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    • Data Warehouse

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    「data warehouse」の87件の結果

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      IBM Skills Network

      IBM Data Warehouse Engineer

      習得できるスキル: Apache, Big Data, Business Analysis, Cloud Computing, Cloud Engineering, Computer Architecture, Computer Networking, Computer Programming, Data Architecture, Data Engineering, Data Management, Data Model, Data Visualization, Data Visualization Software, Data Warehousing, Database Administration, Database Application, Database Design, Databases, DevOps, Distributed Computing Architecture, Extract, Transform, Load, Hardware Design, Interactive Data Visualization, Leadership and Management, Microarchitecture, Network Security, NoSQL, PostgreSQL, Professional Development, Programming Principles, Project Management, Python Programming, SQL, Security Engineering, Security Strategy, Software Architecture, Software Engineering, Statistical Programming, Strategy and Operations, Theoretical Computer Science

      4.6

      (17k件のレビュー)

      Beginner · Professional Certificate · 3-6 Months

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      University of Colorado System

      Data Warehousing for Business Intelligence

      習得できるスキル: Accounting, Business Analysis, Business Intelligence, Computational Logic, Computer Graphic Techniques, Computer Graphics, Computer Programming, Data Analysis, Data Architecture, Data Management, Data Mining, Data Visualization, Data Warehousing, Database Administration, Database Application, Database Design, Database Theory, Databases, Decision Making, Entrepreneurship, Extract, Transform, Load, Leadership and Management, Linear Algebra, Mathematical Theory & Analysis, Mathematics, Modeling, PostgreSQL, Programming Principles, SQL, Spreadsheet Software, Statistical Programming, Theoretical Computer Science

      4.5

      (3.6k件のレビュー)

      Advanced · Specialization · 3-6 Months

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      IBM Skills Network

      BI Foundations with SQL, ETL and Data Warehousing

      習得できるスキル: Apache, Business Analysis, Cloud Computing, Cloud Engineering, Computer Architecture, Computer Programming, Data Management, Data Visualization, Data Visualization Software, Data Warehousing, Database Administration, Database Application, Databases, Extract, Transform, Load, Interactive Data Visualization, Microarchitecture, Programming Principles, Python Programming, SQL, Software Architecture, Software Engineering, Statistical Programming, Theoretical Computer Science

      4.6

      (16.1k件のレビュー)

      Beginner · Specialization · 3-6 Months

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      IBM Skills Network

      Data Engineering Foundations

      習得できるスキル: Apache, Big Data, Computational Logic, Computer Architecture, Computer Networking, Computer Programming, Computer Programming Tools, Data Architecture, Data Engineering, Data Management, Data Model, Data Warehousing, Database Administration, Database Application, Database Design, Databases, Distributed Computing Architecture, Extract, Transform, Load, Leadership and Management, Mathematical Theory & Analysis, Mathematics, Network Security, NoSQL, PostgreSQL, Professional Development, Project Management, Python Programming, SQL, Security Engineering, Security Strategy, Statistical Programming, Strategy and Operations, Theoretical Computer Science

      4.6

      (35.3k件のレビュー)

      Beginner · Specialization · 3-6 Months

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      University of California, Irvine

      Data Warehousing and Business Intelligence

      習得できるスキル: Data Analysis, Data Warehousing, Data Mining, Communication, NoSQL, Marketing, Extract, Transform, Load, Data Management, Modeling, Process, Databases, Regression, Data Model, Probability & Statistics

      4.7

      (39件のレビュー)

      Beginner · Course · 1-4 Weeks

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      University of California, Irvine

      Database Design and Operational Business Intelligence

      習得できるスキル: Accounting, Basic Descriptive Statistics, Big Data, Business Analysis, Business Intelligence, Communication, Data Analysis, Data Management, Data Mining, Data Model, Data Visualization, Data Warehousing, Database Design, Databases, Extract, Transform, Load, General Statistics, Machine Learning, Machine Learning Algorithms, Marketing, NoSQL, Probability & Statistics, Regression, SAS (Software), SQL, Statistical Analysis, Statistical Programming

      4.5

      (88件のレビュー)

      Beginner · Specialization · 1-3 Months

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      University of Illinois at Urbana-Champaign

      Cloud Computing

      習得できるスキル: Algorithms, Apache, Big Data, Cloud Computing, Cloud Engineering, Cloud Infrastructure, Computational Thinking, Computer Architecture, Computer Networking, Computer Programming, Cryptography, Data Architecture, Data Management, Data Warehousing, Database Theory, Databases, Deep Learning, Distributed Computing Architecture, Google Cloud Platform, Graph Theory, Leadership and Management, Machine Learning, Mathematics, Network Architecture, Sales, Security Engineering, Software Architecture, Software As A Service, Software Engineering, Tensorflow, Theoretical Computer Science

      4.4

      (1.9k件のレビュー)

      Intermediate · Specialization · 3-6 Months

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      University of Colorado System

      Data Warehouse Concepts, Design, and Data Integration

      習得できるスキル: Data Architecture, Extract, Transform, Load, Computer Graphics, Mathematical Theory & Analysis, Data Management, Data Analysis, Business Analysis, Data Warehousing, Spreadsheet Software, Data Visualization, Data Mining, Programming Principles, Computational Logic, Computer Graphic Techniques, Theoretical Computer Science, Database Theory, Mathematics, Business Intelligence, Databases, Computer Programming

      4.4

      (996件のレビュー)

      Mixed · Course · 1-3 Months

    • Placeholder
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      IBM Skills Network

      IBM Data Analyst

      習得できるスキル: Algebra, Apache, Big Data, Business Analysis, Computational Logic, Computer Programming, Computer Programming Tools, Correlation And Dependence, Data Analysis, Data Analysis Software, Data Management, Data Mining, Data Structures, Data Visualization, Data Visualization Software, Data Warehousing, Database Administration, Database Application, Databases, Econometrics, Exploratory Data Analysis, Extract, Transform, Load, General Statistics, Geovisualization, Leadership and Management, Machine Learning, Mathematical Theory & Analysis, Mathematics, Matplotlib, Microsoft Excel, NoSQL, Operating Systems, Plot (Graphics), Probability & Statistics, Professional Development, Python Programming, Regression, SQL, Software, Spreadsheet Software, Statistical Analysis, Statistical Machine Learning, Statistical Programming, Statistical Visualization, System Programming, Theoretical Computer Science

      4.6

      (52.4k件のレビュー)

      Beginner · Professional Certificate · 3-6 Months

    • Placeholder
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      IBM Skills Network

      IBM Data Engineering

      習得できるスキル: Algorithms, Apache, Big Data, Business Analysis, Cloud Computing, Cloud Engineering, Computational Logic, Computational Thinking, Computer Architecture, Computer Networking, Computer Programming, Computer Programming Tools, Data Architecture, Data Engineering, Data Management, Data Model, Data Visualization, Data Visualization Software, Data Warehousing, Database Administration, Database Application, Database Design, Database Theory, Databases, DevOps, Distributed Computing Architecture, Extract, Transform, Load, Graph Theory, Hardware Design, IBM Cloud, Interactive Data Visualization, Kubernetes, Leadership and Management, Machine Learning, Machine Learning Algorithms, Mathematical Theory & Analysis, Mathematics, Microarchitecture, Network Architecture, Network Security, NoSQL, PostgreSQL, Professional Development, Programming Principles, Project Management, Python Programming, SQL, Security Engineering, Security Strategy, Software Architecture, Software Engineering, Statistical Programming, Strategy and Operations, Theoretical Computer Science

      4.6

      (35.8k件のレビュー)

      Beginner · Professional Certificate · 3-6 Months

    • Placeholder
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      University of California San Diego

      Big Data

      習得できるスキル: Algorithms, Apache, Big Data, Business Analysis, Computer Architecture, Computer Programming, Data Analysis, Data Architecture, Data Clustering Algorithms, Data Management, Data Model, Data Visualization, Data Warehousing, Database Administration, Databases, Distributed Computing Architecture, Exploratory Data Analysis, General Statistics, Graph Theory, Graphs, Machine Learning, Machine Learning Algorithms, Mathematics, Modeling, Mongodb, NoSQL, PostgreSQL, Probability & Statistics, Python Programming, Regression, SQL, Statistical Programming, Theoretical Computer Science

      4.5

      (13.3k件のレビュー)

      Beginner · Specialization · 3-6 Months

    • Placeholder
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      Microsoft

      Modern Data Warehouse Analytics in Microsoft Azure

      習得できるスキル: Analytics, Data Analysis, Data Warehousing, Big Data, Data Visualization, Data Management, Cloud Computing, Process, Power BI, Microsoft Azure

      4.6

      (31件のレビュー)

      Beginner · Course · 1-4 Weeks

    data warehouseに関連する検索

    data warehouse concepts, design, and data integration
    ibm data warehouse engineer
    modern data warehouse analytics in microsoft azure
    creating a data warehouse through joins and unions
    design and build a data warehouse for business intelligence implementation
    modernizing data lakes and data warehouses with google cloud
    relational database support for data warehouses
    modernizing data lakes and data warehouses with gcp 日本語版
    1234…8

    要約して、data warehouse の人気コース10選をご紹介します。

    • IBM Data Warehouse Engineer: IBM Skills Network
    • Data Warehousing for Business Intelligence: University of Colorado System
    • BI Foundations with SQL, ETL and Data Warehousing: IBM Skills Network
    • Data Engineering Foundations: IBM Skills Network
    • Data Warehousing and Business Intelligence: University of California, Irvine
    • Database Design and Operational Business Intelligence: University of California, Irvine
    • Cloud Computing: University of Illinois at Urbana-Champaign
    • Data Warehouse Concepts, Design, and Data Integration: University of Colorado System
    • IBM Data Analyst: IBM Skills Network
    • IBM Data Engineering: IBM Skills Network

    Machine Learningで学べるスキル

    Pythonプログラミング (33)
    TensorFlow (32)
    ディープラーニング (30)
    人工ニューラルネットワーク (24)
    ビッグデータ (18)
    統計的分類 (17)
    強化学習 (13)
    代数 (10)
    ベイズ (10)
    線型代数学 (10)
    線形回帰 (9)
    NumPy (9)

    Data Warehouseに関するよくある質問

    • 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.‎

    このFAQの内容は、情報提供のみを目的としています。受講生は、自分の個人的、職業的、経済的な目標に合ったコースやその他の資格を取得するために、さらに調べることをお勧めします。
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