Elegant sciPy : the art of scientific Python / by Nunez-Iglesias, Juan

Table of Contents: “…Elegant NumPy: the foundation of Scientific Python -- Quantile normalization with NumPy and SciPy -- Networks of image regions with ndimage -- Frequency and the fast Fourier transform -- Contingency tables using sparse coordinate matrices -- Linear algebra in SciPy -- Function optimization in SciPy -- Big data in little laptop with Toolz.…”


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Introduction to Python for science and engineering by Pine, David J.

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Introduction to Python for science and engineering by Pine, David

Table of Contents: “…Introduction -- Launching Python -- Strings, lists, arrays, and dictionaries -- Input and output -- Conditionals and loops -- Plotting -- Functions -- Curve fitting -- Numerical routines : SciPy and NumPy -- Data manipulation and analysis : Pandas -- Animation -- Python classes and GUIs -- Installing Python -- Jupyter notebooks -- Glossary -- Python resources.…”


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Data science bookcamp : five Python projects by Apeltsin, Leonard

Table of Contents: “…Basic probability and statistical analysis using SciPy -- 6. Making predictions using the central limit theorem and SciPy -- 7. …”


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Bayesian methods for hackers : probabilistic programming and Bayesian inference by Davidson-Pilon, Cameron

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Think complexity : complexity science and computational modeling by Downey, Allen

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Data just right : introduction to large-scale data & analytics by Manoochehri, Michael

Table of Contents: “…-- Challenges of Machine Learning -- Bayesian Classification -- Clustering -- Recommendation Engines -- Apache Mahout: Scalable Machine Learning -- Using Mahout to Classify Text -- MLBase: Distributed Machine Learning Framework -- Summary -- VI.Statistical Analysis for Massive Datasets -- 11.Using R with Large Datasets -- Why Statistics Are Sexy -- Limitations of R for Large Datasets -- R Data Frames and Matrices -- Strategies for Dealing with Large Datasets -- Large Matrix Manipulation: bigmemory and biganalytics -- ff: Working with Data Frames Larger than Memory -- biglm: Linear Regression for Large Datasets -- RHadoop: Accessing Apache Hadoop from R -- Summary -- 12.Building Analytics Workflows Using Python and Pandas -- The Snakes Are Loose in the Data Zoo -- Choosing a Language for Statistical Computation -- Extending Existing Code -- Tools and Testing -- Python Libraries for Data Processing -- NumPy -- SciPy: Scientific Computing for Python -- The Pandas Data Analysis Library -- Building More Complex Workflows -- Working with Bad or Missing Records -- iPython: Completing the Scientific Computing Tool Chain -- Parallelizing iPython Using a Cluster -- Summary -- VII.Looking Ahead -- 13.When to Build, When to Buy, When to Outsource -- Overlapping Solutions -- Understanding Your Data Problem -- A Playbook for the Build versus Buy Problem -- What Have You Already Invested In? …”


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