000			02018cam a22002538i 4500
005			20251224142607.0
008			241022t20252025nju obf 001 0 eng c
020			_a9781394234509
020			_a9781394234516
041			_aeng
082	0	0	_a005.74 _bCadD
100	1		_aCady, Field
245	1	4	_aThe data science handbook / _cField Cady
250			_a2nd ed.
300			_a1 online resource
505	0		_aIntroduction -- The Data Science Road Map -- Programming Languages -- Data Munging: String Manipulation, Regular Expressions, and Data Cleaning -- Visualizations and Simple Metrics -- AI and Machine Learning Overview -- Interlude: Feature Extraction Ideas -- Machine Learning Classification -- Technical Communication and Documentation -- Unsupervised Learning: Clustering and Dimensionality Reduction -- Regression -- Data Encodings and File Formats -- Big Data -- Databases -- Software Engineering Best Practices -- Traditional Natural Language Processing -- Time Series Analysis -- Probability -- Statistics -- Programming Language Concepts -- Performance and Computer Memory -- Computer Memory and Data Structures -- Maximum Likelihood Estimation and Optimization -- Deep Learning and AI -- Stochastic Modeling -- Parting Words: Your Future as a Data Scientist.
520			_a"The goal of this book is to turn you into a data scientist, and there are two parts to this mission. Firstly there is a set of specific concepts, tools and techniques that you can go out and solve problems with today. They include buzzwords such machine learning, Spark and NLP. They also include concepts that are distinctly less sexy but often more useful, like regular expressions, unit tests and SQL queries. It would be impossible to give an exhaustive list in any single book, but I cast a wide net"--
650		0	_aDatabases
650		0	_aStatistics _xData processing
650		0	_aBig data
650		0	_aInformation theory
856			_uhttps://ieeexplore.ieee.org/servlet/opac?bknumber=10766879
942			_cEBK
999			_c7888 _d7888