R data science essentials :: learn the essence of data science and visualization using R in no time at all /
Learn the essence of data science and visualization using R in no time at allAbout This Book Become a pro at making stunning visualizations and dashboards quickly and without hassle For better decision making in business, apply the R programming language with the help of useful statistical technique...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham :
Packt Publishing,
2016.
|
| Schriftenreihe: | Community experience distilled.
|
| Schlagworte: | |
| Online-Zugang: | DE-862 DE-863 |
| Zusammenfassung: | Learn the essence of data science and visualization using R in no time at allAbout This Book Become a pro at making stunning visualizations and dashboards quickly and without hassle For better decision making in business, apply the R programming language with the help of useful statistical techniques. From seasoned authors comes a book that offers you a plethora of fast-paced techniques to detect and analyze data patternsWho This Book Is ForIf you are an aspiring data scientist or analyst who has a basic understanding of data science and has basic hands-on experience in R or any other analytics tool, then R Data Science Essentials is the book for you.What You Will Learn Perform data preprocessing and basic operations on data Implement visual and non-visual implementation data exploration techniques Mine patterns from data using affinity and sequential analysis Use different clustering algorithms and visualize them Implement logistic and linear regression and find out how to evaluate and improve the performance of an algorithm Extract patterns through visualization and build a forecasting algorithm Build a recommendation engine using different collaborative filtering algorithms Make a stunning visualization and dashboard using ggplot and R shinyIn DetailWith organizations increasingly embedding data science across their enterprise and with management becoming more data-driven it is an urgent requirement for analysts and managers to understand the key concept of data science. The data science concepts discussed in this book will help you make key decisions and solve the complex problems you will inevitably face in this new world.R Data Science Essentials will introduce you to various important concepts in the field of data science using R. We start by reading data from multiple sources, then move on to processing the data, extracting hidden patterns, building predictive and forecasting models, building a recommendation engine, and communicating to the user through stunning visualizations and dashboards.By the end of this book, you will have an understanding of some very important techniques in data science, be able to implement them using R, understand and interpret the outcomes, and know how they helps businesses make a decision.Style and approachThis easy-to-follow guide contains hands-on examples of the concepts of data science using R. |
| Beschreibung: | Includes index. |
| Beschreibung: | 1 online resource : illustrations. |
| ISBN: | 9781785286360 1785286366 1785286544 9781785286544 |
Internformat
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| 520 | 8 | |a Learn the essence of data science and visualization using R in no time at allAbout This Book Become a pro at making stunning visualizations and dashboards quickly and without hassle For better decision making in business, apply the R programming language with the help of useful statistical techniques. From seasoned authors comes a book that offers you a plethora of fast-paced techniques to detect and analyze data patternsWho This Book Is ForIf you are an aspiring data scientist or analyst who has a basic understanding of data science and has basic hands-on experience in R or any other analytics tool, then R Data Science Essentials is the book for you.What You Will Learn Perform data preprocessing and basic operations on data Implement visual and non-visual implementation data exploration techniques Mine patterns from data using affinity and sequential analysis Use different clustering algorithms and visualize them Implement logistic and linear regression and find out how to evaluate and improve the performance of an algorithm Extract patterns through visualization and build a forecasting algorithm Build a recommendation engine using different collaborative filtering algorithms Make a stunning visualization and dashboard using ggplot and R shinyIn DetailWith organizations increasingly embedding data science across their enterprise and with management becoming more data-driven it is an urgent requirement for analysts and managers to understand the key concept of data science. The data science concepts discussed in this book will help you make key decisions and solve the complex problems you will inevitably face in this new world.R Data Science Essentials will introduce you to various important concepts in the field of data science using R. We start by reading data from multiple sources, then move on to processing the data, extracting hidden patterns, building predictive and forecasting models, building a recommendation engine, and communicating to the user through stunning visualizations and dashboards.By the end of this book, you will have an understanding of some very important techniques in data science, be able to implement them using R, understand and interpret the outcomes, and know how they helps businesses make a decision.Style and approachThis easy-to-follow guide contains hands-on examples of the concepts of data science using R. | |
| 505 | 0 | |a Cover; Copyright; Credits; About the Authors; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Getting Started with R; Reading data from different sources; Reading data from a database; Data types in R; Variable data types; Data preprocessing techniques; Performing data operations; Arithmetic operations on the data; String operations on the data; Aggregation operations on the data; Mean; Median; Sum; Maximum and minimum; Standard deviation; Control structures in R; Control structures -- if and else; Control structures -- for; Control structures -- while | |
| 505 | 8 | |a Control structures -- repeat and breakControl structures -- next and return; Bringing data to a usable format; Summary; Chapter 2: Exploratory Data Analysis; The Titanic dataset; Descriptive statistics; Box plot; Exercise; Inferential statistics; Univariate analysis; Bivariate analysis; Multivariate analysis; Cross-tabulation analysis; Graphical analysis; Summary; Chapter 3: Pattern Discovery; Transactional datasets; Using the built-in dataset; Building the dataset; Apriori analysis; Support, confidence, and lift; Support; Confidence; Lift; Generating filtering rules; Plotting; Dataset; Rules | |
| 505 | 8 | |a Sequential datasetApriori sequence analysis; Understanding the results; Reference; Business cases; Summary; Chapter 4: Segmentation Using Clustering; Datasets; Reading and formatting the dataset in R; Centroid-based clustering and an ideal number of clusters; Implementation using K-means; Visualizing the clusters; Connectivity-based clustering; Visualizing the connectivity; Business use cases; Summary; Chapter 5: Developing Regression Models; Datasets; Sampling the dataset; Logistic regression; Evaluating logistic regression; Linear regression; Evaluating linear regression | |
| 505 | 8 | |a Methods to improve the accuracyEnsemble models; Replacing NA with mean or median; Removing the highly correlated values; Removing outliers; Summary; Chapter 6: Time Series Forecasting; Datasets; Extracting patterns; Forecasting using ARIMA; Forecasting using Holt-Winters; Methods to improve accuracy; Summary; Chapter 7: Recommendation Engine; Dataset and transformation; Recommendations using user-based CF; Recommendations using item-based CF; Challenges and enhancements; Summary; Chapter 8: Communicating Data Analysis; Dataset; Plotting using the googleVis package | |
| 505 | 8 | |a Creating an interactive dashboard using ShinySummary; Index | |
| 650 | 0 | |a R (Computer program language) |0 http://id.loc.gov/authorities/subjects/sh2002004407 | |
| 650 | 0 | |a Data mining. |0 http://id.loc.gov/authorities/subjects/sh97002073 | |
| 650 | 0 | |a Information visualization. |0 http://id.loc.gov/authorities/subjects/sh2002000243 | |
| 650 | 6 | |a R (Langage de programmation) | |
| 650 | 6 | |a Exploration de données (Informatique) | |
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| author | Koushik, Raja B. Ravindran, Sharan Kumar |
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| contents | Cover; Copyright; Credits; About the Authors; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Getting Started with R; Reading data from different sources; Reading data from a database; Data types in R; Variable data types; Data preprocessing techniques; Performing data operations; Arithmetic operations on the data; String operations on the data; Aggregation operations on the data; Mean; Median; Sum; Maximum and minimum; Standard deviation; Control structures in R; Control structures -- if and else; Control structures -- for; Control structures -- while Control structures -- repeat and breakControl structures -- next and return; Bringing data to a usable format; Summary; Chapter 2: Exploratory Data Analysis; The Titanic dataset; Descriptive statistics; Box plot; Exercise; Inferential statistics; Univariate analysis; Bivariate analysis; Multivariate analysis; Cross-tabulation analysis; Graphical analysis; Summary; Chapter 3: Pattern Discovery; Transactional datasets; Using the built-in dataset; Building the dataset; Apriori analysis; Support, confidence, and lift; Support; Confidence; Lift; Generating filtering rules; Plotting; Dataset; Rules Sequential datasetApriori sequence analysis; Understanding the results; Reference; Business cases; Summary; Chapter 4: Segmentation Using Clustering; Datasets; Reading and formatting the dataset in R; Centroid-based clustering and an ideal number of clusters; Implementation using K-means; Visualizing the clusters; Connectivity-based clustering; Visualizing the connectivity; Business use cases; Summary; Chapter 5: Developing Regression Models; Datasets; Sampling the dataset; Logistic regression; Evaluating logistic regression; Linear regression; Evaluating linear regression Methods to improve the accuracyEnsemble models; Replacing NA with mean or median; Removing the highly correlated values; Removing outliers; Summary; Chapter 6: Time Series Forecasting; Datasets; Extracting patterns; Forecasting using ARIMA; Forecasting using Holt-Winters; Methods to improve accuracy; Summary; Chapter 7: Recommendation Engine; Dataset and transformation; Recommendations using user-based CF; Recommendations using item-based CF; Challenges and enhancements; Summary; Chapter 8: Communicating Data Analysis; Dataset; Plotting using the googleVis package Creating an interactive dashboard using ShinySummary; Index |
| ctrlnum | (OCoLC)936182971 |
| dewey-full | 005.133 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 005 - Computer programming, programs, data, security |
| dewey-raw | 005.133 |
| dewey-search | 005.133 |
| dewey-sort | 15.133 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| format | Electronic eBook |
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| indexdate | 2025-04-11T08:42:59Z |
| institution | BVB |
| isbn | 9781785286360 1785286366 1785286544 9781785286544 |
| language | English |
| oclc_num | 936182971 |
| open_access_boolean | |
| owner | MAIN DE-862 DE-BY-FWS DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-862 DE-BY-FWS DE-863 DE-BY-FWS |
| physical | 1 online resource : illustrations. |
| psigel | ZDB-4-EBA FWS_PDA_EBA ZDB-4-EBA |
| publishDate | 2016 |
| publishDateSearch | 2016 |
| publishDateSort | 2016 |
| publisher | Packt Publishing, |
| record_format | marc |
| series | Community experience distilled. |
| series2 | Community experience distilled |
| spelling | Koushik, Raja B., author. R data science essentials : learn the essence of data science and visualization using R in no time at all / Raja B. Koushik, Sharan Kumar Ravindran. Birmingham : Packt Publishing, 2016. 1 online resource : illustrations. text txt rdacontent computer c rdamedia online resource cr rdacarrier Community experience distilled Online resource; title from PDF title page (EBSCO, viewed February 5, 2016) Includes index. Learn the essence of data science and visualization using R in no time at allAbout This Book Become a pro at making stunning visualizations and dashboards quickly and without hassle For better decision making in business, apply the R programming language with the help of useful statistical techniques. From seasoned authors comes a book that offers you a plethora of fast-paced techniques to detect and analyze data patternsWho This Book Is ForIf you are an aspiring data scientist or analyst who has a basic understanding of data science and has basic hands-on experience in R or any other analytics tool, then R Data Science Essentials is the book for you.What You Will Learn Perform data preprocessing and basic operations on data Implement visual and non-visual implementation data exploration techniques Mine patterns from data using affinity and sequential analysis Use different clustering algorithms and visualize them Implement logistic and linear regression and find out how to evaluate and improve the performance of an algorithm Extract patterns through visualization and build a forecasting algorithm Build a recommendation engine using different collaborative filtering algorithms Make a stunning visualization and dashboard using ggplot and R shinyIn DetailWith organizations increasingly embedding data science across their enterprise and with management becoming more data-driven it is an urgent requirement for analysts and managers to understand the key concept of data science. The data science concepts discussed in this book will help you make key decisions and solve the complex problems you will inevitably face in this new world.R Data Science Essentials will introduce you to various important concepts in the field of data science using R. We start by reading data from multiple sources, then move on to processing the data, extracting hidden patterns, building predictive and forecasting models, building a recommendation engine, and communicating to the user through stunning visualizations and dashboards.By the end of this book, you will have an understanding of some very important techniques in data science, be able to implement them using R, understand and interpret the outcomes, and know how they helps businesses make a decision.Style and approachThis easy-to-follow guide contains hands-on examples of the concepts of data science using R. Cover; Copyright; Credits; About the Authors; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Getting Started with R; Reading data from different sources; Reading data from a database; Data types in R; Variable data types; Data preprocessing techniques; Performing data operations; Arithmetic operations on the data; String operations on the data; Aggregation operations on the data; Mean; Median; Sum; Maximum and minimum; Standard deviation; Control structures in R; Control structures -- if and else; Control structures -- for; Control structures -- while Control structures -- repeat and breakControl structures -- next and return; Bringing data to a usable format; Summary; Chapter 2: Exploratory Data Analysis; The Titanic dataset; Descriptive statistics; Box plot; Exercise; Inferential statistics; Univariate analysis; Bivariate analysis; Multivariate analysis; Cross-tabulation analysis; Graphical analysis; Summary; Chapter 3: Pattern Discovery; Transactional datasets; Using the built-in dataset; Building the dataset; Apriori analysis; Support, confidence, and lift; Support; Confidence; Lift; Generating filtering rules; Plotting; Dataset; Rules Sequential datasetApriori sequence analysis; Understanding the results; Reference; Business cases; Summary; Chapter 4: Segmentation Using Clustering; Datasets; Reading and formatting the dataset in R; Centroid-based clustering and an ideal number of clusters; Implementation using K-means; Visualizing the clusters; Connectivity-based clustering; Visualizing the connectivity; Business use cases; Summary; Chapter 5: Developing Regression Models; Datasets; Sampling the dataset; Logistic regression; Evaluating logistic regression; Linear regression; Evaluating linear regression Methods to improve the accuracyEnsemble models; Replacing NA with mean or median; Removing the highly correlated values; Removing outliers; Summary; Chapter 6: Time Series Forecasting; Datasets; Extracting patterns; Forecasting using ARIMA; Forecasting using Holt-Winters; Methods to improve accuracy; Summary; Chapter 7: Recommendation Engine; Dataset and transformation; Recommendations using user-based CF; Recommendations using item-based CF; Challenges and enhancements; Summary; Chapter 8: Communicating Data Analysis; Dataset; Plotting using the googleVis package Creating an interactive dashboard using ShinySummary; Index R (Computer program language) http://id.loc.gov/authorities/subjects/sh2002004407 Data mining. http://id.loc.gov/authorities/subjects/sh97002073 Information visualization. http://id.loc.gov/authorities/subjects/sh2002000243 R (Langage de programmation) Exploration de données (Informatique) Visualisation de l'information. COMPUTERS / Programming Languages / General bisacsh Data mining fast Information visualization fast R (Computer program language) fast Ravindran, Sharan Kumar, author. has work: R Data Science Essentials (Text) https://id.oclc.org/worldcat/entity/E39PCYCttMBfBKCWgWxBqtJ99P https://id.oclc.org/worldcat/ontology/hasWork 1-78528-654-4 Community experience distilled. http://id.loc.gov/authorities/names/no2011030603 |
| spellingShingle | Koushik, Raja B. Ravindran, Sharan Kumar R data science essentials : learn the essence of data science and visualization using R in no time at all / Community experience distilled. Cover; Copyright; Credits; About the Authors; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Getting Started with R; Reading data from different sources; Reading data from a database; Data types in R; Variable data types; Data preprocessing techniques; Performing data operations; Arithmetic operations on the data; String operations on the data; Aggregation operations on the data; Mean; Median; Sum; Maximum and minimum; Standard deviation; Control structures in R; Control structures -- if and else; Control structures -- for; Control structures -- while Control structures -- repeat and breakControl structures -- next and return; Bringing data to a usable format; Summary; Chapter 2: Exploratory Data Analysis; The Titanic dataset; Descriptive statistics; Box plot; Exercise; Inferential statistics; Univariate analysis; Bivariate analysis; Multivariate analysis; Cross-tabulation analysis; Graphical analysis; Summary; Chapter 3: Pattern Discovery; Transactional datasets; Using the built-in dataset; Building the dataset; Apriori analysis; Support, confidence, and lift; Support; Confidence; Lift; Generating filtering rules; Plotting; Dataset; Rules Sequential datasetApriori sequence analysis; Understanding the results; Reference; Business cases; Summary; Chapter 4: Segmentation Using Clustering; Datasets; Reading and formatting the dataset in R; Centroid-based clustering and an ideal number of clusters; Implementation using K-means; Visualizing the clusters; Connectivity-based clustering; Visualizing the connectivity; Business use cases; Summary; Chapter 5: Developing Regression Models; Datasets; Sampling the dataset; Logistic regression; Evaluating logistic regression; Linear regression; Evaluating linear regression Methods to improve the accuracyEnsemble models; Replacing NA with mean or median; Removing the highly correlated values; Removing outliers; Summary; Chapter 6: Time Series Forecasting; Datasets; Extracting patterns; Forecasting using ARIMA; Forecasting using Holt-Winters; Methods to improve accuracy; Summary; Chapter 7: Recommendation Engine; Dataset and transformation; Recommendations using user-based CF; Recommendations using item-based CF; Challenges and enhancements; Summary; Chapter 8: Communicating Data Analysis; Dataset; Plotting using the googleVis package Creating an interactive dashboard using ShinySummary; Index R (Computer program language) http://id.loc.gov/authorities/subjects/sh2002004407 Data mining. http://id.loc.gov/authorities/subjects/sh97002073 Information visualization. http://id.loc.gov/authorities/subjects/sh2002000243 R (Langage de programmation) Exploration de données (Informatique) Visualisation de l'information. COMPUTERS / Programming Languages / General bisacsh Data mining fast Information visualization fast R (Computer program language) fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh2002004407 http://id.loc.gov/authorities/subjects/sh97002073 http://id.loc.gov/authorities/subjects/sh2002000243 |
| title | R data science essentials : learn the essence of data science and visualization using R in no time at all / |
| title_auth | R data science essentials : learn the essence of data science and visualization using R in no time at all / |
| title_exact_search | R data science essentials : learn the essence of data science and visualization using R in no time at all / |
| title_full | R data science essentials : learn the essence of data science and visualization using R in no time at all / Raja B. Koushik, Sharan Kumar Ravindran. |
| title_fullStr | R data science essentials : learn the essence of data science and visualization using R in no time at all / Raja B. Koushik, Sharan Kumar Ravindran. |
| title_full_unstemmed | R data science essentials : learn the essence of data science and visualization using R in no time at all / Raja B. Koushik, Sharan Kumar Ravindran. |
| title_short | R data science essentials : |
| title_sort | r data science essentials learn the essence of data science and visualization using r in no time at all |
| title_sub | learn the essence of data science and visualization using R in no time at all / |
| topic | R (Computer program language) http://id.loc.gov/authorities/subjects/sh2002004407 Data mining. http://id.loc.gov/authorities/subjects/sh97002073 Information visualization. http://id.loc.gov/authorities/subjects/sh2002000243 R (Langage de programmation) Exploration de données (Informatique) Visualisation de l'information. COMPUTERS / Programming Languages / General bisacsh Data mining fast Information visualization fast R (Computer program language) fast |
| topic_facet | R (Computer program language) Data mining. Information visualization. R (Langage de programmation) Exploration de données (Informatique) Visualisation de l'information. COMPUTERS / Programming Languages / General Data mining Information visualization |
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