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    • Data Warehouse

    Data Warehouse Courses Online

    Explore data warehousing for large-scale data storage and analysis. Learn to design, build, and manage data warehouses using platforms like Snowflake and Redshift.

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    Explore the Data Warehouse Course Catalog

    • Status: New
      New
      G

      Google Cloud

      BigQuery 머신러닝을 사용한 추론

      Skills you'll gain: Data Analysis, Big Data, MLOps (Machine Learning Operations), Applied Machine Learning, Google Cloud Platform, Predictive Modeling, Machine Learning

      Beginner · Course · 1 - 4 Weeks

    • Status: New
      New
      G

      Google Cloud

      Boost Productivity with Gemini in BigQuery - 한국어

      Skills you'll gain: Exploratory Data Analysis, Prompt Engineering, Generative AI, Data Visualization Software, Google Cloud Platform, Data Cleansing, Data Transformation, Debugging, Natural Language Processing

      Beginner · Course · 1 - 4 Weeks

    • P

      Pontificia Universidad Católica de Chile

      Certificado en Toma de Decisiones Basadas en Datos

      Skills you'll gain: Descriptive Analytics, Business Analytics, Data Mining, Database Design, NoSQL, SQL, Data Ethics, Query Languages, Digital Transformation, Predictive Analytics, Big Data, Data Management, Business Intelligence, Data Visualization, Statistical Methods, Data-Driven Decision-Making, Competitive Analysis, Business Strategy, Strategic Thinking, Business Strategies

      Credit offered

      Mastertrack · 6 - 12 Months

    • G

      Google Cloud

      Responsible AI for Developers: Fairness & Bias - Deutsch

      Skills you'll gain: Data Ethics, Google Cloud Platform, Artificial Intelligence, Artificial Intelligence and Machine Learning (AI/ML), Open Source Technology, Machine Learning, Data Validation, Data Quality

      Intermediate · Course · 1 - 3 Months

    • G

      Google Cloud

      Smart Analytics, Machine Learning and AI on GC - 한국어

      Skills you'll gain: Google Cloud Platform, Unstructured Data, Tensorflow, MLOps (Machine Learning Operations), Data Pipelines, Big Data, Applied Machine Learning, Artificial Intelligence and Machine Learning (AI/ML), Machine Learning, Jupyter, Natural Language Processing

      Intermediate · Course · 1 - 3 Months

    • Status: New
      New
      G

      Google Cloud

      MMachine Learning Operations (MLOps) with Vertex AI: Model Evaluation - 한국어

      Skills you'll gain: Generative AI, Continuous Monitoring, Google Cloud Platform, MLOps (Machine Learning Operations), Predictive Modeling, Machine Learning Methods, Data Quality

      Intermediate · Course · 1 - 4 Weeks

    • P

      Pontificia Universidad Católica de Chile

      Magíster en Business Analytics

      Skills you'll gain: Customer Analysis, Descriptive Analytics, Revenue Management, FinTech, Data Mining, Database Design, People Analytics, Time Series Analysis and Forecasting, Biostatistics, Predictive Analytics, Digital Transformation, Network Analysis, Cloud Security, Risk Analysis, Assertiveness, Bayesian Statistics, Web Scraping, Deep Learning, Governance, Dimensionality Reduction

      Earn a degree

      Degree · 1 - 4 Years

    • G

      Google Cloud

      Boost Productivity with Gemini in BigQuery - 简体中文

      Skills you'll gain: Data Pipelines, Exploratory Data Analysis, Google Cloud Platform, Data Cleansing, Interactive Data Visualization, Data Transformation, Data Analysis, Generative AI, SQL

      Beginner · Course · 1 - 4 Weeks

    • N

      Northeastern University

      Master of Science in Data Analytics Engineering

      Skills you'll gain:

      Earn a degree

      Degree · 1 - 4 Years

    • Status: New
      New
      G

      Google Cloud

      Networking in Google Cloud: Fundamentals - Português Brasileiro

      Skills you'll gain: Network Monitoring, Google Cloud Platform, Virtual Private Networks (VPN), Event Monitoring, Computer Networking, Network Architecture, Cloud Infrastructure, Network Troubleshooting, Network Performance Management, System Monitoring, Virtual Machines, Service Level, Data Sharing

      Intermediate · Course · 1 - 3 Months

    • G

      Google Cloud

      Gemini for end-to-end SDLC - 한국어

      Skills you'll gain: Software Development Life Cycle, Google Cloud Platform, Development Testing, Application Lifecycle Management, Web Applications, CI/CD, Application Development, Cloud Development, Software Development, Test Case, Generative AI, Query Languages, Debugging, Data Access

      Beginner · Course · 1 - 4 Weeks

    • G

      Google Cloud

      Google Cloud Customer Care Fundamentals - 한국어

      Skills you'll gain: Product Support, Help Desk Support, Technical Support, Google Cloud Platform, Service Level, Service Management, Cloud Services, Issue Tracking, Problem Management, Technical Communication, Personally Identifiable Information, Information Privacy

      Beginner · Course · 1 - 4 Weeks

    Data Warehouse learners also search

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    In summary, here are 10 of our most popular data warehouse courses

    • BigQuery 머신러닝을 사용한 추론: Google Cloud
    • Boost Productivity with Gemini in BigQuery - 한국어: Google Cloud
    • Certificado en Toma de Decisiones Basadas en Datos: Pontificia Universidad Católica de Chile
    • Responsible AI for Developers: Fairness & Bias - Deutsch: Google Cloud
    • Smart Analytics, Machine Learning and AI on GC - 한국어: Google Cloud
    • MMachine Learning Operations (MLOps) with Vertex AI: Model Evaluation - 한국어: Google Cloud
    • Magíster en Business Analytics: Pontificia Universidad Católica de Chile
    • Boost Productivity with Gemini in BigQuery - 简体中文: Google Cloud
    • Master of Science in Data Analytics Engineering: Northeastern University
    • Networking in Google Cloud: Fundamentals - Português Brasileiro: Google Cloud

    Skills you can learn in Machine Learning

    Python Programming (33)
    Tensorflow (32)
    Deep Learning (30)
    Artificial Neural Network (24)
    Big Data (18)
    Statistical Classification (17)
    Reinforcement Learning (13)
    Algebra (10)
    Bayesian (10)
    Linear Algebra (10)
    Linear Regression (9)
    Numpy (9)

    Frequently Asked Questions about Data Warehouse

    A data warehouse is an organized collection of structured data that is used for applications such as reporting, analytics, or business intelligence. Traditional, on-premise data warehouses are still maintained by hospitals, universities, and large corporations, but these are expensive and space-consuming by today’s standards. Instead, data warehouse solutions like Google BigQuery and Amazon Redshift are allowing organizations of all sizes to benefit from the scalability and cost-effectiveness of cloud computing.

    Data warehouses are important to learn about because they enable organizations to make data-driven decisions that can inform daily operations as well as future strategic initiatives. Analysts can use the data integration and extraction, transformation, loading (ETL) capabilities of business intelligence software like Pentaho to query and present information visually for maximum impact.

    Working with diverse big data sets may require the use of a data lake, which is similar to a data warehouse but can take in all types of data - structured, unstructured, and raw. With software like Apache Hive, data scientists can sort and analyze data delivered by data lake solutions like Amazon S3 and Microsoft Azure to generate the real-time and predictive insights big data can provide.‎

    An organization’s data warehouse is one of its most valuable assets, and it is important for personnel across a wide variety of departments to understand how to access it and how to leverage its data effectively. For example, quantitative financial analysts may use data warehouses to feed machine learning algorithms for stock trading; digital marketing experts rely on detailed datasets on consumer buying behavior; and supply chain managers analyze operational data to guide process improvements.

    Data engineers are the primary personnel responsible for the design and maintenance of the data infrastructure of an organization, including not only data warehouses but data pipelines, data lakes, and databases. They must be familiar with programming languages and frameworks like Python, SQL, and Java, and usually have at least a bachelor’s degree or even a master’s degree in computer engineering or computer science. The national average salary for a data engineer according to Glassdoor is $103,864 per year.‎

    Yes - in fact, data science topics like data warehousing are some of the most popular online learning opportunities on Coursera. There are a wide variety of online courses and Specializations available on data warehousing, as well as related courses on data engineering, database management, and business intelligence. And you can take courses from top-ranked institutions and industry leaders, including the University of Colorado, the University of California Davis, and Google Cloud, so you don’t have to sacrifice the quality of your education for the opportunity to learn online.‎

    Before starting to learn about data warehouse concepts, it’s extremely important to already have strong computer programming skills, a data architecture background, and insights in working with teams of developers, systems architects, and other high-level developers. Designers of data warehouses tend to have computer science degrees and fundamental knowledge of data integration processes. These skills are crucial to working in this field, as businesses rely heavily on data and analytics to stay competitive. Data moves from transaction systems and company databases into warehouses. This data is then extracted into company reports, dashboards, and analytics tools to help guide their decision-making. Having strong experience in data architectures and company operations will be a well-guided step to learning the business of data warehouse.‎

    Clearly, business analysts, data engineers, and data scientists are all best suited for work that involves data warehousing. Computer programmers are often at the foundation in a data warehouse, using their programming skills and analytical thinking to extract the data. Once extracted, results are usually presented in the top tier via a front-end client through reporting, analysis, and data mining tools. An analytics engine exists in the middle tier to access and analyze the data. And finally, data is loaded and stored in the database server, in the bottom tier of the architecture. These data professionals working in a data warehouse can then present their findings to senior company leaders through reports, charts, and spreadsheets, to help them make important decisions.‎

    Learning about data warehouse work might be a great fit for you if you are organized, have a computer software design background, and may even be already working in a data engineer type role or aspiring to work somehow with big data and cloud serving technology. These aspects are all crucial to data warehouse work, which is focused on extracting data from real-world transactional behavior. For instance, a data warehouse may combine customer information from a company's POS system, its email lists, and its physical mailing lists into one large database. If you are the sort of person who is passionate about learning about business intelligence tools and how a data warehouse can coordinate this data for you, then learning data warehouse information may certainly be a great path ahead.‎

    Online Data Warehouse courses offer a convenient and flexible way to enhance your knowledge or learn new Data Warehouse skills. Choose from a wide range of Data Warehouse courses offered by top universities and industry leaders tailored to various skill levels.‎

    When looking to enhance your workforce's skills in Data Warehouse, it's crucial to select a course that aligns with their current abilities and learning objectives. Our Skills Dashboard is an invaluable tool for identifying skill gaps and choosing the most appropriate course for effective upskilling. For a comprehensive understanding of how our courses can benefit your employees, explore the enterprise solutions we offer. Discover more about our tailored programs at Coursera for Business here.‎

    This FAQ content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

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