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Data smart : using data science to transform information into insight / / John W. Foreman
| Data smart : using data science to transform information into insight / / John W. Foreman |
| Autore | Foreman John W. |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Indianapolis : , : Wiley, , [2014] |
| Descrizione fisica | 1 online resource (434 p.) |
| Disciplina | 006.312 |
| Soggetto topico |
Data mining
Web sites - Design Web usage mining |
| ISBN |
1-118-83986-2
1-118-66148-6 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Title Page; Copyright; Contents; Chapter 1 Everything You Ever Needed to Know about Spreadsheets but Were Too Afraid to Ask; Some Sample Data; Moving Quickly with the Control Button; Copying Formulas and Data Quickly; Formatting Cells; Paste Special Values; Inserting Charts; Locating the Find and Replace Menus; Formulas for Locating and Pulling Values; Using VLOOKUP to Merge Data; Filtering and Sorting; Using PivotTables; Using Array Formulas; Solving Stuff with Solver; OpenSolver: I Wish We Didn't Need This, but We Do; Wrapping Up
Chapter 2 Cluster Analysis Part I: Using K-Means to Segment Your Customer Base Girls Dance with Girls, Boys Scratch Their Elbows; Getting Real: K-Means Clustering Subscribers in E-mail Marketing; Joey Bag O' Donuts Wholesale Wine Emporium; The Initial Dataset; Determining What to Measure; Start with Four Clusters; Euclidean Distance: Measuring Distances as the Crow Flies; Distances and Cluster Assignments for Everybody!; Solving for the Cluster Centers; Making Sense of the Results; Getting the Top Deals by Cluster; The Silhouette: A Good Way to Let Different K Values Duke It Out How about Five Clusters? Solving for Five Clusters; Getting the Top Deals for All Five Clusters; Computing the Silhouette for 5-Means Clustering; K-Medians Clustering and Asymmetric Distance Measurements; Using K-Medians Clustering; Getting a More Appropriate Distance Metric; Putting It All in Excel; The Top Deals for the 5-Medians Clusters; Wrapping Up; Chapter 3 Naive Bayes and the Incredible Lightness of Being an Idiot; When You Name a Product Mandrill, You're Going to Get Some Signal and Some Noise; The World's Fastest Intro to Probability Theory; Totaling Conditional Probabilities Joint Probability, the Chain Rule, and Independence What Happens in a Dependent Situation?; Bayes Rule; Using Bayes Rule to Create an AI Model; High-Level Class Probabilities Are Often Assumed to Be Equal; A Couple More Odds and Ends; Let's Get This Excel Party Started; Removing Extraneous Punctuation; Splitting on Spaces; Counting Tokens and Calculating Probabilities; And We Have a Model! Let's Use It; Wrapping Up; Chapter 4 Optimization Modeling: Because That "Fresh Squeezed" Orange Juice Ain't Gonna Blend Itself; Why Should Data Scientists Know Optimization? Starting with a Simple Trade-Off Representing the Problem as a Polytope; Solving by Sliding the Level Set; The Simplex Method: Rooting around the Corners; Working in Excel; There's a Monster at the End of This Chapter; Fresh from the Grove to Your Glass...with a Pit Stop Through a Blending Model; You Use a Blending Model; Let's Start with Some Specs; Coming Back to Consistency; Putting the Data into Excel; Setting Up the Problem in Solver; Lowering Your Standards; Dead Squirrel Removal: The Minimax Formulation; If-Then and the "Big M" Constraint Multiplying Variables: Cranking Up the Volume to 11 |
| Record Nr. | UNINA-9910788939203321 |
Foreman John W.
|
||
| Indianapolis : , : Wiley, , [2014] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Data smart : using data science to transform information into insight / / John W. Foreman
| Data smart : using data science to transform information into insight / / John W. Foreman |
| Autore | Foreman John W. |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Indianapolis : , : Wiley, , [2014] |
| Descrizione fisica | 1 online resource (434 p.) |
| Disciplina | 006.312 |
| Soggetto topico |
Data mining
Web sites - Design Web usage mining |
| ISBN |
1-118-83986-2
1-118-66148-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Title Page; Copyright; Contents; Chapter 1 Everything You Ever Needed to Know about Spreadsheets but Were Too Afraid to Ask; Some Sample Data; Moving Quickly with the Control Button; Copying Formulas and Data Quickly; Formatting Cells; Paste Special Values; Inserting Charts; Locating the Find and Replace Menus; Formulas for Locating and Pulling Values; Using VLOOKUP to Merge Data; Filtering and Sorting; Using PivotTables; Using Array Formulas; Solving Stuff with Solver; OpenSolver: I Wish We Didn't Need This, but We Do; Wrapping Up
Chapter 2 Cluster Analysis Part I: Using K-Means to Segment Your Customer Base Girls Dance with Girls, Boys Scratch Their Elbows; Getting Real: K-Means Clustering Subscribers in E-mail Marketing; Joey Bag O' Donuts Wholesale Wine Emporium; The Initial Dataset; Determining What to Measure; Start with Four Clusters; Euclidean Distance: Measuring Distances as the Crow Flies; Distances and Cluster Assignments for Everybody!; Solving for the Cluster Centers; Making Sense of the Results; Getting the Top Deals by Cluster; The Silhouette: A Good Way to Let Different K Values Duke It Out How about Five Clusters? Solving for Five Clusters; Getting the Top Deals for All Five Clusters; Computing the Silhouette for 5-Means Clustering; K-Medians Clustering and Asymmetric Distance Measurements; Using K-Medians Clustering; Getting a More Appropriate Distance Metric; Putting It All in Excel; The Top Deals for the 5-Medians Clusters; Wrapping Up; Chapter 3 Naive Bayes and the Incredible Lightness of Being an Idiot; When You Name a Product Mandrill, You're Going to Get Some Signal and Some Noise; The World's Fastest Intro to Probability Theory; Totaling Conditional Probabilities Joint Probability, the Chain Rule, and Independence What Happens in a Dependent Situation?; Bayes Rule; Using Bayes Rule to Create an AI Model; High-Level Class Probabilities Are Often Assumed to Be Equal; A Couple More Odds and Ends; Let's Get This Excel Party Started; Removing Extraneous Punctuation; Splitting on Spaces; Counting Tokens and Calculating Probabilities; And We Have a Model! Let's Use It; Wrapping Up; Chapter 4 Optimization Modeling: Because That "Fresh Squeezed" Orange Juice Ain't Gonna Blend Itself; Why Should Data Scientists Know Optimization? Starting with a Simple Trade-Off Representing the Problem as a Polytope; Solving by Sliding the Level Set; The Simplex Method: Rooting around the Corners; Working in Excel; There's a Monster at the End of This Chapter; Fresh from the Grove to Your Glass...with a Pit Stop Through a Blending Model; You Use a Blending Model; Let's Start with Some Specs; Coming Back to Consistency; Putting the Data into Excel; Setting Up the Problem in Solver; Lowering Your Standards; Dead Squirrel Removal: The Minimax Formulation; If-Then and the "Big M" Constraint Multiplying Variables: Cranking Up the Volume to 11 |
| Record Nr. | UNINA-9910813595003321 |
|
Foreman John W.
|
||
| Indianapolis : , : Wiley, , [2014] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||