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.
Этот курс входит в специализацию ''Специализация Database Design and Operational Business Intelligence'
от партнера
Специализация Database Design and Operational Business IntelligenceКалифорнийский университет в ИрвайнеОб этом курсе
Недавно просмотрено: 7 380
Гибкие сроки
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Сертификат, ссылками на который можно делиться с другими людьми
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100% онлайн
Начните сейчас и учитесь по собственному графику.
Курс 2 из 3 в программе
Начальный уровень
No experience in BI or database needed. Experience with at least one programming language is recommended.
Прибл. 7 часов на выполнение
Английский
Субтитры: Английский
Чему вы научитесь
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
Приобретаемые навыки
- Data Mining for Clustering and Association
- Data Clustering Algorithms
- Data Warehousing
- Multidimensional Modeling
- Data Mining for Prediction and Explanation
Гибкие сроки
Назначьте сроки сдачи в соответствии со своим графиком.
Сертификат, ссылками на который можно делиться с другими людьми
Получите сертификат по завершении
100% онлайн
Начните сейчас и учитесь по собственному графику.
Курс 2 из 3 в программе
Начальный уровень
No experience in BI or database needed. Experience with at least one programming language is recommended.
Прибл. 7 часов на выполнение
Английский
Субтитры: Английский
от партнера
Калифорнийский университет в Ирвайне
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.
Программа курса: что вы изучите
2 ч. на завершение
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.
2 ч. на завершение
7 материалов для самостоятельного изучения
2 ч. на завершение
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.
2 ч. на завершение
6 материалов для самостоятельного изучения
2 ч. на завершение
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.
2 ч. на завершение
5 материалов для самостоятельного изучения
2 ч. на завершение
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.
2 ч. на завершение
4 материалов для самостоятельного изучения
Рецензии
- 5 stars74,41 %
- 4 stars18,60 %
- 3 stars2,32 %
- 2 stars2,32 %
- 1 star2,32 %
Лучшие отзывы о курсе DATA WAREHOUSING AND BUSINESS INTELLIGENCE
от партнера SS19 февр. 2022 г.
This course is an excellent and simple introduction for DWH which is easy to grasp and follow.
Специализация Database Design and Operational Business Intelligence: общие сведения
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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