Jordan Goldmeier

Data Smart (eBook, PDF)

Using Data Science to Transform Information into Insight

• Format: PDF

Produktbeschreibung

A straightforward and engaging approach to data science that skips the jargon and focuses on the essentials In the newly revised second edition of Data Smart: Using Data Science to Transform Information into Insight, accomplished data scientist and speaker Jordan Goldmeier delivers an approachable and conversational approach to data science using Microsoft Excel's easily understood features. The author also walks readers through the fundamentals of statistics, machine learning and powerful artificial intelligence concepts, focusing on how to learn by doing. You'll also find: * Four-color data visualizations that highlight and illustrate the concepts discussed in the book * Tutorials explaining complicated data science using just Microsoft Excel * How to take what you've learned and apply it to everyday problems at work and life A must-read guide to data science for every day, non-technical professionals, Data Smart will earn a place on the bookshelves of students, analysts, data-driven managers, marketers, consultants, business intelligence analysts, demand forecasters, and revenue managers.

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Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 448
• Erscheinungstermin: 20. September 2023
• Englisch
• ISBN-13: 9781119931485
• Artikelnr.: 69072027

Autorenporträt

JORDAN GOLDMEIER is an award-winning author in analytics, data science, and data visualization, and 11-time Microsoft MVP winner. Jordan has served analytics solutions for global organizations like NATO, The World Bank and Habitat for Humanity, and Fortune 500 companies likes Principal Financial and H&M. He has taught as an instructor for Wake Forest University, and served as a volunteer Emergency Medical Technician in New York City.

Inhaltsangabe

Introduction xix

1 Everything You Ever Needed to Know About Spreadsheets but Were Too Afraid to Ask 1

Some Sample Data 2

Accessing Quick Descriptive Statistics 3

Excel Tables 4

Filtering and Sorting 5

Table Formatting 7

Structured References 7

Adding Table Columns 10

Lookup Formulas 11

VLOOKUP 11

INDEX/MATCH 13

XLOOKUP 15

PivotTables 16

Using Array Formulas 19

Solving Stuff with Solver 20

2 Set It and Forget It: An Introduction to Power Query 27

What Is Power Query? 27

Sample Data 28

Starting Power Query 29

Filtering Rows 32

Removing Columns 33

Find & Replace 34

Close & Load to Table 35

3 Naïve Bayes and the Incredible Lightness of Being an Idiot 39

The World's Fastest Intro to Probability Theory 39

Totaling Conditional Probabilities 40

Joint Probability, the Chain Rule, and Independence 40

What Happens in a Dependent Situation? 41

Bayes Rule 42

Separating the Signal and the Noise 43

Using the Bayes Rule to Create an AI Model 44

High-Level Class Probabilities Are Often Assumed to Be Equal 45

A Couple More Odds and Ends 46

Let's Get This Excel Party Started 47

Cleaning the Data with Power Query 48

Splitting on Spaces: Giving Each Word Its Due 50

Counting Tokens and Calculating Probabilities 55

We Have a Model! Let's Use It 58

4 Cluster Analysis Part 1: Using K-Means to Segment Your Customer Base 65

Dances at Summer Camp 65

Getting Real: K-Means Clustering Subscribers in Email Marketing 70

The Initial Dataset 71

Determining What to Measure 72

Start with Four Clusters 75

Euclidean Distance: Measuring Distances as the Crow Flies 76

Solving for the Cluster Centers 80

Making Sense of the Results 82

Getting the Top Deals by Cluster 83

The Silhouette: A Good Way to Let Different K Values Duke It Out 86

How About Five Clusters? 95

Solving for Five Clusters 96

Getting the Top Deals for All Five Clusters 96

Computing the Silhouette for 5-Means Clustering 99

K-Medians Clustering and Asymmetric Distance Measurements 100

Using K-Medians Clustering 100

Getting a More Appropriate Distance Metric 100

Putting It All in Excel 102

The Top Deals for the 5-Medians Clusters 104

5 Cluster Analysis Part II: Network Graphs and Community Detection 109

What Is a Network Graph? 110

Visualizing a Simple Graph 110

Beyond GiGraph and Adjacency Lists 115

Building a Graph from the Wholesale Wine Data 117

Creating a Cosine Similarity Matrix 118

Producing an R-Neighborhood Graph 121

Introduction to Gephi 123

Creating a Static Adjacency Matrix 124

Bringing in Your R-Neighborhood Adjacency Matrix into Gephi 124

Node Degree 128

Touching the Graph Data 130

How Much Is an Edge Worth? Points and Penalties in Graph Modularity 132

What's a Point, and What's a Penalty? 133

Setting Up the Score Sheet 136

Let's Get Clustering! 138

Split Number 1 138

Split 2: Electric Boogaloo 143

And. . .Split3: Split with a Vengeance 145

Encoding and Analyzing the Communities 146

There and Back Again: A Gephi Tale 151

6 Regression: The Granddaddy of Supervised Artifici