Data mining & 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 Architecture 3. Data-Warehouses (DW) 4. Relational Databases 5. Transactional Databases 6. Advanced Data and Information Systems and Advanced Applications 7. Data Mining Functionalities 8. Classification of Data Mining Systems 9. Data Mining Task Primitives 10. Integration of a Data Mining System with a DataWarehouse System 11. Major Issues in Data Mining 12. Performance issues in Data Mining 13. Introduction to Data Preprocess 14. Descriptive Data Summarization 15. Measuring the Dispersion of Data 16. Graphic Displays of Basic Descriptive Data Summaries 17. Data Cleaning 18. Noisy Data 19. Data Cleaning Process 20. Data Integration and Transformation 21. Data Transformation 22. Data Reduction 23. Dimensionality Reduction 24. Numerosity Reduction 25. Clustering and Sampling 26. Data Discretization and Concept Hierarchy Generation 27. Concept Hierarchy Generation for Categorical Data 28. Introduction to Data warehouses 29. Differences between Operational Database Systems and Data Warehouses 30. A Multidimensional Data Model 31. A Multidimensional Data Model
License Free
Version 5.3
Operating System Android