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Business Intelligence Assignments help | Business Intelligence Homework Help


 

We at Global web tutors provide expert help for Business Intelligence assignment or Business Intelligence homework. Our Business Intelligence online tutors are expert in providing homework help to students at all levels. Please post your assignment at support@globalwebtutors.com to get the instant Business Intelligence homework help. Business Intelligence online tutors are available 24/7 to provide assignment help as well as Business Intelligence homework help. 

 
Business Intelligence basic principles of Business Intelligence development in BI area IS/ICT architecture core OLAP (On Line Analytical Processing) technologies, demonstration of principles on examples,  BI application areas,  BI in the company management effects of BI applications, database environment data warehouse in MS SQL Server-- principles, architecture, components, management basics,  BI tasks management effect and critical success factors of BI, Planning and analysis design and modeling Data quality management data granularity problems management, BI implementation principles, data pumps, ETL -- principle, documentation in MS -- DTS, ActiveX script, data pumps parameters and logging, Server applications solution, basic principles of MS Analysis Services, functions and options  Client applications solution, ProClarity, MS Office - Excel, Access, export of OLAP cube - off-line solution, query tools in multidimensional databases, Data Mining -- core principles and application options, relations to BI and other applications, relationship between CRM and BI - Customer Intelligence.  BI market segment, BI trends, BI product examples- SAP, Oracle
 
  • introduction to the business uses, value and technologies of business intelligence
  • business intelligence
  • current theories, techniques, frameworks, applications and technologies in business intelligence

 

  • Document crawling, parsing, indexing, searching, large-scale web crawler, wget, curl, Common bottlenecks, pitfalls, multiple queues, multiple downloading processes, DNS pre-fetching, URL normalization, Capitalization, punctuation removal, Stemming, Documents as sets, Jaccard coefficient, multisets, Documents as vectors, definition of Term Frequency, distance in vector spaces, Euclidean, Manhattan, Infinite norm, Cosine similarity, Inverse Document Frequency, Matrix form representation, Querying, Mapping queries in the document space, Bulk calculations of cosine similarities by matrix operations, Performance metrics, Confusion matrix, true and false positive instance, type-1 and type-2 errors, Accuracy, precision, recall metrics, F1 measure, Complexity reduction, de-noising of data
  • Advances in Collaborative Filtering, Gradient Descent method, hierarchical clustering, MinHash algorithm, Changing coordinates to remove correlation, Principal Component Analysis, TF-IDF matrix, attribute-sample matrix, Covariance matrix between attributes, Maximizing the sample variance, Diagonalization of the covariance matrix, Singular Value Decomposition, SVD of a real-valued matrix, complete and reduced form, Small-rank approximation, Frobenius norm, approximation to reduce the computational complexity, similarity-ranking task, Interpretation of the SVD decomposition, latent semantics, Latent Semantic Analysis, Recommender systems
  • latent factors model, SVD-like methods to factorize incomplete matrices, Minimization of the squared errors, Gradient Descent method, Hierarchical clustering, Agglomerative, bottom-up procedure, Distributed computing, MapReduce framework , distributed filesystem, mapper, sorting, aggregation, reducer, access to SAS, Enterprise Miner, Excel Miner, Tableau, Data Exploration, Visualization, Association Rule , Clustering, Dimension Reduction, Multiple Linear Regression, Evaluating Performance, Decision Trees
 

 

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