Data mining for business intelligence / Shmueli Galit

No review available on this record. Title : Data mining for business intelligence : concepts, techniques, and applications in Microsoft Office Excel with XLMiner Material Type: printed text Authors: Shmueli Galit, Author ; Nitin R. Patel, Author ; Peter C. Bruce, Author Publisher: New York : John Wiley & Sons, Inc Publication Date: 2007 Pagination: xviii, 279 p. Layout: ill. Size: 26 cm. ISBN (or other code): 978-0-470-08485-4 General note: Includes bibliographical references (p. 271-272) Includes index (p. 273-279) Languages : English (eng) Original Language : English (eng) Descriptors: Business - Data processing Data mining Microsoft Excel (Computer file) Class number: 005.54 Abstract: Data mining—the art of extracting useful information from large amounts of data—is of growing importance in today's world. Your e-mail spam filter relies at least in part on rules that a data mining algorithm has learned from examining millions of e-mail messages that have been classified as spam or not-spam. Real-time data mining methods enable Web-based merchants to tell you that "customers who purchased x are also likely to purchase y." Data mining helps banks determine which applicants are likely to default on loans, helps tax authorities identify which tax returns are most likely to be fraudulent, and helps catalog merchants target those customers most likely to purchase. And data mining is not just about numbers—text mining techniques help search engines like Google and Yahoo find what you are looking for by ordering documents according to their relevance to your query. In the process they have effectively monetized search by ordering sponsored ads that are relevant to your query. The amount of data flowing from, to, and through enterprises of all sorts is enormous, and growing rapidly—more rapidly than the capabilities of organizations to use it. Successful enterprises are those that make effective use of the abundance of data to which they have access: to make better predictions, better decisions, and better strategies. The margin over a competitor may be small (they, after all, have access to the same methods for making effective use of information), hence the need to take advantage of every possible avenue to advantage. At no time has the need been greater for quantitatively skilled managerial expertise. Successful managers now need to know about the possibilities and limitations of data mining. But at what level? A high-level overview can provide a general idea of what data mining can do for the enterprise but fails to provide the intuition that could be attained by actually building models with real data. Contents note: Overview of the Data Mining Process; Data Exploration and Dimension Reduction; Evaluating Classification and Predictive Performance; Multiple Linear Regression; Classification and Regression Trees; Logistic Regression; Neural Nets; Discriminant Analysis; Association Rules; Cluster analysis; Record link: https://library.seeu.edu.mk/index.php?lvl=notice_display&id=14315

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Barcode	Call number	Media type	Location	Section	Status
1702-000843	005.54 Gal-Dat 2007	General Collection	Library "Max van der Stoel"	English	Available

Microsoft Excel 2010 / Wayne L. Winston

No review available on this record. Title : Microsoft Excel 2010 : data analysis and business modeling Material Type: printed text Authors: Wayne L. Winston, Author Publisher: Redmond, Washington : Microsoft Press Publication Date: 2011 Pagination: xv, 700 p. Layout: ill. Size: 23 cm ISBN (or other code): 978-0-7356-4336-9 General note: Includes index Languages : English (eng) Original Language : English (eng) Descriptors: Decision making - Computer programs Industrial management - Statistical methods - Computer programs Microsoft Excel (Computer file) Class number: 005.54 Abstract: "Microsoft Excel 2010:data analysis and business modeling" - Master the business modeling and analysis techniques that help you transform data into bottom-line results. For more than a decade, Wayne Winston has been teaching corporate clients and MBA students the most effective ways to use Excel to solve business problems and make better decisions. Now this award-winning educator shares the best of his expertise in this hands-on, scenario-focused guide—fully updated for Excel 2010! Contents note: What’s new in Excel 2010; Range names; Lookup functions; The INDEX function; The MATCH function; Text functions; Dates and date functions; Evaluating investments by using net present value criteria; Internal rate of return; More Excel financial functions; Circular references; IF statements; Time and time functions; The paste special command; Three-dimensional formulas; The auditing tool; Sensitivity analysis with data tables; The goal seek command; Using the scenario manager for sensitivity analysis; The COUNTIF, COUNTIFS, COUNT, COUNTA, and COUNTBLANK Functions; The SUMIF, AVERAGEIF, SUMIFS, and AVERAGEIFS functions; The OFFSET function; The INDIRECT function; Conditional formatting; Sorting in Excel; Tables; Spin buttons, scroll bars, option buttons, check boxes, combo boxes, and group list boxes; An introduction to optimization with Excel Solver; Using Solver to determine the optimal product mix; Using Solver to schedule your workforce; Using Solver to solve transportation or distribution problems; Using Solver for capital budgeting; Using Solver for financial planning; Using Solver to rate sports teams; Warehouse location and the GRG multistart and Evolutionary Solver engines; Penalties and the Evolutionary Solver; The traveling salesperson problem; Importing data from a text file or document; Importing data from the internet; Validating data; Summarizing data by using histograms; Summarizing data by using descriptive statistics; Using PivotTables and slicers to describe data; Sparklines; Summarizing data with database statistical functions; Filtering data and removing duplicates; Consolidating data; Creating subtotals; Estimating straight line relationships; Modeling exponential growth; The power curve; Using correlations to summarize relationships; Introduction to multiple regression; Incorporating qualitative factors into multiple regression; Modeling nonlinearities and interactions; Analysis of variance: one-way ANOVA; Randomized blocks and two-way ANOVA; Using moving averages to understand time series; Winters’s method; Ratio-to-moving-average forecast method; Forecasting in the presence of special events; An introduction to random variables; The binomial, hypergeometric, and negative binomial random variables; The Poisson and exponential random variable; The normal random variable; Weibull and beta distributions: modeling machine life and duration of a project; Making probability statements from forecasts; Using the lognormal random variable to model stock prices; Introduction to Monte Carlo simulation; Calculating an optimal bid; Simulating stock prices and asset allocation modeling; Fun and games: simulating gambling and sporting event probabilities; Using resampling to analyze data; Pricing stock options; Determining customer value; The economic order quantity inventory model; Inventory modeling with uncertain demand; Queuing theory: the mathematics of waiting in line; Estimating a demand curve; Pricing products by using tie-ins; Pricing products by using subjectively determined demand; Nonlinear pricing; Array formulas and functions; Record link: https://library.seeu.edu.mk/index.php?lvl=notice_display&id=15358

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Barcode	Call number	Media type	Location	Section	Status
1702-001111	005.54 Win-Mic 2011	General Collection	Library "Max van der Stoel"	English	Not for loan