Python data science essentials : become an efficient data science practitioner by thoroughly understanding the key concepts of Python / Alberto Boschetti, Luca Massaron.

Format:

Book

Author/Creator:

Boschetti, Alberto, author.

Massaron, Luca, author.

Series:

Community experience distilled.

Community Experience Distilled

Language:

English

Subjects (All):

Python (Computer program language).

Scripting languages (Computer science).

Physical Description:

1 online resource (258 p.)

Edition:

1st edition

Other Title:

Become an efficient data science practitioner by thoroughly understanding the key concepts of Python

Place of Publication:

Birmingham, England ; Mumbai, [India] : Packt Publishing, 2015.

Language Note:

English

System Details:

text file

Summary:

If you are an aspiring data scientist and you have at least a working knowledge of data analysis and Python, this book will get you started in data science. Data analysts with experience of R or MATLAB will also find the book to be a comprehensive reference to enhance their data manipulation and machine learning skills.

Contents:

Cover; Copyright; Credits; About the Authors; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: First Steps; Introducing data science and Python; Installing Python; Python 2 or Python 3?; Step-by-step installation; A glance at the essential Python packages; NumPy; SciPy; pandas; Scikit-learn; IPython; Matplotlib; Statsmodels; Beautiful Soup; NetworkX; NLTK; Gensim; PyPy; The installation of packages; Package upgrades; Scientific distributions; Anaconda; Enthought Canopy; PythonXY; WinPython; Introducing IPython; The IPython Notebook

Datasets and code used in the bookScikit-learn toy datasets; MLdata.org public repository; LIBSVM data examples; Loading data directly from CSV or text files; Scikit-learn sample generators; Summary; Chapter 2: Data Munging; The data science process; Data loading and preprocessing with pandas; Fast and easy data loading; Dealing with problematic data; Dealing with big datasets; Accessing other data formats; Data preprocessing; Data selection; Working with categorical and textual data; A special type of data-text; Data processing with NumPy; NumPy's n-dimensional array

The basics of NumPy ndarray objectsCreating NumPy arrays; From lists to unidimensional arrays; Controlling the memory size; Heterogeneous lists; From lists to multidimensional arrays; Resizing arrays; Arrays derived from NumPy functions; Getting an array directly from a file; Extracting data from pandas; NumPy fast operation and computations; Matrix operations; Slicing and indexing with NumPy arrays; Stacking NumPy arrays; Summary; Chapter 3: Data Science Pipeline; Introducing EDA; Feature creation; Dimensionality reduction; Covariance matrix; Principal Component Analysis (PCA)

A variation of PCA for big data-randomized PCALatent Factor Analysis (LFA); Linear Discriminant Analysis (LDA); Latent Semantical Analysis (LSA); Independent Component Analysis (ICA); Kernel PCA; Restricted Boltzmann Machine (RBM); Detection and treatment of outliers; Univariate outlier detection; EllipticEnvelope; OneClassSVM; Scoring functions; Multilabel classification; Binary classification; Regression; Testing and validating; Cross-validation; Using cross-validation iterators; Sampling and bootstrapping; Hyper-parameters optimization; Building custom scoring functions

Reducing grid search runtimeFeature selection; Univariate selection; Recursive elimination; Stability and L1-based selection; Summary; Chapter 4: Machine Learning; Linear and logistic regression; Naive Bayes; The k-Nearest Neighbors; Advanced nonlinear algorithms; SVM for classification; SVM for regression; Tuning SVM; Ensemble strategies; Pasting by random samples; Bagging with weak ensembles; Random Subspaces and Random Patches; Sequences of models - AdaBoost; Gradient tree boosting (GTB); Dealing with big data; Creating some big datasets as examples; Scalability with volume

Keeping up with velocity

Notes:

Includes index.

Description based on online resource; title from PDF title page (ebrary, viewed June 8, 2015).

OCLC:

910639609