Download E-books Data Smart: Using Data Science to Transform Information into Insight PDF

By John W. Foreman

Data Science will get thrown round within the press like it really is magic. significant outlets are predicting every thing from whilst their clients are pregnant to after they desire a new pair of Chuck Taylors. it is a courageous new international the place possible meaningless information may be reworked into worthwhile perception to force shrewdpermanent enterprise decisions.

But how does one precisely do info technological know-how? must you rent the sort of monks of the darkish arts, the "data scientist," to extract this gold out of your facts? Nope.

Data technological know-how is little greater than utilizing straight-forward steps to strategy uncooked info into actionable perception. And in Data Smart, writer and information scientist John Foreman will express you ways that is performed in the normal atmosphere of a spreadsheet.

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Ninety eight four Optimization Modeling: simply because That “Fresh Squeezed” Orange Juice Ain’t Gonna mix Itself . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . one hundred and one Why may still facts Scientists comprehend Optimization? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 Contents beginning with an easy Trade-Off . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 Representing the matter as a Polytope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 fixing by means of Sliding the extent Set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . one hundred and five The Simplex process: Rooting round the Corners . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106 operating in Excel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108 There’s a Monster on the finish of This bankruptcy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 clean from the Grove in your Glass... with a Pit cease via a mixing version . . . . . . . . . 118 you employ a mixing version . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 Let’s commence with a few specifications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 Coming again to Consistency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 placing the knowledge into Excel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 constructing the matter in Solver . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124 decreasing Your criteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126 useless Squirrel removing: The Minimax formula . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131 If-Then and the “Big M” Constraint. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 Multiplying Variables: Cranking Up the amount to eleven . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 Modeling threat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . one hundred forty four typically allotted information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . a hundred forty five Wrapping Up . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154 five Cluster research half II: community Graphs and group Detection . . . . . . . . . . . a hundred and fifty five what's a community Graph? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156 Visualizing an easy Graph . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157 short creation to Gephi. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159 Gephi deploy and dossier training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . one hundred sixty Laying Out the Graph . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162 Node measure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . a hundred sixty five beautiful Printing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166 Touching the Graph facts. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168 construction a Graph from the Wholesale Wine information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . a hundred and seventy making a Cosine Similarity Matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172 generating an r-Neighborhood Graph . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174 How a lot Is an facet worthy? issues and consequences in Graph Modularity .

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