最新章節

• Index
• Bibliography
• About matplotlib
• Packages websites
• Packages installed with Anaconda
• An overview of conda

• Index 更新時間：2021-07-09 18:52:29
• Bibliography
• About matplotlib
• Packages websites
• Packages installed with Anaconda
• An overview of conda
• Appendix B. Go Forth and Explore Visualization
• Summary
• Computer simulation
• Chapter 8. Advanced Visualization
• Summary
• Stochastic block models
• A genetic programming example
• Maximum flow and minimum cut
• The directed acyclic graph test
• The planar graph test
• Analysis of social networks
• The clustering coefficient of graphs
• Directed graphs and multigraphs
• Chapter 7. Bioinformatics Genetics and Network Models
• Summary
• k-means clustering
• Principal component analysis
• Support vector machines
• Logistic regression
• k-nearest neighbors
• Viewing positive sentiments using word clouds
• The Na?ˉve Bayes classifier using TextBlob
• The Na?ˉve Bayes classifier
• The Bayes theorem
• Decision tree
• Linear regression
• Understanding linear regression
• Classification methods
• Chapter 6. Statistical and Machine Learning
• Summary
• Creating animated and interactive plots
• An overview of statistical and machine learning
• The threshold model
• The stochastic model
• The deterministic model
• Chapter 5. Financial and Statistical Models
• Summary
• The visualization example in sports
• Visualization using matplotlib
• Other data structures
• Array indexing
• Slicing
• Scalar selection
• NumPy SciPy and MKL functions
• Chapter 4. Numerical Computing and Interactive Plotting
• Summary
• Interactive visualization packages
• Visualization plots with Anaconda
• The IDE tools in Python
• Chapter 3. Getting Started with the Python IDE
• Summary
• Interactive visualization
• Visualization tools in Python
• Some best practices for visualization
• Perception and presentation methods
• Creating interesting stories with data
• A sports example
• The Ebola example
• Why does visualization require planning?
• Chapter 2. Data Analysis and Visualization
• Summary
• Visualization plots
• How does visualization help decision-making?
• Data visualization history
• The transformation of data
• Data information knowledge and insight
• Chapter 1. A Conceptual Framework for Data Visualization
• Part 3. Module 3
• Appendix A. A List of Links
• Summary
• Best practices for business contexts
• Best practices for statistics
• Best practices for algorithms
• Best practices for data handling
• Best practices for coding
• Chapter 9. Best Practices for Predictive Modelling
• Summary
• Understanding and implementing random forests
• Understanding and implementing regression trees
• Implementing a decision tree with scikit-learn
• Understanding the mathematics behind decision trees
• Introducing decision trees
• Chapter 8. Trees and Random Forests with Python
• Summary
• Fine-tuning the clustering
• Implementing clustering using Python
• Mathematics behind clustering
• Introduction to clustering – what why and how?
• Chapter 7. Clustering with Python
• Summary
• Model validation
• Model validation and evaluation
• Implementing logistic regression with Python
• Understanding the math behind logistic regression
• Linear regression versus logistic regression
• Chapter 6. Logistic Regression with Python
• Summary
• Handling other issues in linear regression
• Model validation
• Implementing linear regression with Python
• Making sense of result parameters
• Understanding the maths behind linear regression
• Chapter 5. Linear Regression with Python
• Summary
• Correlation
• Chi-square tests
• Hypothesis testing
• Random sampling and the central limit theorem
• Chapter 4. Statistical Concepts for Predictive Modelling
• Summary
• Merging/joining datasets
• Concatenating and appending data
• Random sampling – splitting a dataset in training and testing datasets
• Grouping the data – aggregation filtering and transformation
• Generating random numbers and their usage
• Subsetting a dataset
• Chapter 3. Data Wrangling
• Summary
• Visualizing a dataset by basic plotting
• Creating dummy variables
• Handling missing values
• Basics – summary dimensions and structure
• Case 4 – miscellaneous cases
• Case 3 – reading data from a URL
• Case 2 – reading a dataset using the open method of Python
• Use cases of the read_csv method
• The read_csv method
• Various methods of importing data in Python
• Reading the data – variations and examples
• Chapter 2. Data Cleaning
• Summary
• IDEs for Python
• Python and its packages for predictive modelling
• Python and its packages – download and installation
• Applications and examples of predictive modelling
• Introducing predictive modelling
• Chapter 1. Getting Started with Predictive Modelling
• Part 2. Module 2
• Summary
• Measuring prediction performance
• Unsupervised learning – clustering and dimensionality reduction
• Supervised learning – classification and regression
• Data representation in scikit-learn
• The scikit-learn modules for different models
• An overview of machine learning models
• Chapter 8. Machine Learning Models with scikit-learn
• Summary
• Grouping data
• Data aggregation
• Data munging
• Chapter 7. Data Analysis Application Examples
• Summary
• Interacting with data in Redis
• Interacting with data in MongoDB
• Interacting with data in binary format
• Interacting with data in text format
• Chapter 6. Interacting with Databases
• Summary
• Time series plotting
• Timedeltas
• Time zone handling
• Upsampling time series data
• Downsampling time series data
• Resampling time series
• Working with date and time objects
• Time series primer
• Chapter 5. Time Series
• Summary
• Additional Python data visualization tools
• Plotting functions with Pandas
• Legends and annotations
• Exploring plot types
• The matplotlib API primer
• Chapter 4. Data Visualization
• Summary
• Advanced uses of Pandas for data analysis
• Working with missing data
• Computational tools
• Indexing and selecting data
• The essential basic functionality
• The Pandas data structure
• An overview of the Pandas package
• Chapter 3. Data Analysis with Pandas
• Summary
• NumPy random numbers
• Linear algebra with NumPy
• Data processing using arrays
• Array functions
• NumPy arrays
• Chapter 2. NumPy Arrays and Vectorized Computation
• Summary
• Python libraries in data analysis
• An overview of the libraries in data analysis
• Data analysis and processing
• Chapter 1. Introducing Data Analysis and Libraries
• Part 1. Module 1
• Preface
• Credits
• 版權信息
• 封面

• 封面
• 版權信息
• Credits
• Preface
• Part 1. Module 1
• Chapter 1. Introducing Data Analysis and Libraries
• Data analysis and processing
• An overview of the libraries in data analysis
• Python libraries in data analysis
• Summary
• Chapter 2. NumPy Arrays and Vectorized Computation
• NumPy arrays
• Array functions
• Data processing using arrays
• Linear algebra with NumPy
• NumPy random numbers
• Summary
• Chapter 3. Data Analysis with Pandas
• An overview of the Pandas package
• The Pandas data structure
• The essential basic functionality
• Indexing and selecting data
• Computational tools
• Working with missing data
• Advanced uses of Pandas for data analysis
• Summary
• Chapter 4. Data Visualization
• The matplotlib API primer
• Exploring plot types
• Legends and annotations
• Plotting functions with Pandas
• Additional Python data visualization tools
• Summary
• Chapter 5. Time Series
• Time series primer
• Working with date and time objects
• Resampling time series
• Downsampling time series data
• Upsampling time series data
• Time zone handling
• Timedeltas
• Time series plotting
• Summary
• Chapter 6. Interacting with Databases
• Interacting with data in text format
• Interacting with data in binary format
• Interacting with data in MongoDB
• Interacting with data in Redis
• Summary
• Chapter 7. Data Analysis Application Examples
• Data munging
• Data aggregation
• Grouping data
• Summary
• Chapter 8. Machine Learning Models with scikit-learn
• An overview of machine learning models
• The scikit-learn modules for different models
• Data representation in scikit-learn
• Supervised learning – classification and regression
• Unsupervised learning – clustering and dimensionality reduction
• Measuring prediction performance
• Summary
• Part 2. Module 2
• Chapter 1. Getting Started with Predictive Modelling
• Introducing predictive modelling
• Applications and examples of predictive modelling
• Python and its packages – download and installation
• Python and its packages for predictive modelling
• IDEs for Python
• Summary
• Chapter 2. Data Cleaning
• Reading the data – variations and examples
• Various methods of importing data in Python
• The read_csv method
• Use cases of the read_csv method
• Case 2 – reading a dataset using the open method of Python
• Case 3 – reading data from a URL
• Case 4 – miscellaneous cases
• Basics – summary dimensions and structure
• Handling missing values
• Creating dummy variables
• Visualizing a dataset by basic plotting
• Summary
• Chapter 3. Data Wrangling
• Subsetting a dataset
• Generating random numbers and their usage
• Grouping the data – aggregation filtering and transformation
• Random sampling – splitting a dataset in training and testing datasets
• Concatenating and appending data
• Merging/joining datasets
• Summary
• Chapter 4. Statistical Concepts for Predictive Modelling
• Random sampling and the central limit theorem
• Hypothesis testing
• Chi-square tests
• Correlation
• Summary
• Chapter 5. Linear Regression with Python
• Understanding the maths behind linear regression
• Making sense of result parameters
• Implementing linear regression with Python
• Model validation
• Handling other issues in linear regression
• Summary
• Chapter 6. Logistic Regression with Python
• Linear regression versus logistic regression
• Understanding the math behind logistic regression
• Implementing logistic regression with Python
• Model validation and evaluation
• Model validation
• Summary
• Chapter 7. Clustering with Python
• Introduction to clustering – what why and how?
• Mathematics behind clustering
• Implementing clustering using Python
• Fine-tuning the clustering
• Summary
• Chapter 8. Trees and Random Forests with Python
• Introducing decision trees
• Understanding the mathematics behind decision trees
• Implementing a decision tree with scikit-learn
• Understanding and implementing regression trees
• Understanding and implementing random forests
• Summary
• Chapter 9. Best Practices for Predictive Modelling
• Best practices for coding
• Best practices for data handling
• Best practices for algorithms
• Best practices for statistics
• Best practices for business contexts
• Summary
• Appendix A. A List of Links
• Part 3. Module 3
• Chapter 1. A Conceptual Framework for Data Visualization
• Data information knowledge and insight
• The transformation of data
• Data visualization history
• How does visualization help decision-making?
• Visualization plots
• Summary
• Chapter 2. Data Analysis and Visualization
• Why does visualization require planning?
• The Ebola example
• A sports example
• Creating interesting stories with data
• Perception and presentation methods
• Some best practices for visualization
• Visualization tools in Python
• Interactive visualization
• Summary
• Chapter 3. Getting Started with the Python IDE
• The IDE tools in Python
• Visualization plots with Anaconda
• Interactive visualization packages
• Summary
• Chapter 4. Numerical Computing and Interactive Plotting
• NumPy SciPy and MKL functions
• Scalar selection
• Slicing
• Array indexing
• Other data structures
• Visualization using matplotlib
• The visualization example in sports
• Summary
• Chapter 5. Financial and Statistical Models
• The deterministic model
• The stochastic model
• The threshold model
• An overview of statistical and machine learning
• Creating animated and interactive plots
• Summary
• Chapter 6. Statistical and Machine Learning
• Classification methods
• Understanding linear regression
• Linear regression
• Decision tree
• The Bayes theorem
• The Na?ˉve Bayes classifier
• The Na?ˉve Bayes classifier using TextBlob
• Viewing positive sentiments using word clouds
• k-nearest neighbors
• Logistic regression
• Support vector machines
• Principal component analysis
• k-means clustering
• Summary
• Chapter 7. Bioinformatics Genetics and Network Models
• Directed graphs and multigraphs
• The clustering coefficient of graphs
• Analysis of social networks
• The planar graph test
• The directed acyclic graph test
• Maximum flow and minimum cut
• A genetic programming example
• Stochastic block models
• Summary
• Chapter 8. Advanced Visualization
• Computer simulation
• Summary
• Appendix B. Go Forth and Explore Visualization
• An overview of conda
• Packages installed with Anaconda
• Packages websites
• About matplotlib
• Bibliography
• Index 更新時間：2021-07-09 18:52:29