Python Data Science Essentials - Second Edition

Table of contents

1. Python Data Science Essentials - Second Edition
   1. Python Data Science Essentials - Second Edition
   2. Credits
   3. About the Authors
   4. About the Reviewer
   5. www.PacktPub.com
      1. Why subscribe?
   6. Preface
      1. What this book covers
      2. What you need for this book
      3. Who this book is for
      4. Conventions
      5. Reader feedback
      6. Customer support
         1. Downloading the example code
         2. Downloading the color images of this book
         3. Errata
         4. Piracy
         5. Questions
   7. 1. First Steps
      1. Introducing data science and Python
      2. Installing Python
         1. Python 2 or Python 3?
         2. Step-by-step installation
         3. The installation of packages
         4. Package upgrades
         5. Scientific distributions
            1. Anaconda
            2. Leveraging conda to install packages
            3. Enthought Canopy
            4. PythonXY
            5. WinPython
         6. Explaining virtual environments
            1. conda for managing environments
         7. A glance at the essential packages
            1. NumPy
            2. SciPy
            3. pandas
            4. Scikit-learn
            5. Jupyter
            6. Matplotlib
            7. Statsmodels
            8. Beautiful Soup
            9. NetworkX
            10. NLTK
            11. Gensim
            12. PyPy
            13. XGBoost
            14. Theano
            15. Keras
      3. Introducing Jupyter
         1. Fast installation and first test usage
         2. Jupyter magic commands
         3. How Jupyter Notebooks can help data scientists
         4. Alternatives to Jupyter
      4. Datasets and code used in the book
         1. Scikit-learn toy datasets
            1. The MLdata.org public repository
            2. LIBSVM data examples
            3. Loading data directly from CSV or text files
            4. Scikit-learn sample generators
      5. Summary
   8. 2. Data Munging
      1. The data science process
      2. Data loading and preprocessing with pandas
         1. Fast and easy data loading
         2. Dealing with problematic data
         3. Dealing with big datasets
         4. Accessing other data formats
         5. Data preprocessing
         6. Data selection
      3. Working with categorical and text data
         1. A special type of data – text
         2. Scraping the Web with Beautiful Soup
      4. Data processing with NumPy
         1. NumPy's n-dimensional array
         2. The basics of NumPy ndarray objects
      5. Creating NumPy arrays
         1. From lists to unidimensional arrays
         2. Controlling the memory size
         3. Heterogeneous lists
         4. From lists to multidimensional arrays
         5. Resizing arrays
         6. Arrays derived from NumPy functions
         7. Getting an array directly from a file
         8. Extracting data from pandas
      6. NumPy's fast operations and computations
         1. Matrix operations
         2. Slicing and indexing with NumPy arrays
         3. Stacking NumPy arrays
      7. Summary
   9. 3. The Data Pipeline
      1. Introducing EDA
      2. Building new features
      3. Dimensionality reduction
         1. The covariance matrix
         2. Principal Component Analysis (PCA)
         3. PCA for big data – RandomizedPCA
         4. Latent Factor Analysis (LFA)
         5. Linear Discriminant Analysis (LDA)
         6. Latent Semantical Analysis (LSA)
         7. Independent Component Analysis (ICA)
         8. Kernel PCA
         9. T-SNE
         10. Restricted Boltzmann Machine (RBM)
      4. The detection and treatment of outliers
         1. Univariate outlier detection
         2. EllipticEnvelope
         3. OneClassSVM
      5. Validation metrics
         1. Multilabel classification
         2. Binary classification
         3. Regression
      6. Testing and validating
      7. Cross-validation
         1. Using cross-validation iterators
         2. Sampling and bootstrapping
      8. Hyperparameter optimization
         1. Building custom scoring functions
         2. Reducing the grid search runtime
      9. Feature selection
         1. Selection based on feature variance
         2. Univariate selection
         3. Recursive elimination
         4. Stability and L1-based selection
      10. Wrapping everything in a pipeline
          1. Combining features together and chaining transformations
          2. Building custom transformation functions
      11. Summary
   10. 4. Machine Learning
       1. Preparing tools and datasets
       2. Linear and logistic regression
       3. Naive Bayes
       4. K-Nearest Neighbors
       5. Nonlinear algorithms
          1. SVM for classification
          2. SVM for regression
          3. Tuning SVM
       6. Ensemble strategies
          1. Pasting by random samples
          2. Bagging with weak classifiers
          3. Random subspaces and random patches
          4. Random Forests and Extra-Trees
          5. Estimating probabilities from an ensemble
          6. Sequences of models – AdaBoost
          7. Gradient tree boosting (GTB)
          8. XGBoost
       7. Dealing with big data
          1. Creating some big datasets as examples
          2. Scalability with volume
          3. Keeping up with velocity
          4. Dealing with variety
          5. An overview of Stochastic Gradient Descent (SGD)
       8. Approaching deep learning
       9. A peek at Natural Language Processing (NLP)
          1. Word tokenization
          2. Stemming
          3. Word tagging
          4. Named Entity Recognition (NER)
          5. Stopwords
          6. A complete data science example – text classification
       10. An overview of unsupervised learning
       11. Summary
   11. 5. Social Network Analysis
       1. Introduction to graph theory
       2. Graph algorithms
       3. Graph loading, dumping, and sampling
       4. Summary
   12. 6. Visualization, Insights, and Results
       1. Introducing the basics of matplotlib
          1. Curve plotting
          2. Using panels
          3. Scatterplots for relationships in data
          4. Histograms
          5. Bar graphs
          6. Image visualization
          7. Selected graphical examples with pandas
             1. Boxplots and histograms
          8. Scatterplots
          9. Parallel coordinates
       2. Wrapping up matplotlib's commands
          1. Introducing Seaborn
          2. Enhancing your EDA capabilities
       3. Interactive visualizations with Bokeh
       4. Advanced data-learning representations
          1. Learning curves
          2. Validation curves
          3. Feature importance for RandomForests
          4. GBT partial dependence plots
          5. Creating a prediction server for ML-AAS
       5. Summary
   13. 1. Strengthen Your Python Foundations
       1. Your learning list
          1. Lists
          2. Dictionaries
          3. Defining functions
          4. Classes, objects, and OOP
          5. Exceptions
          6. Iterators and generators
          7. Conditionals
          8. Comprehensions for lists and dictionaries
       2. Learn by watching, reading, and doing
          1. MOOCs
          2. PyCon and PyData
          3. Interactive Jupyter
          4. Don't be shy, take a real challenge