NUST Institutions Library Catalogue

Data science from scratch : first principles with Python / Joel Grus.

By: Grus, Joel (Software engineer) [author.]Material type: TextTextPublisher: Sebastopol, CA : O'Reilly Media, [2019]Edition: Second editionDescription: xvii, 384 pages : illustrations ; 24 cmContent type: text Media type: unmediated Carrier type: volumeISBN: 9781492041139; 1492041130Subject(s): Python (Computer program language) | Database management | Data structures (Computer science) | Data mining | Data mining -- Mathematics | Data mining | Data structures (Computer science) | Database management | Python (Computer program language) | E-BOOKBANK.SEECSTEXTBOOKDDC classification: 005.7565 LOC classification: QA76.73.P98 | G78 2019Online resources: Click here to access online
Contents:
Introduction -- A crash course in Python -- Visualizing data -- Linear algebra -- Statistics -- Probability -- Hypothesis and inference -- Gradient descent -- Getting data -- Working with data -- Machine learning -- k-Nearest neighbors -- Naive bayes -- Simple linear regression -- Multiple regression -- Logistic regression -- Decision trees -- Neural networks -- Deep learning -- Clustering -- Natural language processing -- Network analysis -- Recommender systems -- Databases and SQL -- MapReduce -- Data ethics -- Go forth and do data science.
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Item type Current location Home library Call number Status Date due Barcode Item holds
Book Book Central Library (CL)
Central Library (CL)
280 SCB (Browse shelf) Available SCB-1568
Book Book Central Library (CL)
Central Library (CL)
005.7565 GRU (Browse shelf) Available CL-1568
Total holds: 0

Includes bibliographical references and index.

Introduction -- A crash course in Python -- Visualizing data -- Linear algebra -- Statistics -- Probability -- Hypothesis and inference -- Gradient descent -- Getting data -- Working with data -- Machine learning -- k-Nearest neighbors -- Naive bayes -- Simple linear regression -- Multiple regression -- Logistic regression -- Decision trees -- Neural networks -- Deep learning -- Clustering -- Natural language processing -- Network analysis -- Recommender systems -- Databases and SQL -- MapReduce -- Data ethics -- Go forth and do data science.

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