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Published by Pragmatic Programmers, LLC, The, 2016
ISBN 10: 1680501844 ISBN 13: 9781680501841
Language: English
Seller: Better World Books, Mishawaka, IN, U.S.A.
Condition: Very Good. Used book that is in excellent condition. May show signs of wear or have minor defects.
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Paperback. Condition: Very Good. No Jacket. May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less 0.95.
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Seller: GreatBookPrices, Columbia, MD, U.S.A.
Condition: As New. Unread book in perfect condition.
Published by Pragmatic Bookshelf, Raleigh, 2016
ISBN 10: 1680501844 ISBN 13: 9781680501841
Language: English
Seller: Grand Eagle Retail, Mason, OH, U.S.A.
Paperback. Condition: new. Paperback. Go from messy, unstructured artifacts stored in SQL and NoSQL databases to a neat, well-organized dataset with this quick reference for the busy data scientist. Understand text mining, machine learning, and network analysis; process numeric data with the NumPy and Pandas modules; describe and analyze data using statistical and network-theoretical methods; and see actual examples of data analysis at work. This one-stop solution covers the essential data science you need in Python. Data science is one of the fastest-growing disciplines in terms of academic research, student enrollment, and employment. Python, with its flexibility and scalability, is quickly overtaking the R language for data-scientific projects. Keep Python data-science concepts at your fingertips with this modular, quick reference to the tools used to acquire, clean, analyze, and store data. This one-stop solution covers essential Python, databases, network analysis, natural language processing, elements of machine learning, and visualization. Access structured and unstructured text and numeric data from local files, databases, and the Internet. Arrange, rearrange, and clean the data.Work with relational and non-relational databases, data visualization, and simple predictive analysis (regressions, clustering, and decision trees). See how typical data analysis problems are handled. And try your hand at your own solutions to a variety of medium-scale projects that are fun to work on and look good on your resume. Keep this handy quick guide at your side whether you're a student, an entry-level data science professional converting from R to Python, or a seasoned Python developer who doesn't want to memorize every function and option. What You Need: You need a decent distribution of Python 3.3 or above that includes at least NLTK, Pandas, NumPy, Matplotlib, Networkx, SciKit-Learn, and BeautifulSoup. A great distribution that meets the requirements is Anaconda, available for free from If you plan to set up your own database servers, you also need MySQL and MongoDB . Both packages are free and run on Windows, Linux, and Mac OS. Go from messy, unstructured artifacts stored in SQL and NoSQL databases to a neat, well-organized dataset with this quick reference for the busy data scientist. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Seller: GreatBookPrices, Columbia, MD, U.S.A.
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Published by Pragmatic Bookshelf 8/20/2016, 2016
ISBN 10: 1680501844 ISBN 13: 9781680501841
Language: English
Seller: BargainBookStores, Grand Rapids, MI, U.S.A.
Paperback or Softback. Condition: New. Data Science Essentials in Python: Collect - Organize - Explore - Predict - Value 0.87. Book.
Seller: Lucky's Textbooks, Dallas, TX, U.S.A.
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Seller: California Books, Miami, FL, U.S.A.
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Add to basketPaperback / softback. Condition: New. New copy - Usually dispatched within 4 working days. 438.
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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Published by Pragmatic Bookshelf, Raleigh, 2016
ISBN 10: 1680501844 ISBN 13: 9781680501841
Language: English
Seller: AussieBookSeller, Truganina, VIC, Australia
US$ 42.67
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Add to basketPaperback. Condition: new. Paperback. Go from messy, unstructured artifacts stored in SQL and NoSQL databases to a neat, well-organized dataset with this quick reference for the busy data scientist. Understand text mining, machine learning, and network analysis; process numeric data with the NumPy and Pandas modules; describe and analyze data using statistical and network-theoretical methods; and see actual examples of data analysis at work. This one-stop solution covers the essential data science you need in Python. Data science is one of the fastest-growing disciplines in terms of academic research, student enrollment, and employment. Python, with its flexibility and scalability, is quickly overtaking the R language for data-scientific projects. Keep Python data-science concepts at your fingertips with this modular, quick reference to the tools used to acquire, clean, analyze, and store data. This one-stop solution covers essential Python, databases, network analysis, natural language processing, elements of machine learning, and visualization. Access structured and unstructured text and numeric data from local files, databases, and the Internet. Arrange, rearrange, and clean the data.Work with relational and non-relational databases, data visualization, and simple predictive analysis (regressions, clustering, and decision trees). See how typical data analysis problems are handled. And try your hand at your own solutions to a variety of medium-scale projects that are fun to work on and look good on your resume. Keep this handy quick guide at your side whether you're a student, an entry-level data science professional converting from R to Python, or a seasoned Python developer who doesn't want to memorize every function and option. What You Need: You need a decent distribution of Python 3.3 or above that includes at least NLTK, Pandas, NumPy, Matplotlib, Networkx, SciKit-Learn, and BeautifulSoup. A great distribution that meets the requirements is Anaconda, available for free from If you plan to set up your own database servers, you also need MySQL and MongoDB . Both packages are free and run on Windows, Linux, and Mac OS. Go from messy, unstructured artifacts stored in SQL and NoSQL databases to a neat, well-organized dataset with this quick reference for the busy data scientist. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Seller: Revaluation Books, Exeter, United Kingdom
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Add to basketPaperback. Condition: Brand New. 1st edition. 200 pages. 9.50x7.50x0.40 inches. In Stock.
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Add to basketCondition: New. Go from messy, unstructured artifacts stored in SQL and NoSQL databases to a neat, well-organized dataset with this quick reference for the busy data scientist.Über den AutorrnrnDmitry Zinoviev has an MS in Physics from Moscow S.
Published by Pragmatic Programmers Sep 2016, 2016
ISBN 10: 1680501844 ISBN 13: 9781680501841
Language: English
Seller: AHA-BUCH GmbH, Einbeck, Germany
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Add to basketTaschenbuch. Condition: Neu. Neuware - Go from messy, unstructured artifacts stored in SQL and NoSQL databases to a neat, well-organized dataset with this quick reference for the busy data scientist. Understand text mining, machine learning, and network analysis; process numeric data with the NumPy and Pandas modules; describe and analyze data using statistical and network-theoretical methods; and see actual examples of data analysis at work.
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Add to basketCondition: Gut. Zustand: Gut | Sprache: Englisch | Produktart: Bücher.
Seller: THE SAINT BOOKSTORE, Southport, United Kingdom
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Add to basketPaperback / softback. Condition: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 438.