Python Data Science Essentials

Table of contents

1. Python Data Science Essentials
   1. Table of Contents
   2. Python Data Science Essentials
   3. Credits
   4. About the Authors
   5. About the Reviewers
   6. www.PacktPub.com
      1. Support files, eBooks, discount offers, and more
         1. Why subscribe?
         2. Free access for Packt account holders
   7. 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. Errata
         3. Piracy
         4. Questions
   8. 1. First Steps
      1. Introducing data science and Python
      2. Installing Python
         1. Python 2 or Python 3?
         2. Step-by-step installation
         3. A glance at the essential Python packages
            1. NumPy
            2. SciPy
            3. pandas
            4. Scikit-learn
            5. IPython
            6. Matplotlib
            7. Statsmodels
            8. Beautiful Soup
            9. NetworkX
            10. NLTK
            11. Gensim
            12. PyPy
         4. The installation of packages
         5. Package upgrades
      3. Scientific distributions
         1. Anaconda
         2. Enthought Canopy
         3. PythonXY
         4. WinPython
      4. Introducing IPython
         1. The IPython Notebook
         2. Datasets and code used in the book
            1. Scikit-learn toy datasets
            2. The MLdata.org public repository
            3. LIBSVM data examples
            4. Loading data directly from CSV or text files
            5. Scikit-learn sample generators
      5. Summary
   9. 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 textual data
         1. A special type of data – text
      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 fast operation and computations
         1. Matrix operations
         2. Slicing and indexing with NumPy arrays
         3. Stacking NumPy arrays
      7. Summary
   10. 3. The Data Science Pipeline
       1. Introducing EDA
       2. Feature creation
       3. Dimensionality reduction
          1. The covariance matrix
          2. Principal Component Analysis (PCA)
          3. A variation of 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. Restricted Boltzmann Machine (RBM)
       4. The detection and treatment of outliers
          1. Univariate outlier detection
          2. EllipticEnvelope
          3. OneClassSVM
       5. Scoring functions
          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. Hyper-parameters' optimization
          1. Building custom scoring functions
          2. Reducing the grid search runtime
       9. Feature selection
          1. Univariate selection
          2. Recursive elimination
          3. Stability and L1-based selection
       10. Summary
   11. 4. Machine Learning
       1. Linear and logistic regression
       2. Naive Bayes
       3. The k-Nearest Neighbors
       4. Advanced nonlinear algorithms
          1. SVM for classification
          2. SVM for regression
          3. Tuning SVM
       5. Ensemble strategies
          1. Pasting by random samples
          2. Bagging with weak ensembles
          3. Random Subspaces and Random Patches
          4. Sequences of models – AdaBoost
          5. Gradient tree boosting (GTB)
          6. 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. A quick overview of Stochastic Gradient Descent (SGD)
       6. A peek into 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
       7. An overview of unsupervised learning
       8. Summary
   12. 5. Social Network Analysis
       1. Introduction to graph theory
       2. Graph algorithms
       3. Graph loading, dumping, and sampling
       4. Summary
   13. 6. Visualization
       1. Introducing the basics of matplotlib
          1. Curve plotting
          2. Using panels
          3. Scatterplots
          4. Histograms
          5. Bar graphs
          6. Image visualization
       2. Selected graphical examples with pandas
          1. Boxplots and histograms
          2. Scatterplots
          3. Parallel coordinates
       3. Advanced data learning representation
          1. Learning curves
          2. Validation curves
          3. Feature importance
          4. GBT partial dependence plot
       4. Summary
   14. Index