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