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Jupyter Cookbook [[electronic resource] /] / Toomey, Dan
| Jupyter Cookbook [[electronic resource] /] / Toomey, Dan |
| Autore | Toomey Dan |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Packt Publishing, , 2018 |
| Descrizione fisica | 1 online resource (238 pages) |
| Soggetto genere / forma | Electronic books. |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910467759003321 |
Toomey Dan
|
||
| Packt Publishing, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Jupyter cookbook : over 75 recipes to perform interactive computing across Python, R, Scala, Spark, JavaScript, and more / / Dan Toomey
| Jupyter cookbook : over 75 recipes to perform interactive computing across Python, R, Scala, Spark, JavaScript, and more / / Dan Toomey |
| Autore | Toomey Dan |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Birmingham ; ; Mumbai : , : Packt, , [2018] |
| Descrizione fisica | 1 online resource (238 pages) |
| Disciplina | 005.434 |
| Soggetto topico | Command languages (Computer science) |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910794630103321 |
|
Toomey Dan
|
||
| Birmingham ; ; Mumbai : , : Packt, , [2018] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Jupyter cookbook : over 75 recipes to perform interactive computing across Python, R, Scala, Spark, JavaScript, and more / / Dan Toomey
| Jupyter cookbook : over 75 recipes to perform interactive computing across Python, R, Scala, Spark, JavaScript, and more / / Dan Toomey |
| Autore | Toomey Dan |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Birmingham ; ; Mumbai : , : Packt, , [2018] |
| Descrizione fisica | 1 online resource (238 pages) |
| Disciplina | 005.434 |
| Soggetto topico | Command languages (Computer science) |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910820826103321 |
|
Toomey Dan
|
||
| Birmingham ; ; Mumbai : , : Packt, , [2018] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Learning Jupyter : learn how to write code, mathematics, graphics, and output, all in a single document as well as in a web browser using Project Jupyter / / Dan Toomey
| Learning Jupyter : learn how to write code, mathematics, graphics, and output, all in a single document as well as in a web browser using Project Jupyter / / Dan Toomey |
| Autore | Toomey Dan |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Birmingham, England ; ; Mumbai, [India] : , : Packt Publishing, , 2016 |
| Descrizione fisica | 1 online resource (230 pages) : color illustrations |
| Disciplina | 502.85 |
| Soggetto topico |
Science - Data processing
Data mining Information visualization |
| ISBN | 1-78588-945-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | Learn how to write code, mathematics, graphics, and output, all in a single document as well as in a web browser using Project Jupyter |
| Record Nr. | UNINA-9910155043003321 |
|
Toomey Dan
|
||
| Birmingham, England ; ; Mumbai, [India] : , : Packt Publishing, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
R for data science : learn and explore the fundamentals of data science with R / / Dan Toomey
| R for data science : learn and explore the fundamentals of data science with R / / Dan Toomey |
| Autore | Toomey Dan |
| Pubbl/distr/stampa | Birmingham, England : , : Packt Publishing, , 2014 |
| Descrizione fisica | 1 online resource (364 p.) |
| Disciplina | 519.502855133 |
| Collana | Community Experience Distilled |
| Soggetto topico |
R (Computer program language)
Mathematical statistics - Data processing |
| Soggetto genere / forma | Electronic books. |
| ISBN | 1-78439-265-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Data Mining Patterns; Cluster analysis; K-means clustering; Usage; Example; K-medoids clustering; Usage; Example; Hierarchical clustering; Usage; Example; Expectation-maximization; Usage; List of model names; Example; Density estimation; Usage; Example; Anomaly detection; Show outliers; Example; Example; Another anomaly detection example; Calculating anomalies; Usage; Example 1; Example 2; Association rules; Mine for associations; Usage; Example; Questions; Summary
Chapter 2: Data Mining SequencesPatterns; Eclat; Usage; Using eclat to find similarities in adult behavior; Finding frequent items in a dataset; An example focusing on highest frequency; arulesNBMiner; Usage; Mining the Agrawal data for frequent sets; Apriori; Usage; Evaluating associations in a shopping basket; Determining sequences using TraMineR; Usage; Determining sequences in training and careers; Similarities in the sequence; Sequence metrics; Usage; Example; Questions; Summary; Chapter 3: Text Mining; Packages; Text processing; Example; Creating a corpus; Text clusters; Word graphics Analyzing the XML textQuestions; Summary; Chapter 4: Data Analysis - Regression Analysis; Packages; Simple regression; Multiple regression; Multivariate regression analysis; Robust regression; Questions; Summary; Chapter 5: Data Analysis - Correlation; Packages; Correlation; Example; Visualizing correlations; Covariance; Pearson correlation; Polychoric correlation; Tetrachoric correlation; A heterogeneous correlation matrix; Partial correlation; Questions; Summary; Chapter 6: Data Analysis - Clustering; Packages; K-means clustering; Example; Optimal number of clusters; Medoids clusters The cascadeKM functionSelecting clusters based on Bayesian information; Affinity propagation clustering; Gap statistic to estimate the number of clusters; Hierarchical clustering; Questions; Summary; Chapter 7: Data Visualization - R Graphics; Packages; Interactive graphics; The latticist package; Bivariate binning display; Mapping; Plotting points on a map; Plotting points on a world map; Google Maps; The ggplot2 package; Questions; Summary; Chapter 8: Data Visualization - Plotting; Packages; Scatter plots; Regression line; A lowess line; scatterplot; Scatterplot matrices splom - display matrix datacpairs - plot matrix data; Density scatter plots; Bar charts and plots; Bar plot; Usage; Bar chart; ggplot2; Word cloud; Questions; Summary; Chapter 9: Data Visualization - 3D; Packages; Generating 3D graphics; Lattice Cloud - 3D scatterplot; scatterplot3d; scatter3d; cloud3d; RgoogleMaps; vrmlgenbar3D; Big Data; pbdR; bigmemory; Research areas; Rcpp; parallel; microbenchmark; pqR; SAP integration; roxygen2; bioconductor; swirl; pipes; Questions; Summary; Chapter 10: Machine Learning in Action; Packages; Dataset; Data partitioning; Model; Linear model; Prediction Logistic regression |
| Record Nr. | UNINA-9910464122103321 |
|
Toomey Dan
|
||
| Birmingham, England : , : Packt Publishing, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
R for data science : learn and explore the fundamentals of data science with R / / Dan Toomey
| R for data science : learn and explore the fundamentals of data science with R / / Dan Toomey |
| Autore | Toomey Dan |
| Pubbl/distr/stampa | Birmingham, England : , : Packt Publishing, , 2014 |
| Descrizione fisica | 1 online resource (364 p.) |
| Disciplina | 519.502855133 |
| Collana | Community Experience Distilled |
| Soggetto topico |
R (Computer program language)
Mathematical statistics - Data processing |
| ISBN | 1-78439-265-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Data Mining Patterns; Cluster analysis; K-means clustering; Usage; Example; K-medoids clustering; Usage; Example; Hierarchical clustering; Usage; Example; Expectation-maximization; Usage; List of model names; Example; Density estimation; Usage; Example; Anomaly detection; Show outliers; Example; Example; Another anomaly detection example; Calculating anomalies; Usage; Example 1; Example 2; Association rules; Mine for associations; Usage; Example; Questions; Summary
Chapter 2: Data Mining SequencesPatterns; Eclat; Usage; Using eclat to find similarities in adult behavior; Finding frequent items in a dataset; An example focusing on highest frequency; arulesNBMiner; Usage; Mining the Agrawal data for frequent sets; Apriori; Usage; Evaluating associations in a shopping basket; Determining sequences using TraMineR; Usage; Determining sequences in training and careers; Similarities in the sequence; Sequence metrics; Usage; Example; Questions; Summary; Chapter 3: Text Mining; Packages; Text processing; Example; Creating a corpus; Text clusters; Word graphics Analyzing the XML textQuestions; Summary; Chapter 4: Data Analysis - Regression Analysis; Packages; Simple regression; Multiple regression; Multivariate regression analysis; Robust regression; Questions; Summary; Chapter 5: Data Analysis - Correlation; Packages; Correlation; Example; Visualizing correlations; Covariance; Pearson correlation; Polychoric correlation; Tetrachoric correlation; A heterogeneous correlation matrix; Partial correlation; Questions; Summary; Chapter 6: Data Analysis - Clustering; Packages; K-means clustering; Example; Optimal number of clusters; Medoids clusters The cascadeKM functionSelecting clusters based on Bayesian information; Affinity propagation clustering; Gap statistic to estimate the number of clusters; Hierarchical clustering; Questions; Summary; Chapter 7: Data Visualization - R Graphics; Packages; Interactive graphics; The latticist package; Bivariate binning display; Mapping; Plotting points on a map; Plotting points on a world map; Google Maps; The ggplot2 package; Questions; Summary; Chapter 8: Data Visualization - Plotting; Packages; Scatter plots; Regression line; A lowess line; scatterplot; Scatterplot matrices splom - display matrix datacpairs - plot matrix data; Density scatter plots; Bar charts and plots; Bar plot; Usage; Bar chart; ggplot2; Word cloud; Questions; Summary; Chapter 9: Data Visualization - 3D; Packages; Generating 3D graphics; Lattice Cloud - 3D scatterplot; scatterplot3d; scatter3d; cloud3d; RgoogleMaps; vrmlgenbar3D; Big Data; pbdR; bigmemory; Research areas; Rcpp; parallel; microbenchmark; pqR; SAP integration; roxygen2; bioconductor; swirl; pipes; Questions; Summary; Chapter 10: Machine Learning in Action; Packages; Dataset; Data partitioning; Model; Linear model; Prediction Logistic regression |
| Record Nr. | UNINA-9910788049803321 |
|
Toomey Dan
|
||
| Birmingham, England : , : Packt Publishing, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
R for data science : learn and explore the fundamentals of data science with R / / Dan Toomey
| R for data science : learn and explore the fundamentals of data science with R / / Dan Toomey |
| Autore | Toomey Dan |
| Pubbl/distr/stampa | Birmingham, England : , : Packt Publishing, , 2014 |
| Descrizione fisica | 1 online resource (364 p.) |
| Disciplina | 519.502855133 |
| Collana | Community Experience Distilled |
| Soggetto topico |
R (Computer program language)
Mathematical statistics - Data processing |
| ISBN | 1-78439-265-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Data Mining Patterns; Cluster analysis; K-means clustering; Usage; Example; K-medoids clustering; Usage; Example; Hierarchical clustering; Usage; Example; Expectation-maximization; Usage; List of model names; Example; Density estimation; Usage; Example; Anomaly detection; Show outliers; Example; Example; Another anomaly detection example; Calculating anomalies; Usage; Example 1; Example 2; Association rules; Mine for associations; Usage; Example; Questions; Summary
Chapter 2: Data Mining SequencesPatterns; Eclat; Usage; Using eclat to find similarities in adult behavior; Finding frequent items in a dataset; An example focusing on highest frequency; arulesNBMiner; Usage; Mining the Agrawal data for frequent sets; Apriori; Usage; Evaluating associations in a shopping basket; Determining sequences using TraMineR; Usage; Determining sequences in training and careers; Similarities in the sequence; Sequence metrics; Usage; Example; Questions; Summary; Chapter 3: Text Mining; Packages; Text processing; Example; Creating a corpus; Text clusters; Word graphics Analyzing the XML textQuestions; Summary; Chapter 4: Data Analysis - Regression Analysis; Packages; Simple regression; Multiple regression; Multivariate regression analysis; Robust regression; Questions; Summary; Chapter 5: Data Analysis - Correlation; Packages; Correlation; Example; Visualizing correlations; Covariance; Pearson correlation; Polychoric correlation; Tetrachoric correlation; A heterogeneous correlation matrix; Partial correlation; Questions; Summary; Chapter 6: Data Analysis - Clustering; Packages; K-means clustering; Example; Optimal number of clusters; Medoids clusters The cascadeKM functionSelecting clusters based on Bayesian information; Affinity propagation clustering; Gap statistic to estimate the number of clusters; Hierarchical clustering; Questions; Summary; Chapter 7: Data Visualization - R Graphics; Packages; Interactive graphics; The latticist package; Bivariate binning display; Mapping; Plotting points on a map; Plotting points on a world map; Google Maps; The ggplot2 package; Questions; Summary; Chapter 8: Data Visualization - Plotting; Packages; Scatter plots; Regression line; A lowess line; scatterplot; Scatterplot matrices splom - display matrix datacpairs - plot matrix data; Density scatter plots; Bar charts and plots; Bar plot; Usage; Bar chart; ggplot2; Word cloud; Questions; Summary; Chapter 9: Data Visualization - 3D; Packages; Generating 3D graphics; Lattice Cloud - 3D scatterplot; scatterplot3d; scatter3d; cloud3d; RgoogleMaps; vrmlgenbar3D; Big Data; pbdR; bigmemory; Research areas; Rcpp; parallel; microbenchmark; pqR; SAP integration; roxygen2; bioconductor; swirl; pipes; Questions; Summary; Chapter 10: Machine Learning in Action; Packages; Dataset; Data partitioning; Model; Linear model; Prediction Logistic regression |
| Record Nr. | UNINA-9910819389303321 |
|
Toomey Dan
|
||
| Birmingham, England : , : Packt Publishing, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||