专项课程： 这是一个包含四个课程的Google Cloud Labs专项，您将获得在BigQuery和Cloud Data Fusion上实际操作的经验。课程内容包括BigQuery的基础知识、仓库的构建与优化，以及Cloud Data Fusion的高级数据集成功能的实际操作。 学习平台：通过Google Cloud的Qwiklab平台，您将获得完成每个实验室所需的虚拟环境和资源。课程要求学习者应具备一定的SQL和机器学习基础，预计完成时间为大约一个月，建议每周学习5小时。 课程列表： 1. **BigQuery基础知识（为数据分析师）**： 学习如何高效查询与分析数据仓库中的数据，并理解BigQuery的最佳实践。 2. **在Cloud Data Fusion中构建高级无代码管道**： 通过实际操作掌握Cloud Data Fusion的高级数据集成功能，学习建立更强大、可重用的动态管道。 3. **Google Cloud上的数据科学**： 实践数据集的采集、准备、处理、查询、探索与可视化，运用Google Cloud的工具与服务。 4. **Google Cloud上的数据科学：机器学习**： 利用现代工具与真实数据集运行完整的机器学习任务，深入了解机器学习的应用。 完成该课程后，您将获得可共享的证书，并经历一系列实践项目，深化理论知识与实际技能的结合。
Course: 1
Course Link: https://www.coursera.org/learn/bigquery-basics-data-analysts?specialization=hands-on-data-science-machine-learning
Title:BigQuery Basics for Data Analysts
Description: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 collection of BigQuery Google Cloud Labs Series. 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.
Course: 2
Title:Building Advanced Codeless Pipelines on Cloud Data Fusion
Description:In this Google Cloud Labs Series, learners get hands-on practice on the more advanced data integration features available in Cloud Data Fusion, while sharing best practices to build more robust, reusable, dynamic pipelines. Learners get to try out the data lineage feature as well to derive interesting insights into their data’s history.
Course: 3
Course Link: https://www.coursera.org/learn/data-science-google-cloud?specialization=hands-on-data-science-machine-learning
Title:Data Science on Google Cloud
Description:Activities in these self-paced labs are derived from the exercises from the book Data Science on Google Cloud Platform by Valliappa Lakshmanan, published by O'Reilly Media, Inc. In this first Google Cloud Labs Series, 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.
Course: 4
Title:Data Science on Google Cloud: Machine Learning
Description:Activities in these self-paced labs are derived from the exercises from the book Data Science on Google Cloud Platform by Valliappa Lakshmanan, published by O'Reilly Media, Inc. In this Google Cloud Labs Series, covering chapter 9 through the end of the book, you run full-fledged machine learning jobs with state-of-the-art tools and real-world data sets, all using Google Cloud tools and services.
What you will learn
and practice all aspects of ingestion, preparation, processing, querying, exploring and visualizing data sets using Google Cloud tools.
Skills you will gain
Bigquery
Data Analysis
data
SQL
Data Management
Data Pipelines
Cloud Data Fusion
Data ingestion
Data Processing
Data Visualization (DataViz)
Tensorflow
Machine Learning
About this Specialization
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In this Google Cloud Labs Specialization, you'll receive hands-on experience building and practicing skills in BigQuery and Cloud Data Fusion. You will start learning the basics of BigQuery, building and optimizing warehouses, and then get hands-on practice on the more advanced data integration features available in Cloud Data Fusion.
Learning will take place leveraging Google Cloud's Qwiklab platform where you will have the virtual environment and resources need to complete each lab.
This specialization is broken up into 4 courses comprised of a series of courses:
BigQuery Basics for Data Analysts Building Advanced Codeless Pipelines on Cloud Data Fusion
Data Science on Google Cloud Data Science on Google Cloud: Machine Learning
You will even be able to earn a Skills Badge in one of these lab-based courses.
Applied Learning Project
This specialization leverages hands-on labs using our Qwiklabs platform. You can expect to gain practical hands-on experience with the concepts explained throughout each lab.
Learners will be able to practice:
Creating dataset partitions that will reduce cost and improve query performance.
Using macros in Data Fusion that introduce dynamic variables to plugin configurations so that you can specify the variable substitutions at runtime.
Building a reusable pipeline that reads data from Cloud Storage, performs data quality checks, and writes to Cloud Storage.
Using Google Cloud Machine Learning and TensorFlow to develop and evaluate prediction models using machine learning.
Implementing logistic regression using a machine learning library for Apache Spark running on a Google Cloud Dataproc cluster to develop a model for data from a multivariable dataset.
And much more!
Shareable Certificate
Shareable Certificate
Earn a Certificate upon completion
100% online courses
100% online courses
Start instantly and learn at your own schedule.
Flexible Schedule
Flexible Schedule
Set and maintain flexible deadlines.
Intermediate Level
Intermediate Level
You should have some familiarity with SQL and ML basics. Some course-based labs have recommended backgrounds. Please read them carefully.
Hours to complete
Approximately 1 month to complete
Suggested pace of 5 hours/week
Available languages
English
Subtitles: English
Shareable Certificate
Shareable Certificate
Earn a Certificate upon completion
100% online courses
100% online courses
Start instantly and learn at your own schedule.
Flexible Schedule
Flexible Schedule
Set and maintain flexible deadlines.
Intermediate Level
Intermediate Level
You should have some familiarity with SQL and ML basics. Some course-based labs have recommended backgrounds. Please read them carefully.
Hours to complete
Approximately 1 month to complete
Suggested pace of 5 hours/week
Available languages
English
Subtitles: English
