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Python for Information Professionals: How to Design Practical Applications to Capitalize on the Data Explosion

De (autor): Brady Lund

Coperta cărții 'Python for Information Professionals: How to Design Practical Applications to Capitalize on the Data Explosion - Brady'
Python for Information Professionals: How to Design Practical Applications to Capitalize on the Data Explosion

De (autor): Brady Lund

Python for Information Professionals: How to Design Practical Applications to Capitalize on the Data Explosion is an introduction to the Python programming language for library and information professionals with little or no prior experience. As opposed to the many Python books available today that focus on the language only from a general sense, this book is designed specifically for information professionals who are seeking to advance their career prospects or challenge themselves in new ways by acquiring skills within the rapidly expanding field of data science.

Readers of Python for Information Professionals will learn to:

    1. Develop Python applications for the retrieval, cleaning, and analysis of large datasets.
    2. Design applications to support traditional library functions and create new opportunities to maximize library value.
    3. Consider data security and privacy relevant to data analysis when using the Python language.

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Python for Information Professionals: How to Design Practical Applications to Capitalize on the Data Explosion is an introduction to the Python programming language for library and information professionals with little or no prior experience. As opposed to the many Python books available today that focus on the language only from a general sense, this book is designed specifically for information professionals who are seeking to advance their career prospects or challenge themselves in new ways by acquiring skills within the rapidly expanding field of data science.

Readers of Python for Information Professionals will learn to:

    1. Develop Python applications for the retrieval, cleaning, and analysis of large datasets.
    2. Design applications to support traditional library functions and create new opportunities to maximize library value.
    3. Consider data security and privacy relevant to data analysis when using the Python language.

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