Dive into data science Use Python to tackle your toughest business challenges
Book - 2023
"Learn how to apply the principles of data science to improve business strategies. Chapters cover concepts such as A/B testing, supervised and unsupervised machine learning, web scraping, and more. Each concept is illustrated using real-world business applications, real-world data, and useful Python code examples"--
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- Published
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San Francisco, CA :
No Starch Press
[2023]
- Language
- English
- Main Author
- Item Description
- Includes index.
- Physical Description
- xxv, 256 pages : illustrations ; 24 cm
- ISBN
- 9781718502888
- Acknowledgments
- Introduction
- Who is This Book For?
- About This Book
- Setting Up the Environment
- Windows
- MacOS
- Linux
- Installing Packages with Python
- Other Tools
- Summary
- 1. Exploratory Data Analysis
- Your First Day as CEO
- Finding Patterns in Datasets
- Using .csv Files to Review and Store Data
- Displaying Data with Python
- Calculating Summary Statistics
- Analyzing Subsets of Data
- Nighttime Data
- Seasonal Data
- Visualizing Data with Matplotlib
- Drawing and Displaying a Simple Plot
- Clarifying Plots with Titles and Labels
- Plotting Subsets of Data
- Testing Different Plot Types
- Exploring Correlations
- Calculating Correlations
- Understanding Strong vs. Weak Correlations
- Finding Correlations Between Variables
- Creating Heat Maps
- Exploring Further
- Summary
- 2. Forecasting
- Predicting Customer Demand
- Cleaning Erroneous Data
- Plotting Data to Find Trends
- Performing Linear Regression
- Applying Algebra to the Regression Line
- Calculating Error Measurements
- Using Regression to Forecast Future Trends
- Trying More Regression Models
- Multivariate Linear Regression to Predict Sales
- Trigonometry to Capture Variations
- Choosing the Best Regression to Use for Forecasting
- Exploring Further
- Summary
- 3. Group Comparisons
- Reading Population Data
- Summary Statistics
- Random Samples
- Differences Between Sample Data
- Performing Hypothesis Testing
- The f-Test
- Nuances of Hypothesis Testing
- Comparing Groups in a Practical Context
- Summary
- 4. A/B Testing
- The Need for Experimentation
- Running Experiments to Test New Hypotheses
- Understanding the Math of A/B Testing
- Translating the Math into Practice
- Optimizing with the Champion/Challenger Framework
- Preventing Mistakes with Twyman's Law and A/A Testing
- Understanding Effect Sizes
- Calculating the Significance of Data
- Applications and Advanced Considerations
- The Ethics of A/B Testing
- Summary
- 5. Binary Classification
- Minimizing Customer Attrition
- Using Linear Probability Models to Find High-Risk Customers
- Plotting Attrition Risk
- Confirming Relationships with Linear Regression
- Predicting the Future
- Making Business Recommendations
- Measuring Prediction Accuracy
- Using Multivariate LPMs
- Creating New Metrics
- Considering the Weaknesses of LPMs
- Predicting Binary Outcomes with Logistic Regression
- Drawing Logistic Curves
- Fitting the Logistic Function to Our Data
- Applications of Binary Classification
- Summary
- 6. Supervised Learning
- Predicting Website Traffic
- Reading and Plotting News Article Data
- Using Linear Regression as a Prediction Method
- Understanding Supervised Learning
- K-Nearest Neighbors
- Implementing k-NN
- Performing k-NN with Python's sklearn
- Using Other Supervised Learning Algorithms
- Decision Trees
- Random Forests
- Neural Networks
- Measuring Prediction Accuracy
- Working with Multivariate Models
- Using Classification Instead of Regression
- Summary
- 7. Unsupervised Learning
- Unsupervised Learning vs. Supervised Learning
- Generating and Exploring Data
- Rolling the Dice
- Using Another Kind of Die
- The Origin of Observations with Clustering
- Clustering in Business Applications
- Analyzing Multiple Dimensions
- E-M Clustering
- The Guessing Step
- The Expectation Step
- The Maximization Step
- The Convergence Step
- Other Clustering Methods
- Other Unsupervised Learning Methods
- Summary
- 8. Web Scraping
- Understanding How Websites Work
- Creating Your First Web Scraper
- Parsing HTML Code
- Scraping an Email Address
- Searching for Addresses Directly
- Performing Searches with Regular Expressions
- Using Metacharacters for Flexible Searches
- Fine-Tuning Searches with Escape Sequences
- Combining Metacharacters for Advanced Searches
- Using Regular Expressions to Search for Email Addresses
- Converting Results to Usable Data
- Using Beautiful Soup
- Parsing HTML Label Elements
- Scraping and Parsing HTML Tables
- Advanced Scraping
- Summary
- 9. Recommendation Systems
- Popularity-Based Recommendations
- Item-Based Collaborative Filtering
- Measuring Vector Similarity
- Calculating Cosine Similarity
- Implementing Item-Based Collaborative Filtering
- User-Based Collaborative Filtering
- Case Study: Music Recommendations
- Generating Recommendations with Advanced Systems
- Summary
- 10. Natural Language Processing
- Using NLP to Detect Plagiarism
- Understanding the word2vec NLP Model
- Quantifying Similarities Between Words
- Creating a System of Equations
- Analyzing Numeric Vectors in word2vec
- Manipulating Vectors with Mathematical Calculations
- Detecting Plagiarism with word2vec
- Using Skip-Thoughts
- Topic Modeling
- Other Applications of NLP
- Summary
- 11. Data Science in Other Languages
- Winning Soccer Games with SQL
- Reading and Analyzing Data
- Getting Familiar with SQL
- Setting Up a SQL Database
- Running SQL Queries
- Combining Data by Joining Tables
- Winning Soccer Games with R
- Getting Familiar with R
- Applying Linear Regression in R
- Using K to Plot Data
- Gaining Other Valuable Skills
- Summary
- Index