Statistics for Data Scient PB
1st Edition
Published on 27. April 2024
274 pages
978-1-68392-879-9 (ISBN)
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No detailed description available for "Statistics Using Python".
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Language
English
Place of publication
Herndon, VA
United States
Target group
Professional and scholarly
US School Grade: College Graduate Student
File size
9,45 MB
ISBN-13
978-1-68392-879-9 (9781683928799)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
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Statistics Using Python
Book
12/2023
1st Edition
Mercury Learning & Information
€54.95
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Statistics Using Python
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12/2023
1st Edition
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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
