Sharan Kumar Ravindran - R Data Science Essentials
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- Book:R Data Science Essentials
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Learn the essence of data science and visualization using R in no time at all
About 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 patterns
Who This Book Is For
If 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 shiny
In Detail
With 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 approach
This easy-to-follow guide contains hands-on examples of the concepts of data science using R.
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 this book elsewhere, you can visit http://www.PacktPub.com/support and register to have the code file.
Sharan Kumar Ravindran: author's other books
Who wrote R Data Science Essentials? Find out the surname, the name of the author of the book and a list of all author's works by series.
![Thomas Mailund [Thomas Mailund] - Beginning Data Science in R: Data Analysis, Visualization, and Modelling for the Data Scientist](/uploads/posts/book/119629/thumbs/thomas-mailund-thomas-mailund-beginning-data.jpg)
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- aggregation operations
- about /
- mean /
- median /
- sum /
- minimum /
- maximum /
- standard deviation /
- Apriori analysis
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- Apriori sequence analysis
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- reference link /
- business cases /
- arithmetic operations
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- association rule analysis, parameters
- support /
- confidence /
- lift /
- autoregressive integrated moving average (ARIMA)
- used, for forecasting /
- order parameter /
- order parameter, URL /
- bivariate analysis
- about /
- box plot
- plotting, for descriptive statistics /
- break control structure
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- Cassandra
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- centroid-based clustering
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- clustering
- datasets, using /
- datasets, reading /
- datasets, formatting /
- business use cases /
- clusters
- ideal number, obtaining /
- implementing, K-means algorithm used /
- visualizing /
- comma-separated values (CSV) format
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- connectivity-based clustering
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- visualizing /
- control structures
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- if and else /
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- cross tabulation analysis
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- data
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- reading, from database /
- preparing, for analysis /
- dataframe
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- data operations
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- arithmetic operations /
- string operations /
- aggregation operations /
- data preprocessing
- techniques /
- dataset
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- data types
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- vector /
- matrix /
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- factors /
- dataframe /
- data visualization
- dataset, using /
- plotting, googleVis package used /
- interactive dashboard, creating with Shiny /
- DBI driver
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- descriptive statistics
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- box plot /
- ensemble models
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- NA values, replacing with mean or median /
- highly correlated values, removing /
- outliers, removing /
- factors
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- forecasting
- datasets, using /
- extracting patterns /
- autoregressive integrated moving average (ARIMA), using /
- Holt-Winters, using /
- accuracy, improving /
- for loop
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- googleVis package
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- graphical analysis
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- Hadoop
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- Holt-Winters
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- if and else control structure
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- in-built dataset
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- inferential statistics
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- interactive dashboard
- creating, Shiny used /
- item-based CF method
- used, for implementing recommendation system /
- JDBC driver
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- K-means algorithm
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- used, for cluster implementation /
- linear regression
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- evaluating /
- list
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- logistic regression
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- evaluating /
- matrix
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- MongoDB
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- multivariate analysis
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- cross tabulation analysis /
- graphical analysis /
- next control structure
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- Oracle
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- PostgreSQL
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- public dataset
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- Random Forest
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- recommendation system
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- implementing, item-based CF method used /
- challenges /
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- regression models
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- dataset, sampling /
- logistic regression /
- linear regression /
- accuracy, improving /
- ensemble models, building /
- Support Vector Machine (SVM) /
- Random Forest /
- repeat control structure
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- return control structure
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- rules
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- plotting /
- results, checking /
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- SAS
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- Seasonal Decomposition of Time series
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- sensitivity and specificity
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- sequential dataset
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- Shiny
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- SPSS
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- SQL Server
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- Stata
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- stl function
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- string operations
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- Support Vector Machine (SVM)
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- Systat
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- Titanic dataset
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- transactional datasets
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- univariate analysis
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- user-based CF method
- used, for implementing recommendation system /
- variable data types
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- vector
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- while loop
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R is one of the most popular programming languages used in computation statistics, data visualization, and data science. With the increasing number of companies becoming data-driven, the user base of R is also increasing fast. R is supported by over two million users worldwide.
In this book, you will learn how to use R to load data from different sources, carry out fundamental data manipulation techniques, extract the hidden patterns in data through exploratory data analysis, and build complex predictive as well as forecasting models. Finally, you will learn to visualize and communicate the data analysis to the audience. This book is aimed at beginners and intermediate users of R, taking them through the most important techniques in data science that will help them start their data scientist journey.
In this chapter, we will be covering the basic concepts of R such as reading data from different sources, understanding the data format, learning about the preprocessing techniques, and performing basic arithmetic and string operations.
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