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Analytics.in
Resources on Business Intelligence, Data Warehousing & Data Mining
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Analytics.in provides a comprehensive directory for web resources on business intelligence, data warehousing & data mining.
Resources available for the following areas:
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Some interesting & popular finance related web searches (click on the link next to the search term for search results from Google)
Analytics
Related fields - Operations Research, Statistics and Probability
Statistical analysis and data mining
Enterprise decision management
Marketing analytics
Predictive science
Strategy science
Credit risk analysis
Fraud analytics
Business analytics - Portfolio Analysis
Predictive analytics - Encompasses a variety of techniques from statistics and data mining that process current and historical data in order to make “predictions” about future events. In business, the models often process historical and transactional data to identify the risk or opportunity associated with a specific customer or transaction. These analyses weigh the relationship between many data elements to isolate each customer’s risk or potential, which guides the action on that customer. Predictive analytics is widely used in making customer decisions. One of the most well-known applications is credit scoring, which is used throughout financial services. Scoring models process a customer’s credit history, loan application, customer data, etc., in order to rank-order individuals by their likelihood of making future credit payments on time. Predictive analytics are also used in insurance, telecommunications, retail, travel, healthcare, pharmaceuticals and other fields. (Source: Wikipedia article on Predictive analytics - http://en.wikipedia.org/wiki/Predictive_analytics ) 1 Types of predictive analytics 1.1 Predictive models 1.2 Descriptive models 1.3 Decision models 2 Predictive analytics 2.1 Definition 2.2 Current uses 2.2.1 Analytical Customer Relationship Management (CRM) 2.2.2 Direct marketing 2.2.3 Cross-sell 2.2.4 Customer retention 2.2.5 Underwriting 2.2.6 Collection analytics 2.2.7 Fraud detection 2.2.8 Portfolio, product or economy level prediction 2.3 Statistical techniques 2.3.1 Regression Techniques 2.3.1.1 Linear Regression Model 2.3.2 Discrete choice models 2.3.2.1 Logistic regression 2.3.2.2 Multinomial logit regression 2.3.2.3 Probit regression 2.3.2.4 Logit vs. Probit 2.3.3 Time series models 2.3.4 Survival or duration analysis 2.3.5 Classification and regression trees 2.3.6 Multivariate regression splines 2.4 Machine learning techniques 2.4.1 Neural networks 2.4.2 Radial basis functions 2.4.3 Support vector machines 2.4.4 Naïve Bayes 2.4.5 k-nearest neighbours Popular Predictive Analytics Tools KnowledgeSEEKER or KXEN SPSS SAS modules such as STAT, ETS, OR etc. SPLUS STATA. KnowledgeSTUDIO Clementine KXEN Analytic Framework Excel Miner Classification Tree analysis using CART software. R WEKA RapidMiner (formerly YALE)
Data mining Related terms to Data Mining Knowledge discovery Knowledge Discovery Metamodel (KDM) Common Warehouse Metamodel stepwise regression methods [edit] Structured Data Mining Concept mining Database mining Relational data mining Database Document warehouse Data warehouse Graph mining Molecule mining Sequence mining Data stream mining Learning from time-varying data streams under concept drift Tree mining Decision tree learning Web mining Software mining Unstructured Data Mining - Text mining, Image mining
Machine learning
List of software engineering topics
Statistics
OLAP Online Analytical Processing, or OLAP, is an approach to quickly providing answers to analytical queries that are multidimensional in nature. OLAP is part of the broader category business intelligence, which also includes Extract transform load (ETL), relational reporting and data mining. The typical applications of OLAP are in business reporting for sales, marketing, management reporting, business process management (BPM), budgeting and forecasting, financial reporting and similar areas. The term OLAP was created as a slight modification of the traditional database term OLTP (Online Transaction Processing). 1 Functionality 2 Aggregations 3 Types 3.1 Multidimensional 3.2 Relational 3.3 Hybrid 3.4 Comparison 3.5 Other types 4 APIs and query languages 5 Products 5.1 History 5.2 Market shares 6 See also 7 External links Functionality In the core of any OLAP system is a concept of an OLAP cube (also called a multidimensional cube or an hypercube). Aggregations Types OLAP systems have been traditionally categorized using the following taxonomy Other types The following acronyms are also used sometimes, although they are not as widespread as the ones above: WOLAP - Web-based OLAP DOLAP - Desktop OLAP RTOLAP - Real-Time OLAP SOLAP - Spatial OLAP (see Location intelligence) Products & Companies Microsoft Corporation - 31.6% Hyperion Solutions Corporation - 18.9% Cognos - 12.9% Business Objects - 7.3% MicroStrategy - 7.3% SAP AG - 5.8% Cartesis SA - 3.7% Applix - 3.6% Infor - 3.5% Oracle Corporation - 2.8%
Business intelligence Business intelligence software and applications includes a range of tools. Some BI applications are used to analyze performance, projects, or internal operations, such as AQL - Associative Query Logic; Scorecarding; Business activity monitoring; Business Performance Management and Performance Measurement; Business Planning; Business Process Re-engineering; Competitive Analysis; User/End-user Query and Reporting;Enterprise Management systems; Executive Information Systems (EIS); Supply Chain Management/Demand Chain Management; and Finance and Budgeting tools.
Other BI applications are used to store and analyze data, such as Data mining (DM), Data Farming, and Data warehouses;Decision Support Systems (DSS) and Forecasting; Document warehouses and Document Management;Knowledge Management; Mapping, Information visualization, and Dashboarding; Management Information Systems (MIS);Geographic Information Systems (GIS); Trend Analysis; Software as a service (SaaS) Business Intelligence offerings (On Demand)- similar to traditional BI solutions but software is hosted for customers by a provider; Online analytical processing (OLAP) and multidimensional analysis; sometimes called "Analytics" (based on the "hypercube" or "cube"); Real time business intelligence; Statistics and Technical Data Analysis; Web Mining, Text mining and Systems intelligence. Other BI applications are used to analyze or manage the "human" side of businesses, such as Customer Relationship Management (CRM) and Marketing tools and Human Resources applications. Web Personalization For examples of implemented Business Intelligence systems, see the BI screenshot collection at The Dashboard Spy.
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