03405nam a22004455i 4500001001800000003000900018005001700027007001500044008004100059020001800100024003500118050000900153072001500162072002300177072001400200100008200214245009600296250001800392264006900410300006500479336002600544337002600570338003600596347002400632505028000656520155400936650004602490650004002536650002902576650001802605650001202623650002902635700008102664710003402745773002602779776003602805776003602841776003602877856004602913978-3-031-52473-8DE-He21320251030111824.0cr nn 008mamaa240418s2024    sz |    s    |||| 0|eng d  a97830315247387 a10.1007/978-3-031-52473-82doi 4aQ336 7aUN2bicssc 7aCOM0210002bisacsh 7aUN2thema1 aMuddana, A. Lakshmi.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut10aPython for Data Scienceh[electronic resource] /cby A. Lakshmi Muddana, Sandhya Vinayakam.  a1st ed. 2024. 1aCham :bSpringer Nature Switzerland :bImprint: Springer,c2024.  aXVII, 392 p. 95 illus., 3 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda0 aRepresentation of Discrete Signals and Systems -- The z-transform Analysis of Discrete Time Systems -- Discrete Fourier Transform and Computation -- Design of IIR Digital Filters -- Design of Finite Impulse Response (FIR) Digital Filters -- Digital Signal Processor -- Index.  aThe book is designed to serve as a textbook for courses offered to undergraduate and graduate students enrolled in data science. This book aims to help the readers understand the basic and advanced concepts for developing simple programs and the fundamentals required for building machine learning models. The book covers basic concepts like data types, operators, and statements that enable the reader to solve simple problems. As functions are the core of any programming, a detailed illustration of defining & invoking functions and recursive functions is covered. Built-in data structures of Python, such as strings, lists, tuples, sets, and dictionary structures, are discussed in detail with examples and exercise problems. Files are an integrated part of programming when dealing with large data. File handling operations are illustrated with examples and a case study at the end of the chapter. Widely used Python packages for data science, such as Pandas, Data Visualization libraries, and regular expressions, are discussed with examples and case studies at the end of the chapters. The book also contains a chapter on SQLite3, a small relational database management system of Python, to understand how to create and manage databases. As AI applications are becoming popular for developing intelligent solutions to various problems, the book includes chapters on Machine Learning and Deep Learning. They cover the basic concepts, example applications, and case studies using popular frameworks such as SKLearn and Keras on public datasets. 0aArtificial intelligencexData processing. 0aPython (Computer program language). 0aArtificial intelligence.14aData Science.24aPython.24aArtificial Intelligence.1 aVinayakam, Sandhya.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut2 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303152472108iPrinted edition:z978303152474508iPrinted edition:z978303152475240uhttps://doi.org/10.1007/978-3-031-52473-8