
Statistics for Data Scient PB
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Content
- Front Cover
- Half-Title Page
- LICENSE, DISCLAIMER OF LIABILITY, AND LIMITED WARRANTY
- Title Page
- Copyright Page
- Dedication
- Contents
- Preface
- CHAPTER 1: Working with Data
- What is Data Literacy?
- Exploratory Data Analysis (EDA)
- Dealing with Data: What Can Go Wrong?
- An Explanation of Data Types
- Working with Data Types
- What is Drift?
- Discrete Data Versus Continuous Data
- Binning Data Values
- Correlation
- Working with Synthetic Data
- Summary
- CHAPTER 2: Introduction to Probability
- What is Set Theory?
- Open, Closed, Compact, and Convex Sets (Optional)
- Concepts in Probability
- Set Theory and Probability
- Coin Tossing Probabilities
- Dice Tossing Probabilities
- Card Drawing Probabilities
- Container-Based Probabilities
- Children-Related Probabilities
- Summary
- CHAPTER 3: Introduction to Statistics
- Introduction to Statistics
- Basic Concepts in Statistics
- The Variance and Standard Deviation
- The Moments of a Function (Optional)
- Random Variables
- Multiple Random Variables
- Sampling Techniques for a Population
- What is Bias?
- Two Important Results in Probability
- Summary
- CHAPTER 4: Metrics in Statistics
- The Confusion Matrix
- The ROC Curve and AUC Curve
- The sklearn.metrics Module (Optional)
- Statistical Metrics for Categorical Data
- Metrics for Continuous Data
- MAE, MSE, and RMSE
- Approximating Linear Data with np.linspace()
- Summary
- CHAPTER 5: Probability Distributions
- PDF, CDF, and PMF
- Two Types of Probability Distributions
- Discrete Probability Distributions
- Continuous Probability Distributions
- Advanced Probability Functions
- Non-Gaussian Distributions
- The Best-Fitting Distribution for Data
- Summary
- CHAPTER 6: Hypothesis Testing
- What is Hypothesis Testing?
- Components of Hypothesis Testing
- Test Statistics
- Working with p-values
- Working with Alpha Values
- Point Estimation, Confidence Level, and Confidence Intervals
- What is A/B Testing?
- The Lifespan of an A/B Test
- Maximum Likelihood Estimation (MLE)
- Summary
- Appendix A: Introduction to Python
- Tools for Python
- Python Installation
- Setting the PATH Environment Variable (Windows Only)
- Launching Python on Your Machine
- Identifiers
- Lines, Indentation, and Multi-Line Statements
- Quotation Marks and Comments
- Saving Your Code in a Module
- Some Standard Modules
- The help() and dir() Functions
- Compile Time and Runtime Code Checking
- Simple Data Types
- Working with Numbers
- Working with Fractions
- Unicode and UTF-8
- Working with Strings
- Slicing and Splicing Strings
- Search and Replace a String in Other Strings
- Remove Leading and Trailing Characters
- Printing Text without New Line Characters
- Text Alignment
- Working with Dates
- Exception Handling
- Handling User Input
- Python and Emojis (Optional)
- Command-Line Arguments
- Summary
- Appendix B: Introduction to Pandas
- What is Pandas?
- A Pandas Data Frame with a NumPy Example
- Describing a Pandas Data Frame
- Boolean Data Frames
- Data Frames and Random Numbers
- Reading CSV Files in Pandas
- The loc() and iloc() Methods
- Converting Categorical Data to Numeric Data
- Matching and Splitting Strings
- Converting Strings to Dates
- Working with Date Ranges
- Detecting Missing Dates
- Interpolating Missing Dates
- Other Operations with Dates
- Merging and Splitting Columns in Pandas
- Reading HTML Web Pages
- Saving a Pandas Data Frame as an HTML Web Page
- Summary
- Index
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