This course builds on “The Nature of Data and Relational Database Design” to extend the process of capturing and manipulating data through data warehousing and data mining. Once the transactional data is processed through ETL (Extract, Transform, Load), it is then stored in a data warehouse for use in managerial decision making. Data mining is one of the key enablers in the process of converting data stored in a data warehouse into actionable insight for better and faster decision making.
제공자:
Database Design and Operational Business Intelligence 특화 과정캘리포니아 대학교 어바인 캠퍼스이 강좌에 대하여
최근 조회 5,913회
유연한 마감일
일정에 따라 마감일을 재설정합니다.
공유 가능한 수료증
완료 시 수료증 획득
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
다음 특화 과정의 3개 강좌 중 2번째 강좌:
초급 단계
No experience in BI or database needed. Experience with at least one programming language is recommended.
완료하는 데 약 7시간 필요
영어
자막: 영어
배울 내용
Explain different data warehousing architectures and multidimensional data modeling
Develop predictive data mining models, including classification and estimation models
Develop explanatory data mining models, including clustering and association models
귀하가 습득할 기술
- Data Mining for Clustering and Association
- Data Clustering Algorithms
- Data Warehousing
- Multidimensional Modeling
- Data Mining for Prediction and Explanation
유연한 마감일
일정에 따라 마감일을 재설정합니다.
공유 가능한 수료증
완료 시 수료증 획득
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
다음 특화 과정의 3개 강좌 중 2번째 강좌:
초급 단계
No experience in BI or database needed. Experience with at least one programming language is recommended.
완료하는 데 약 7시간 필요
영어
자막: 영어
제공자:
캘리포니아 대학교 어바인 캠퍼스
Since 1965, the University of California, Irvine has combined the strengths of a major research university with the bounty of an incomparable Southern California location. UCI’s unyielding commitment to rigorous academics, cutting-edge research, and leadership and character development makes the campus a driving force for innovation and discovery that serves our local, national and global communities in many ways.
강의 계획표 - 이 강좌에서 배울 내용
완료하는 데 2시간 필요
Overview of Data Warehousing
Welcome to Module 1, Overview of Data Warehousing. In this module, we will overview data warehousing and data warehousing architectures. We will also define the Extract, Transform, Load (ETL) process as well as touch on data warehousing in the cloud and practice these through a short quiz. Finally, in our activity we will differentiate between the Kimball and Inmon design approaches for data warehouse architecture.
완료하는 데 2시간 필요
7 개의 읽기 자료
완료하는 데 2시간 필요
Multidimensional Modeling for Data Warehousing
Welcome to Module 2, Multidimensional Modeling for Data Warehousing. In this module, we will go over data modeling for data warehousing. We will also learn the steps needed to construct a multidimensional data model and differentiate between star schema and snowflake schema. These will be practiced through a short quiz. Finally, we will create a normalized snowflake schema in our activity.
완료하는 데 2시간 필요
6 개의 읽기 자료
완료하는 데 2시간 필요
Data Mining for Prediction and Explanation
Welcome to Module 3, Data Mining for Prediction and Explanation. In this module, we will overview the data mining process and data mining methods. We will also identify the steps in a data mining process and differentiate between data mining methods. We will practice identifying these through a short quiz. In our activity, we will also select what data mining methods are best for a particular data set.
완료하는 데 2시간 필요
5 개의 읽기 자료
완료하는 데 2시간 필요
Data Mining for Clustering and Association
Welcome to Module 4, Data Mining for Clustering and Association. In this module, we will go over unsupervised data mining for explanatory modeling. We will also learn the definitions for clustering and segmentation, K-means clustering, association, and market basket analysis and practice these through a short quiz. Finally we will practice identifying clusters in a dataset through our activity.
완료하는 데 2시간 필요
4 개의 읽기 자료
Database Design and Operational Business Intelligence 특화 과정 정보
The goal of this specialization is to provide a comprehensive and holistic view of business intelligence and its enabling technologies, including relational databases, data warehousing, descriptive statistics, data mining, and visual analytics. Through this series of courses, you will explore relational database design, data manipulation through Extract/Transform/Load (ETL), gaining actionable insight through data analytics, data-based decision support, data visualization, and practical, hands-on experience with real-world business intelligence tools.
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