LEADER 06130nam 2200673 a 450 001 9910785726403321 005 20200520144314.0 010 $a0-12-385890-9 035 $a(CKB)2670000000259157 035 $a(EBL)1034439 035 $a(OCoLC)815281390 035 $a(SSID)ssj0000720130 035 $a(PQKBManifestationID)12329729 035 $a(PQKBTitleCode)TC0000720130 035 $a(PQKBWorkID)10669073 035 $a(PQKB)10030141 035 $a(Au-PeEL)EBL1034439 035 $a(CaPaEBR)ebr10607275 035 $a(CaONFJC)MIL107824 035 $a(CaSebORM)9780123858894 035 $a(MiAaPQ)EBC1034439 035 $a(EXLCZ)992670000000259157 100 $a20120813d2012 uy 0 101 0 $aeng 135 $aurunu||||| 181 $ctxt 182 $cc 183 $acr 200 10$aBusiness intelligence$b[electronic resource] $ethe savvy manager's guide /$fDavid Loshin 205 $a2nd ed. 210 $aWaltham, Mass. $cMorgan Kaufmann$dc2012 215 $a1 online resource (401 p.) 225 1 $aThe Savvy Manager's Guides 300 $aDescription based upon print version of record. 311 $a0-12-385889-5 320 $aIncludes bibliographical references and index. 327 $aFront Cover; Business Intelligence; Copyright; Contents; Preface; Introduction; What This Book Is; Why You Should Be Reading This Book; Organization of the Book; Our Approach to Knowledge Transfer; Contact Me; Acknowledgements; Foreword; Chapter 1- Business Intelligence and Information Exploitation; Improving the Decision-Making Process; Why a Business Intelligence Program?; Taking Advantage of the Information Asset; Business Intelligence and Program Success; Business Intelligence Defined; Actionable Intelligence; The Analytics Spectrum; Taming the Information Explosion; Considerations 327 $aContinuing Your Business Intelligence Education End notes; Chapter 2 -The Value of Business Intelligence; Value Drivers and Information Use; Performance Metrics and Key Performance Indicators; Using Actionable Knowledge; Horizontal Use Cases for Business Intelligence; Vertical Use Cases for Business Intelligence; Business Intelligence Adds Value; Chapter 3 -Planning for Success; Introduction; Organizational Preparedness for Business Intelligence and Analytics; Initial Steps in Starting a Business Intelligence Program; Bridging the Gaps Between Information Technology and the Business Users 327 $aKnowing the Different Types of Business Intelligence Users Business Intelligence Success Factors: A Deeper Dive; More on Building Your Team; Strategic Versus Tactical Planning; Summary; End notes; Chapter 4 -Developing Your Business Intelligence Roadmap; A Business Intelligence Strategy: Vision to Blueprint; Review: The Business Intelligence and Analytics Spectrum; The Business Intelligence Roadmap: Example Phasing; Planning the Business Intelligence Plan; Chapter 5 -The Business Intelligence Environment; Aspects of a Business Intelligence and Analytics Platform and Strategy 327 $aThe Organizational Business Intelligence Framework Services and System Evolution; Management Issues; Additional Considerations; Chapter 6 -Business Processes and Information Flow; Analytical Information Needs and Information Flows; Information Processing and Information Flow; The Information Flow Model; Practical Use; Modeling Frameworks; Management Issues; Deeper Dives; Chapter 7 -Data Requirements Analysis; Introduction; Business Uses of Information; Metrics: Facts, Qualifiers, and Models; What is Data Requirements Analysis?; Assessing Suitability; Summary 327 $aChapter 8 -Data Warehouses and the Technical Business Intelligence Architecture Introduction; Data Modeling and Analytics; The Data Warehouse; Analytical Platforms; Operational Data Stores; Management; Do You Really Need a Data Warehouse?; Summary; Chapter 9 -Metadata; What is Metadata?; The Origin and Utility of Metadata; Types of Metadata; Semantic Metadata Processes for Business Analytics; Further Considerations; Using Metadata Tools; Establishing Usability of Candidate Data Sources; Data Profiling Activities; Data Model Inference; Attribute Analysis; Relationship Analysis 327 $aManagement Issues 330 $aFollowing the footsteps of the first edition, the second edition of Business Intelligence is a full overview of what comprises business intelligence. It is intended to provide an introduction to the concepts to uncomplicate the learning process when implementing a business intelligence program. Over a relatively long lifetime (7 years), the current edition of book has received numerous accolades from across the industry for its straightforward introduction to both business and technical aspects of business intelligence. As an author, David Loshin has a distinct ability to translate challenging topics into a framework that is easily digestible by managers, business analysts, and technologists alike. In addition, his material has developed a following (such as the recent Master Data Management book) among practitioners and key figures in the industry (both analysts and vendors) and that magnifies our ability to convey the value of this book. Guides managers through developing, administering, or simply understanding business intelligence technology. Keeps pace with the changes in best practices, tools, methods and processes used to transform an organization's data into actionable knowledge. Contains a handy, quick-reference to technologies and terminology. 410 4$aThe Savvy Manager's Guides 606 $aBusiness intelligence 606 $aInformation technology$xManagement 606 $aManagement information systems 615 0$aBusiness intelligence. 615 0$aInformation technology$xManagement. 615 0$aManagement information systems. 676 $a658.4/72 700 $aLoshin$b David$f1963-$0627545 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910785726403321 996 $aBusiness intelligence$91213064 997 $aUNINA LEADER 09752nam 22006973 450 001 9911006690203321 005 20221210060240.0 010 $a9781683928249 010 $a1683928245 010 $a9781683928256 010 $a1683928253 024 7 $a10.1515/9781683928256 035 $a(MiAaPQ)EBC30286674 035 $a(Au-PeEL)EBL30286674 035 $a(CKB)25609444600041 035 $a(DE-B1597)654033 035 $a(DE-B1597)9781683928256 035 $a(BIP)086603675 035 $a(FR-PaCSA)88949058 035 $a(FRCYB88949058)88949058 035 $a(Perlego)4268391 035 $a(OCoLC)1394872424 035 $a(EXLCZ)9925609444600041 100 $a20221210d2022 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aPandas Basics 205 $a1st ed. 210 1$aBloomfield :$cMercury Learning & Information,$d2022. 210 4$dİ2022. 215 $a1 online resource (215 pages) 311 08$a9781683928263 311 08$a1683928261 327 $aCover -- Title Page -- Copyright -- Dedication -- Contents -- Preface -- Chapter 1: Introduction to Python -- Tools for Python -- easy_install and pip -- virtualenv -- IPython -- Python Installation -- Setting the PATH Environment Variable (Windows Only) -- Launching Python on Your Machine -- The Python Interactive Interpreter -- Python Identifiers -- Lines, Indentation, and Multi-lines -- Quotations and Comments -- Saving Your Code in a Module -- Some Standard Modules -- The help() and dir() Functions -- Compile Time and Runtime Code Checking -- Simple Data Types -- Working with Numbers -- Working with Other Bases -- The chr() Function -- The round() Function -- Formatting Numbers -- Working with Fractions -- Unicode and UTF-8 -- Working with Unicode -- Working with Strings -- Comparing Strings -- Formatting Strings -- Uninitialized Variables and the Value None -- Slicing and Splicing Strings -- Testing for Digits and Alphabetic Characters -- Search and Replace a String in Other Strings -- Remove Leading and Trailing Characters -- Printing Text without NewLine Characters -- Text Alignment -- Working with Dates -- Converting Strings to Dates -- Exception Handling -- Handling User Input -- Command-line Arguments -- Summary -- Chapter 2: Working with Data -- Dealing with Data: What Can Go Wrong? -- What is Data Drift? -- What are Datasets? -- Data Preprocessing -- Data Types -- Preparing Datasets -- Discrete Data Versus Continuous Data -- Binning Continuous Data -- Scaling Numeric Data via Normalization -- Scaling Numeric Data via Standardization -- Scaling Numeric Data via Robust Standardization -- What to Look for in Categorical Data -- Mapping Categorical Data to Numeric Values -- Working with Dates -- Working with Currency -- Working with Outliers and Anomalies -- Outlier Detection/Removal -- Finding Outliers with NumPy. 327 $aFinding Outliers with Pandas -- Calculating Z-scores to Find Outliers -- Finding Outliers with SkLearn (Optional) -- Working with Missing Data -- Imputing Values: When is Zero a Valid Value? -- Dealing with Imbalanced Datasets -- What is SMOTE? -- SMOTE extensions -- The Bias-Variance Tradeoff -- Types of Bias in Data -- Analyzing Classifiers (Optional) -- What is LIME? -- What is ANOVA? -- Summary -- Chapter 3: Introduction to Probability and Statistics -- What is a Probability? -- Calculating the Expected Value -- Random Variables -- Discrete versus Continuous Random Variables -- Well-known Probability Distributions -- Fundamental Concepts in Statistics -- The Mean -- The Median -- The Mode -- The Variance and Standard Deviation -- Population, Sample, and Population Variance -- Chebyshev's Inequality -- What is a p-value? -- The Moments of a Function (Optional) -- What is Skewness? -- What is Kurtosis? -- Data and Statistics -- The Central Limit Theorem -- Correlation versus Causation -- Statistical Inferences -- Statistical Terms: RSS, TSS, R^2, and F1 Score -- What is an F1 score? -- Gini Impurity, Entropy, and Perplexity -- What is the Gini Impurity? -- What is Entropy? -- Calculating the Gini Impurity and Entropy Values -- Multi-dimensional Gini Index -- What is Perplexity? -- Cross-Entropy and KL Divergence -- What is Cross-Entropy? -- What is KL Divergence? -- What's Their Purpose? -- Covariance and Correlation Matrices -- The Covariance Matrix -- Covariance Matrix: An Example -- The Correlation Matrix -- Eigenvalues and Eigenvectors -- Calculating Eigenvectors: A Simple Example -- Gauss Jordan Elimination (Optional) -- PCA (Principal Component Analysis) -- The New Matrix of Eigenvectors -- Well-known Distance Metrics -- Pearson Correlation Coefficient -- Jaccard Index (or Similarity) -- Local Sensitivity Hashing (Optional). 327 $aTypes of Distance Metrics -- What is Bayesian Inference? -- Bayes' Theorem -- Some Bayesian Terminology -- What is MAP? -- Why Use Bayes' Theorem? -- Summary -- Chapter 4: Introduction to Pandas (1) -- What is Pandas? -- Pandas Options and Settings -- Pandas Data Frames -- Data Frames and Data Cleaning Tasks -- Alternatives to Pandas -- A Pandas Data Frame with a NumPy Example -- Describing a Pandas Data Frame -- Pandas Boolean Data Frames -- Transposing a Pandas Data Frame -- Pandas Data Frames and Random Numbers -- Reading CSV Files in Pandas -- Specifying a Separator and Column Sets in Text Files -- Specifying an Index in Text Files -- The loc() and iloc() Methods in Pandas -- Converting Categorical Data to Numeric Data -- Matching and Splitting Strings in Pandas -- Converting Strings to Dates in Pandas -- Working with Date Ranges in Pandas -- Detecting Missing Dates in Pandas -- Interpolating Missing Dates in Pandas -- Other Operations with Dates in Pandas -- Merging and Splitting Columns in Pandas -- Reading HTML Web Pages in Pandas -- Saving a Pandas Data Frame as an HTML Web Page -- Summary -- Chapter 5: Introduction to Pandas (2) -- Combining Pandas Data Frames -- Data Manipulation with Pandas Data Frames (1) -- Data Manipulation with Pandas Data Frames (2) -- Data Manipulation with Pandas Data Frames (3) -- Pandas Data Frames and CSV Files -- Managing Columns in Data Frames -- Switching Columns -- Appending Columns -- Deleting Columns -- Inserting Columns -- Scaling Numeric Columns -- Managing Rows in Pandas -- Selecting a Range of Rows in Pandas -- Finding Duplicate Rows in Pandas -- Inserting New Rows in Pandas -- Handling Missing Data in Pandas -- Multiple Types of Missing Values -- Test for Numeric Values in a Column -- Replacing NaN Values in Pandas -- Summary -- Chapter 6: Introduction to Pandas (3) -- Threshold Values and Outliers. 327 $aThe Pandas Pipe Method -- Pandas query() Method for Filtering Data -- Sorting Data Frames in Pandas -- Working with groupby() in Pandas -- Working with apply() and mapapply() in Pandas -- Handling Outliers in Pandas -- Pandas Data Frames and Scatterplots -- Pandas Data Frames and Simple Statistics -- Aggregate Operations in Pandas Data Frames -- Aggregate Operations with the titanic.csv Dataset -- Save Data Frames as CSV Files and Zip Files -- Pandas Data Frames and Excel Spreadsheets -- Working with JSON-based Data -- Python Dictionary and JSON -- Python, Pandas, and JSON -- Window Functions in Pandas -- Useful One-line Commands in Pandas -- What is pandasql? -- What is Method Chaining? -- Pandas and Method Chaining -- Pandas Profiling -- Alternatives to Pandas -- Summary -- Chapter 7: Data Visualization -- What is Data Visualization? -- Types of Data Visualization -- What is Matplotlib? -- Lines in a Grid in Matplotlib -- A Colored Grid in Matplotlib -- Randomized Data Points in Matplotlib -- A Histogram in Matplotlib -- A Set of Line Segments in Matplotlib -- Plotting Multiple Lines in Matplotlib -- Trigonometric Functions in Matplotlib -- Display IQ Scores in Matplotlib -- Plot a Best-Fitting Line in Matplotlib -- The Iris Dataset in Sklearn -- Sklearn, Pandas, and the Iris Dataset -- Working with Seaborn -- Features of Seaborn -- Seaborn Built-in Datasets -- The Iris Dataset in Seaborn -- The Titanic Dataset in Seaborn -- Extracting Data from the Titanic Dataset in Seaborn (1) -- Extracting Data from the Titanic Dataset in Seaborn (2) -- Visualizing a Pandas Dataset in Seaborn -- Data Visualization in Pandas -- What is Bokeh? -- Summary -- Index. 330 $aThis book is intended for those who plan to become data scientists as well as anyone who needs to perform data cleaning tasks using Pandas and NumPy. It contains a variety of code samples and features of NumPy and Pandas, and how to write regular expressions. Chapter 3 includes fundamental statistical concepts and Chapter 7 covers data visualization with Matplotlib and Seaborn. Companion files with code are available for downloading from the publisher. FEATURES:Provides the reader with numerous code samples for Pandas and NumPy programming concepts, and an introduction to statistical concepts and data visualizationIncludes an introductory chapter on PythonCompanion files with code 606 $aCOMPUTERS / Programming Languages / Python$2bisacsh 610 $aComputer Science. 610 $aData Science. 610 $aDevelopers. 610 $aMatplotlib. 610 $aNumPy. 610 $aProgramming. 610 $aPython. 610 $aSeaborn. 610 $adata mining. 615 7$aCOMPUTERS / Programming Languages / Python. 676 $a005.133 700 $aCampesato$b Oswald$01594522 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911006690203321 996 $aPandas Basics$94389395 997 $aUNINA