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Python Data Science Essentials : a Practitioner's Guide Covering Essential Data Science Principles, Tools, and Techniques, 3rd Edition.

Author: Alberto Boschetti; Luca Massaron
Publisher: Birmingham : Packt Publishing Ltd, 2018.
Edition/Format:   eBook : Document : English : 3rd edView all editions and formats
Summary:
Python Data Science Essentials, Third Edition provides modern insight in setting up and performing data science operations effectively using the latest python tools and libraries. It builds faster governance on the most essential tasks such as data munging and pre-processing, along with all the techniques you require.
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Boschetti, Alberto.
Python Data Science Essentials : A Practitioner's Guide Covering Essential Data Science Principles, Tools, and Techniques, 3rd Edition.
Birmingham : Packt Publishing Ltd, ©2018
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Alberto Boschetti; Luca Massaron
ISBN: 9781789531893 1789531896
OCLC Number: 1056906376
Notes: Wrapping everything in a pipeline.
Description: 1 online resource (466 pages)
Contents: Cover; Title Page; Copyright and Credits; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: First Steps; Introducing data science and Python; Installing Python; Python 2 or Python 3?; Step-by-step installation; Installing the necessary packages; Package upgrades; Scientific distributions; Anaconda; Leveraging conda to install packages; Enthought Canopy; WinPython; Explaining virtual environments; Conda for managing environments; A glance at the essential packages; NumPy; SciPy; pandas; pandas-profiling; Scikit-learn; Jupyter; JupyterLab; Matplotlib; Seaborn; Statsmodels. Beautiful SoupNetworkX; NLTK; Gensim; PyPy; XGBoost; LightGBM; CatBoost; TensorFlow; Keras; Introducing Jupyter; Fast installation and first test usage; Jupyter magic commands; Installing packages directly from Jupyter Notebooks; Checking the new JupyterLab environment; How Jupyter Notebooks can help data scientists; Alternatives to Jupyter; Datasets and code used in this book; Scikit-learn toy datasets; The MLdata.org and other public repositories for open source data; LIBSVM data examples; Loading data directly from CSV or text files; Scikit-learn sample generators; Summary. Chapter 2: Data MungingThe 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; Putting data together; Data preprocessing; Data selection; Working with categorical and textual data; A special type of data --
text; Scraping the web with Beautiful Soup; Data processing with NumPy; NumPy's n-dimensional array; The basics of NumPy ndarray objects; Creating NumPy arrays; From lists to unidimensional arrays; Controlling memory size; Heterogeneous lists. From lists to multidimensional arraysResizing 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; Working with sparse arrays; Summary; Chapter 3: The Data Pipeline; Introducing EDA; Building new features; Dimensionality reduction; The covariance matrix; Principal component analysis; PCA for big data --
RandomizedPCA; Latent factor analysis; Linear discriminant analysis; Latent semantical analysis. Independent component analysisKernel PCA; T-SNE; Restricted Boltzmann Machine; The detection and treatment of outliers; Univariate outlier detection; EllipticEnvelope; OneClassSVM; Validation metrics; Multilabel classification; Binary classification; Regression; Testing and validating; Cross-validation; Using cross-validation iterators; Sampling and bootstrapping; Hyperparameter optimization; Building custom scoring functions; Reducing the grid search runtime; Feature selection; Selection based on feature variance; Univariate selection; Recursive elimination; Stability and L1-based selection.

Abstract:

Python Data Science Essentials, Third Edition provides modern insight in setting up and performing data science operations effectively using the latest python tools and libraries. It builds faster  Read more...

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