Dive Into Data Science: Use Python To Tackle Your Toughest Business Challenges

Title Page....9

Copyright....10

Dedication....11

About the Author....12

Acknowledgments....13

Introduction....14

Who Is This Book For?....16

About This Book....17

Setting Up the Environment....18

Windows....18

macOS....19

Linux....19

Installing Packages with Python....20

Other Tools....22

Summary....23

Chapter 1: Exploratory Data Analysis....24

Your First Day as CEO....25

Finding Patterns in Datasets....25

Using .csv Files to Review and Store Data....28

Displaying Data with Python....29

Calculating Summary Statistics....33

Analyzing Subsets of Data....36

Nighttime Data....36

Seasonal Data....38

Visualizing Data with Matplotlib....40

Drawing and Displaying a Simple Plot....40

Clarifying Plots with Titles and Labels....41

Plotting Subsets of Data....42

Testing Different Plot Types....44

Exploring Correlations....52

Calculating Correlations....52

Understanding Strong vs. Weak Correlations....53

Finding Correlations Between Variables....58

Creating Heat Maps....59

Exploring Further....63

Summary....63

Chapter 2: Forecasting....65

Predicting Customer Demand....65

Cleaning Erroneous Data....66

Plotting Data to Find Trends....69

Performing Linear Regression....70

Applying Algebra to the Regression Line....73

Calculating Error Measurements....76

Using Regression to Forecast Future Trends....81

Trying More Regression Models....83

Multivariate Linear Regression to Predict Sales....84

Trigonometry to Capture Variations....87

Choosing the Best Regression to Use for Forecasting....91

Exploring Further....96

Summary....97

Chapter 3: Group Comparisons....99

Reading Population Data....99

Summary Statistics....100

Random Samples....102

Differences Between Sample Data....105

Performing Hypothesis Testing....109

The t-Test....111

Nuances of Hypothesis Testing....113

Comparing Groups in a Practical Context....115

Summary....120

Chapter 4: A/B Testing....121

The Need for Experimentation....121

Running Experiments to Test New Hypotheses....123

Understanding the Math of A/B Testing....128

Translating the Math into Practice....129

Optimizing with the Champion/Challenger Framework....132

Preventing Mistakes with Twyman’s Law and A/A Testing....134

Understanding Effect Sizes....136

Calculating the Significance of Data....138

Applications and Advanced Considerations....141

The Ethics of A/B Testing....143

Summary....146

Chapter 5: Binary Classification....147

Minimizing Customer Attrition....147

Using Linear Probability Models to Find High-Risk Customers....149

Plotting Attrition Risk....151

Confirming Relationships with Linear Regression....152

Predicting the Future....156

Making Business Recommendations....158

Measuring Prediction Accuracy....159

Using Multivariate LPMs....162

Creating New Metrics....164

Considering the Weaknesses of LPMs....167

Predicting Binary Outcomes with Logistic Regression....168

Drawing Logistic Curves....168

Fitting the Logistic Function to Our Data....171

Applications of Binary Classification....173

Summary....174

Chapter 6: Supervised Learning....175

Predicting Website Traffic....176

Reading and Plotting News Article Data....177

Using Linear Regression as a Prediction Method....180

Understanding Supervised Learning....182

k-Nearest Neighbors....184

Implementing k-NN....186

Performing k-NN with Python’s sklearn....188

Using Other Supervised Learning Algorithms....190

Decision Trees....192

Random Forests....194

Neural Networks....195

Measuring Prediction Accuracy....198

Working with Multivariate Models....201

Using Classification Instead of Regression....202

Summary....205

Chapter 7: Unsupervised Learning....206

Unsupervised Learning vs. Supervised Learning....206

Generating and Exploring Data....208

Rolling the Dice....208

Using Another Kind of Die....213

The Origin of Observations with Clustering....215

Clustering in Business Applications....220

Analyzing Multiple Dimensions....222

E-M Clustering....224

The Guessing Step....227

The Expectation Step....229

The Maximization Step....231

The Convergence Step....234

Other Clustering Methods....237

Other Unsupervised Learning Methods....240

Summary....242

Chapter 8: Web Scraping....243

Understanding How Websites Work....243

Creating Your First Web Scraper....245

Parsing HTML Code....248

Scraping an Email Address....248

Searching for Addresses Directly....250

Performing Searches with Regular Expressions....251

Using Metacharacters for Flexible Searches....253

Fine-Tuning Searches with Escape Sequences....254

Combining Metacharacters for Advanced Searches....257

Using Regular Expressions to Search for Email Addresses....259

Converting Results to Usable Data....260

Using Beautiful Soup....262

Parsing HTML Label Elements....264

Scraping and Parsing HTML Tables....265

Advanced Scraping....268

Summary....269

Chapter 9: Recommendation Systems....271

Popularity-Based Recommendations....272

Item-Based Collaborative Filtering....275

Measuring Vector Similarity....277

Calculating Cosine Similarity....279

Implementing Item-Based Collaborative Filtering....281

User-Based Collaborative Filtering....284

Case Study: Music Recommendations....288

Generating Recommendations with Advanced Systems....290

Summary....292

Chapter 10: Natural Language Processing....293

Using NLP to Detect Plagiarism....293

Understanding the word2vec NLP Model....295

Quantifying Similarities Between Words....295

Creating a System of Equations....298

Analyzing Numeric Vectors in word2vec....304

Manipulating Vectors with Mathematical Calculations....308

Detecting Plagiarism with word2vec....309

Using Skip-Thoughts....311

Topic Modeling....314

Other Applications of NLP....317

Summary....318

Chapter 11: Data Science in Other Languages....320

Winning Soccer Games with SQL....321

Reading and Analyzing Data....321

Getting Familiar with SQL....323

Setting Up a SQL Database....324

Running SQL Queries....325

Combining Data by Joining Tables....329

Winning Soccer Games with R....333

Getting Familiar with R....333

Applying Linear Regression in R....335

Using R to Plot Data....337

Gaining Other Valuable Skills....339

Summary....342

Index....343

Dive into the exciting world of data science with this practical introduction. Packed with essential skills and useful examples, Dive Into Data Science will show you how to obtain, analyze, and visualize data so you can leverage its power to solve common business challenges.With only a basic understanding of Python and high school math, you’ll be able to effortlessly work through the book and start implementing data science in your day-to-day work. From improving a bike sharing company to extracting data from websites and creating recommendation systems, you’ll discover how to find and use data-driven solutions to make business decisions.Topics covered include conducting exploratory data analysis, running A/B tests, performing binary classification using logistic regression models, and using machine learning algorithms.

You’ll also learn how to:

• Forecast consumer demand
• Optimize marketing campaigns
• Reduce customer attrition
• Predict website traffic
• Build recommendation systems

With this practical guide at your fingertips, harness the power of programming, mathematical theory, and good old common sense to find data-driven solutions that make a difference. Don’t wait; dive right in!