Python Data Analysis Second by Armando Fandango (21 results)
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- Softcover
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- Softcover
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- Softcover
- Print on Demand
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- Softcover
- Print on Demand
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- Softcover
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Über den AutorrnrnArmando Fandango creates AI empowered products by leveraging his expertise in deep learning, machine learning, distributed computing, and computational methods and has provided thought leadership… roles as Chief Data Scient.
- Softcover
- Print on Demand
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Learn how to apply powerful data analysis techniques with popular open source Python modulesKey FeaturesFind, manipulate, and analyze your data using the Python 3.5 librariesPerform advanced, high-performance linear algebra and mathemati…cal calculations with clean and efficient Python codeAn easy-to-follow guide with realistic examples that are frequently used in real-world data analysis projects.Book DescriptionData analysis techniques generate useful insights from small and large volumes of data. Python, with its strong set of libraries, has become a popular platform to conduct various data analysis and predictive modeling tasks.With this book, you will learn how to process and manipulate data with Python for complex analysis and modeling. We learn data manipulations such as aggregating, concatenating, appending, cleaning, and handling missing values, with NumPy and Pandas. The book covers how to store and retrieve data from various data sources such as SQL and NoSQL, CSV fies, and HDF5. We learn how to visualize data using visualization libraries, along with advanced topics such as signal processing, time series, textual data analysis, machine learning, and social media analysis.The book covers a plethora of Python modules, such as matplotlib, statsmodels, scikit-learn, and NLTK. It also covers using Python with external environments such as R, Fortran, C/C++, and Boost libraries.What you will learnInstall open source Python modules such NumPy, SciPy, Pandas, stasmodels, scikit-learn,theano, keras, and tensorflow on various platformsPrepare and clean your data, and use it for exploratory analysisManipulate your data with PandasRetrieve and store your data from RDBMS, NoSQL, and distributed filesystems such as HDFS and HDF5Visualize your data with open source libraries such as matplotlib, bokeh, and plotlyLearn about various machine learning methods such as supervised, unsupervised, probabilistic, and BayesianUnderstand signal processing and time series data analysisGet to grips with graph processing and social network analysis.
