Data mining & Data Warehousing for Android

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Developer’s Description

Learn more about data mining and data warehousing.

The app is a complete free handbook of Data mining & Data Warehousing which cover important topics, notes, materials, news & blogs on the course. Download the App as a reference material & digital book for computer science, AI, data science & software engineering programs & business management degree courses. This useful App lists 200 topics with detailed notes, diagrams, equations, formulas & course material, the topics are listed in 5 chapters. The app is must have for all the computer science & engineering students & professionals. The app provides quick revision and reference to the important topics like a detailed flash card notes, it makes it easy & useful for the student or a professional to cover the course syllabus quickly before an exams or interview for jobs. Track your learning, set reminders, edit the study material, add favorite topics, share the topics on social media. You can also blog about engineering technology, innovation, engineering startups, college research work, institute updates, Informative links on course materials & education programs from your smartphone or tablet or at http://www.engineeringapps.net/. Use this useful engineering app as your tutorial, digital book, a reference guide for syllabus, course material, project work, sharing your views on the blog. Some of the topics Covered in the app are:1. Introduction to Data mining 2. Data Architecture3. Data-Warehouses (DW)4. Relational Databases5. Transactional Databases6. Advanced Data and Information Systems and Advanced Applications7. Data Mining Functionalities8. Classification of Data Mining Systems9. Data Mining Task Primitives10. Integration of a Data Mining System with a DataWarehouse System11. Major Issues in Data Mining12. Performance issues in Data Mining13. Introduction to Data Preprocess14. Descriptive Data Summarization15. Measuring the Dispersion of Data16. Graphic Displays of Basic Descriptive Data Summaries17. Data Cleaning18. Noisy Data19. Data Cleaning Process20. Data Integration and Transformation21. Data Transformation22. Data Reduction23. Dimensionality Reduction24. Numerosity Reduction25. Clustering and Sampling26. Data Discretization and Concept Hierarchy Generation27. Concept Hierarchy Generation for Categorical Data28. Introduction to Data warehouses29. Differences between Operational Database Systems and Data Warehouses30. A Multidimensional Data Model31. A Multidimensional Data Model