LEADER 00000cam a2200673 i 4500 
003    OCoLC 
005    20240129213017.0 
006    m     o  d         
007    cr unu|||||||| 
008    190516s2019    enka    ob    000 0 eng d 
019    1091587914 
020    9781789952100|q(electronic bk.) 
020    1789952107|q(electronic bk.) 
029 1  AU@|b000071304092 
035    (OCoLC)1101443885|z(OCoLC)1091587914 
037    CL0501000048|bSafari Books Online 
037    AFD71241-5953-4EC2-A110-849A9373AF22|bOverDrive, Inc.
       |nhttp://www.overdrive.com 
040    UMI|beng|erda|epn|cUMI|dTEFOD|dCEF|dN$T|dOCLCF|dC6I|dYDX
       |dOCLCQ|dOCLCO|dKSU|dOCLCQ|dOCLCO 
049    INap 
082 04 658.830285 
082 04 658.830285|223 
099    eBook O'Reilly for Public Libraries 
100 1  Blanchard, Tommy,|eauthor. 
245 10 Data science for marketing analytics /|cTommy Blanchard, 
       Debasish Behera, Pranshu Bhatnagar.|h[O'Reilly electronic 
       resource] 
264  1 Birmingham, UK :|bPackt Publishing,|c2019. 
300    1 online resource (1 volume) :|billustrations 
336    text|btxt|2rdacontent 
337    computer|bc|2rdamedia 
338    online resource|bcr|2rdacarrier 
504    Includes bibliographical references. 
520    Explore new and more sophisticated tools that reduce your 
       marketing analytics efforts and give you precise results 
       Key Features Study new techniques for marketing analytics 
       Explore uses of machine learning to power your marketing 
       analyses Work through each stage of data analytics with 
       the help of multiple examples and exercises Book 
       Description Data Science for Marketing Analytics covers 
       every stage of data analytics, from working with a raw 
       dataset to segmenting a population and modeling different 
       parts of the population based on the segments. The book 
       starts by teaching you how to use Python libraries, such 
       as pandas and Matplotlib, to read data from Python, 
       manipulate it, and create plots, using both categorical 
       and continuous variables. Then, you'll learn how to 
       segment a population into groups and use different 
       clustering techniques to evaluate customer segmentation. 
       As you make your way through the chapters, you'll explore 
       ways to evaluate and select the best segmentation approach,
       and go on to create a linear regression model on customer 
       value data to predict lifetime value. In the concluding 
       chapters, you'll gain an understanding of regression 
       techniques and tools for evaluating regression models, and
       explore ways to predict customer choice using 
       classification algorithms. Finally, you'll apply these 
       techniques to create a churn model for modeling customer 
       product choices. By the end of this book, you will be able
       to build your own marketing reporting and interactive 
       dashboard solutions. What you will learn Analyze and 
       visualize data in Python using pandas and Matplotlib Study
       clustering techniques, such as hierarchical and k-means 
       clustering Create customer segments based on manipulated 
       data Predict customer lifetime value using linear 
       regression Use classification algorithms to understand 
       customer choice Optimize classification algorithms to 
       extract maximal information Who this book is for Data 
       Science for Marketing Analytics is designed for developers
       and marketing analysts looking to use new, more 
       sophisticated tools in their marketing analytics efforts. 
       It'll help if you have prior experience of coding in 
       Python and knowledge of high school level mathematics. 
       Some experience with databases, Excel, statistics, or 
       Tableau is useful but not necessary. Downloading the 
       example code for this book You can download the example 
       code files for all Packt books you have purchased from 
       your account at http://www.PacktPub.com. If you purchased 
       ... 
588 0  Online resource; title from copyright page (Safari, viewed
       May 15, 2019). 
590    O'Reilly|bO'Reilly Online Learning: Academic/Public 
       Library Edition 
650  0 Marketing|xData processing. 
650  0 Marketing research. 
650  0 Python (Computer program language) 
650  0 Information visualization. 
650  6 Marketing|xInformatique. 
650  6 Marketing|xRecherche. 
650  6 Python (Langage de programmation) 
650  6 Visualisation de l'information. 
650  7 Information visualization|2fast 
650  7 Marketing|xData processing|2fast 
650  7 Marketing research|2fast 
650  7 Python (Computer program language)|2fast 
700 1  Bhatnagar, Pranshu,|eauthor. 
700 1  Behera, Debasish,|eauthor. 
856 40 |uhttps://ezproxy.naperville-lib.org/login?url=https://
       learning.oreilly.com/library/view/~/9781789959413/?ar
       |zAvailable on O'Reilly for Public Libraries 
938    EBSCOhost|bEBSC|n2094757 
938    YBP Library Services|bYANK|n16141424 
994    92|bJFN