MARC Record from marc_columbia

LEADER: 06187cam a2200493Mu 4500
001 13674588
005 20210302173427.0
006 m o d
007 cr |n|---|||||
008 171104s2017 enk o 000 0 eng d
035 $a(OCoLC)on1009240867
035 $a(NNC)13674588
040 $aEBLCP$beng$epn$cEBLCP$dIDEBK$dMERUC$dIDB$dOCLCQ$dOCLCO$dYDX$dOCLCF$dN$T$dOCLCQ$dLVT$dZCU$dOCLCQ
019 $a1007923378$a1079798759
020 $a9781785883293$q(electronic bk.)
020 $a1785883291$q(electronic bk.)
035 $a(OCoLC)1009240867$z(OCoLC)1007923378$z(OCoLC)1079798759
050 4 $aT58.6
050 4 $aT55.4-60.8
072 7 $aCOM$x039000$2bisacsh
082 04 $a005.1$223
049 $aZCUA
100 1 $aToomey, Dan.
245 10 $aJupyter for Data Science.
260 $aBirmingham :$bPackt Publishing,$c2017.
300 $a1 online resource (236 pages)
336 $atext$btxt$2rdacontent
337 $acomputer$bc$2rdamedia
338 $aonline resource$bcr$2rdacarrier
588 0 $aPrint version record.
505 0 $aCover -- Copyright -- Credits -- About the Author -- About the Reviewers -- www.PacktPub.com -- Customer Feedback -- Table of Contents -- Preface -- Chapter 1: Jupyter and Data Science -- Jupyter concepts -- A first look at the Jupyter user interface -- Detailing the Jupyter tabs -- What actions can I perform with Jupyter? -- What objects can Jupyter manipulate? -- Viewing the Jupyter project display -- File menu -- Edit menu -- View menu -- Insert menu -- Cell menu -- Kernel menu -- Help menu -- Icon toolbar
505 8 $aHow does it look when we execute scripts?Industry data science usage -- Real life examples -- Finance, Python -- European call option valuation -- Finance, Python -- Monte Carlo pricing -- Gambling, R -- betting analysis -- Insurance, R -- non-life insurance pricing -- Consumer products, R -- marketing effectiveness -- Using Docker with Jupyter -- Using a public Docker service -- Installing Docker on your machine -- How to share notebooks with others -- Can you email a notebook? -- Sharing a notebook on Google Drive -- Sharing on GitHub
505 8 $aStore as HTML on a web serverInstall Jupyter on a web server -- How can you secure a notebook? -- Access control -- Malicious content -- Summary -- Chapter 2: Working with Analytical Data on Jupyter -- Data scraping with a Python notebook -- Using heavy-duty data processing functions in Jupyter -- Using NumPy functions in Jupyter -- Using pandas in Jupyter -- Use pandas to read text files in Jupyter -- Use pandas to read Excel files in Jupyter -- Using pandas to work with data frames -- Using the groupby function in a data frame
505 8 $aManipulating columns in a data frameCalculating outliers in a data frame -- Using SciPy in Jupyter -- Using SciPy integration in Jupyter -- Using SciPy optimization in Jupyter -- Using SciPy interpolation in Jupyter -- Using SciPy Fourier Transforms in Jupyter -- Using SciPy linear algebra in Jupyter -- Expanding on panda data frames in Jupyter -- Sorting and filtering data frames in Jupyter/IPython -- Filtering a data frame -- Sorting a data frame -- Summary -- Chapter 3: Data Visualization and Prediction -- Make a prediction using scikit-learn
505 8 $aMake a prediction using RInteractive visualization -- Plotting using Plotly -- Creating a human density map -- Draw a histogram of social data -- Plotting 3D data -- Summary -- Chapter 4: Data Mining and SQL Queries -- Special note for Windows installation -- Using Spark to analyze data -- Another MapReduce example -- Using SparkSession and SQL -- Combining datasets -- Loading JSON into Spark -- Using Spark pivot -- Summary -- Chapter 5: R with Jupyter -- How to set up R for Jupyter -- R data analysis of the 2016 US election demographics
500 $a""Analyzing 2016 voter registration and voting""
520 $aData -- Review spread -- Finding the top rated firms -- Finding the most rated firms -- Finding all ratings for a top rated firm -- Determining the correlation between ratings and number of reviews -- Building a model of reviews -- Using Python to compare ratings -- Visualizing average ratings by cuisine -- Arbitrary search of ratings -- Determining relationships between number of ratings and ratings -- Summary -- Chapter 9: Machine Learning Using Jupyter -- Naive Bayes -- Naive Bayes using R -- Naive Bayes using Python -- Nearest neighbor estimator -- Nearest neighbor using R -- Nearest neighbor using Python -- Decision trees -- Decision trees in R -- Decision trees in Python -- Neural networks -- Neural networks in R -- Random forests -- Random forests in R -- Summary -- Chapter 10: Optimizing Jupyter Notebooks -- Deploying notebooks -- Deploying to JupyterHub -- Installing JupyterHub -- Accessing a JupyterHub Installation -- Jupyter hosting -- Optimizing your script -- Optimizing your Python scripts -- Determining how long a script takes -- Using Python regular expressions -- Using Python string handling -- Minimizing loop operations -- Profiling your script -- Optimizing your R scripts -- Using microbenchmark to profile R script -- Modifying provided functionality -- Optimizing name lookup -- Optimizing data frame value extraction -- Changing R Implementation -- Changing algorithms -- Monitoring Jupyter -- Caching your notebook -- Securing a notebook -- Managing notebook authorization -- Securing notebook content -- Scaling Jupyter Notebooks -- Sharing Jupyter Notebooks -- Sharing Jupyter Notebook on a notebook server -- Sharing encrypted Jupyter Notebook on a notebook server -- Sharing notebook on a web server -- Sharing notebook on Docker -- Converting a notebook -- Versioning a notebook -- Summary -- Index.
650 0 $aManagement information systems.
650 7 $aCOMPUTERS$xManagement Information Systems.$2bisacsh
650 7 $aManagement information systems.$2fast$0(OCoLC)fst01007271
655 4 $aElectronic books.
776 08 $iPrint version:$aToomey, Dan.$tJupyter for Data Science.$dBirmingham : Packt Publishing, ©2017
856 40 $uhttp://www.columbia.edu/cgi-bin/cul/resolve?clio13674588$zAll EBSCO eBooks
852 8 $blweb$hEBOOKS