001438379 000__ 04951cam\a2200565\i\4500
001438379 001__ 1438379
001438379 003__ OCoLC
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001438379 019__ $$a1260819162$$a1266810103
001438379 020__ $$a9781484271735$$q(electronic bk.)
001438379 020__ $$a1484271734$$q(electronic bk.)
001438379 020__ $$z9781484271728
001438379 020__ $$z1484271726
001438379 0247_ $$a10.1007/978-1-4842-7173-5$$2doi
001438379 035__ $$aSP(OCoLC)1261364192
001438379 040__ $$aEBLCP$$beng$$erda$$epn$$cEBLCP$$dYDX$$dGW5XE$$dOCLCO$$dOCLCF$$dDCT$$dN$T$$dEBLCP$$dUKAHL$$dOCLCQ$$dOCLCO$$dUPM$$dOCLCQ
001438379 049__ $$aISEA
001438379 050_4 $$aQA76.9.B45$$bS37 2021
001438379 08204 $$a005.7$$223
001438379 1001_ $$aSarka, Dejan,$$eauthor.
001438379 24510 $$aAdvanced analytics with Transact-SQL :$$bexploring hidden patterns and rules in your data /$$cDejan Sarka.
001438379 264_1 $$a[Berkeley, CA] :$$bApress,$$c[2021]
001438379 264_4 $$c©2021
001438379 300__ $$a1 online resource :$$billustrations
001438379 336__ $$atext$$btxt$$2rdacontent
001438379 337__ $$acomputer$$bc$$2rdamedia
001438379 338__ $$aonline resource$$bcr$$2rdacarrier
001438379 347__ $$atext file
001438379 347__ $$bPDF
001438379 500__ $$aIncludes index.
001438379 5050_ $$aPart I. Statistics -- 1. Descriptive Statistics.-2. Associations Between Pairs of Variables -- Part II. Data Preparation and Quality -- 3. Data Preparation -- 4. Data Quality and Information -- Part III. Dealing with Time -- 5. Time-Oriented Data -- 6. Time-Oriented Analyses -- Part IV. Data Science -- 7. Data Mining -- 8. Text Mining.
001438379 506__ $$aAccess limited to authorized users.
001438379 520__ $$aLearn about business intelligence (BI) features in T-SQL and how they can help you with data science and analytics efforts without the need to bring in other languages such as R and Python. This book shows you how to compute statistical measures using your existing skills in T-SQL. You will learn how to calculate descriptive statistics, including centers, spreads, skewness, and kurtosis of distributions. You will also learn to find associations between pairs of variables, including calculating linear regression formulas and confidence levels with definite integration. No analysis is good without data quality. Advanced Analytics with Transact-SQL introduces data quality issues and shows you how to check for completeness and accuracy, and measure improvements in data quality over time. The book also explains how to optimize queries involving temporal data, such as when you search for overlapping intervals. More advanced time-oriented information in the book includes hazard and survival analysis. Forecasting with exponential moving averages and autoregression is covered as well. Every web/retail shop wants to know the products customers tend to buy together. Trying to predict the target discrete or continuous variable with few input variables is important for practically every type of business. This book helps you understand data science and the advanced algorithms use to analyze data, and terms such as data mining, machine learning, and text mining. Key to many of the solutions in this book are T-SQL window functions. Author Dejan Sarka demonstrates efficient statistical queries that are based on window functions and optimized through algorithms built using mathematical knowledge and creativity. The formulas and usage of those statistical procedures are explained so you can understand and modify the techniques presented. T-SQL is supported in SQL Server, Azure SQL Database, and in Azure Synapse Analytics. There are so many BI features in T-SQL that it might become your primary analytic database language. If you want to learn how to get information from your data with the T-SQL language that you already are familiar with, then this is the book for you. You will learn to: Describe distribution of variables with statistical measures Find associations between pairs of variables Evaluate the quality of the data you are analyzing Perform time-series analysis on your data Forecast values of a continuous variable Perform market-basket analysis to predict customer purchasing patterns Predict target variable outcomes from one or more input variables Categorize passages of text by extracting and analyzing keywords.
001438379 588__ $$aDescription based on print version record.
001438379 650_0 $$aBig data.
001438379 650_0 $$aBusiness intelligence$$xData processing.
001438379 650_0 $$aSQL (Computer program language)
001438379 650_6 $$aDonnées volumineuses.
001438379 650_6 $$aSQL (Langage de programmation)
001438379 655_0 $$aElectronic books.
001438379 77608 $$iPrint version:$$aSARKA, DEJAN.$$tADVANCED ANALYTICS WITH TRANSACT-SQL.$$d[S.l.] : APRESS, 2021$$z1484271726$$w(OCoLC)1252962084
001438379 852__ $$bebk
001438379 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-1-4842-7173-5$$zOnline Access$$91397441.1
001438379 909CO $$ooai:library.usi.edu:1438379$$pGLOBAL_SET
001438379 980__ $$aBIB
001438379 980__ $$aEBOOK
001438379 982__ $$aEbook
001438379 983__ $$aOnline
001438379 994__ $$a92$$bISE