Top 4 Apps Similar to ASP.NET INTERVIEW QUESTIONS
.Net Framework Interview 1.0.3
.Net Framework Programming Interview app which almost coverbasicsinterview questions for computer science engineering,b.tech,mca,bca,ms student & IT,CS freshers. An overview of the.NETframework.Common Language Runtime (CLR),.NET Frameworkclasslibrary (FCL),ASP.NET,ADO.NET,Visual Studio .NET,C#,BasicIO,including output to the console and messagesboxes,Datatypes,Arithmetic operations and expressions,Relationaland logicaloperations,Control structures. These include "if","while","do-while", "for", and "switch",Namespaces and methodssupplied bythe FCL,Writing methods,Recursion,overloading,Scopingrules,Arraysand datarepresentation,Classdefinitions,Properties,indexers,accesscontrol,Inheritance andpolymorphism,Delegates,Exceptionhandling,GUI Programming,to buildGUI applications,eventhandling,dialog boxes andmenus,Files,databases,The Framework ClassLibrary(FCL),Containers,Regular expressions,The mail classes,readandwrite mail. Internetclasses,Processes,Multithreading,Graphicprogramming,Newer featuresin .NET,Generic Programming,Languageinteroperability.
MyInterview 1.0
Features:1. One place solution for all type of topics.2. MyInterview can be use off-line.3. Question and Answers can be updated by refreshoptionwithoutupdating the app.4. Simple in use.5. Availability of new questions will get notify.6. Variety of topics.7. Topics currently available,C#ASP.NetSQLLINQJQueryHTML5Angular JSAJAX
Data mining & Data Warehousing 7
The app is a complete free handbook of Data mining &DataWarehousing which cover important topics, notes, materials,news& blogs on the course. Download the App as a referencematerial& digital book for computer science, AI, datascience& software engineering programs & businessmanagementdegree courses. This useful App lists 200 topicswithdetailed notes, diagrams, equations, formulas &coursematerial, the topics are listed in 5 chapters. The app ismust havefor all the computer science & engineering students&professionals. The app provides quick revision andreferenceto the important topics like a detailed flash card notes,it makesit easy & useful for the student or a professional tocover thecourse syllabus quickly before an exams or interviewforjobs. Track your learning, set reminders, edit thestudymaterial, add favorite topics, share the topics onsocialmedia. You can also blog about engineeringtechnology,innovation, engineering startups, college researchwork,institute updates, Informative links on course materials&education programs from your smartphone or tablet orathttp://www.engineeringapps.net/. Use this usefulengineeringapp as your tutorial, digital book, a reference guidefor syllabus,course material, project work, sharing your views ontheblog. 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. TransactionalDatabases 6. AdvancedData and Information Systems and AdvancedApplications 7. DataMining Functionalities 8. Classification ofData Mining Systems 9.Data Mining Task Primitives 10. Integrationof a Data Mining Systemwith a DataWarehouse System 11. Major Issuesin Data Mining 12.Performance issues in Data Mining 13.Introduction to DataPreprocess 14. Descriptive Data Summarization15. Measuring theDispersion of Data 16. Graphic Displays of BasicDescriptive DataSummaries 17. Data Cleaning 18. Noisy Data 19. DataCleaningProcess 20. Data Integration and Transformation 21.DataTransformation 22. Data Reduction 23. Dimensionality Reduction24.Numerosity Reduction 25. Clustering and Sampling 26.DataDiscretization and Concept Hierarchy Generation 27.ConceptHierarchy Generation for Categorical Data 28. Introductionto Datawarehouses 29. Differences between Operational DatabaseSystems andData Warehouses 30. A Multidimensional Data Model 31.AMultidimensional Data Model 32. Data Warehouse Architecture 33.TheProcess of Data Warehouse Design 34. A Three-Tier DataWarehouseArchitecture 35. Data Warehouse Back-End Tools andUtilities 36.Types of OLAP Servers: ROLAP versus MOLAP versus HOLAP37. DataWarehouse Implementation 38. Data Warehousing to DataMining 39.On-Line Analytical Processing to On-Line AnalyticalMining 40.Methods for Data Cube Computation 41. Multiway ArrayAggregationfor Full Cube Computation 42. Star-Cubing: ComputingIceberg CubesUsing a Dynamic Star-tree Structure 43. Pre-computingShellFragments for Fast High-Dimensional OLAP 44. DrivenExploration ofData Cubes 45. Complex Aggregation at MultipleGranularity: Multifeature Cubes 46. Attribute-Oriented Induction47.Attribute-Oriented Induction for Data Characterization48.Efficient Implementation of Attribute-Oriented Induction 49.MiningClass Comparisons: Discriminating between Different Classes50.Frequent patterns 51. The Apriori Algorithm 52. Efficientandscalable frequently itemset mining methods Each topic iscompletewith diagrams, equations and other forms ofgraphicalrepresentations for better learning and quickunderstanding. Data mining & Data Warehousing is part ofcomputer science,software engineering, AI, Machine learning &StatisticalComputing education course and information technology&business management degree programs at variousuniversities.
