Beamsync is providing analytics courses in Bangalore. You will get training for Data Analytics, Data Scientist, Business Ananalytics along with R tool.
For upcoming schedules visit: http://beamsync.com/business-analytics-training-bangalore/
EXTRA is an open source rules based classification engine, developed by IPTC supported by a grant from Google DNI. Why are rules better than machine learning for breaking news? How can automation better support the manual crafting of news rules.
The Use of Big Data Techniques for Digital ArchivingSven Schlarb
These slides were used in a presentation at the "Our Digital Future - Multidisciplinary Perspectives on Long Term Data Preservation and Access" conference in Cambridge/UK in March 2016 in the session "Current and Future perspectives on technology for data preservation and sharing". They describe work in progress in the E-ARK project, which is co-funded by the European Commission and has as its main objective the creation of a scalable open source, digital archiving system offering efficent search and access content of very large digital object collections. The focus of this presentation lies on describing the core big data technologies (Apache Hadoop, Apache Hbase, and the document repository Lily developed by NGData), the architecture of the E-ARK integrated prototype implementation, and data mining use cases related to geographical data, named entitity extraction, and OLAP data analysis.
An update on the EXTRA project - an open source rules based classifier for news content. Including the application for additional funding from Google DNI for FRANCIS
SCAPE Information Day at BL - Characterising content in web archives with NaniteSCAPE Project
This presentation was given by Will Palmer at ‘SCAPE Information Day at the British Library’, on 14 July 2014. The information day introduced the EU-funded project SCAPE (Scalable Preservation Environments) and its tools and services to the participants.
In this presentation Will Palmer introduced the SCAPE developed tool Nanite which can help institutions analyze their web archive data.
A Framework for Multi-source Studies based on Unstructured Data.Sebastiano Panichella
Mathias Birrer, Pooja Ruhal, Sebastiano Panichella, and Oscar Niestrasz: Makar: A Framework for Multi-source Studies based on Unstructured Data. International Conference on Software Analysis, Evolution and Reengineering (SANER 2021).
The Mint is an authority control / vocabulary server designed to supply authority services to repositories. It is designed to be a practical tool for working towards Linked Data repositories, making it easy build high-quality metadata collection and discovery system.
Visualising Research Graph using Neo4j and Gephiamiraryani
This presentation discusses visualizing research graphs using Neo4j and Gephi. It introduces Research Graph, which connects research datasets, ORCID profiles, and grants to enable cross-platform discovery. The presentation demonstrates loading DSpace data into Neo4j, exploring the graph, and filtering the graph for export to Gephi for visualization. The goal is to automate connecting research information across platforms through authors, funding, and other relationships.
This document discusses research objects (ROs) and their role in reproducible science. It makes three key points:
1. Publications should convince readers of validity through reproducible results, but current systems do not fully facilitate reproducibility. ROs can address this by explicitly representing methods used.
2. Reproducibility reinforces results and is a key factor in scientific discovery. ROs provide a reproducible representation of methods.
3. ROs bundle together essential resources from a computational study, such as data, results, methods, people involved, and annotations for understanding, interpretation, and reuse. They support the full experimental lifecycle from problem definition to publication.
EXTRA is an open source rules based classification engine, developed by IPTC supported by a grant from Google DNI. Why are rules better than machine learning for breaking news? How can automation better support the manual crafting of news rules.
The Use of Big Data Techniques for Digital ArchivingSven Schlarb
These slides were used in a presentation at the "Our Digital Future - Multidisciplinary Perspectives on Long Term Data Preservation and Access" conference in Cambridge/UK in March 2016 in the session "Current and Future perspectives on technology for data preservation and sharing". They describe work in progress in the E-ARK project, which is co-funded by the European Commission and has as its main objective the creation of a scalable open source, digital archiving system offering efficent search and access content of very large digital object collections. The focus of this presentation lies on describing the core big data technologies (Apache Hadoop, Apache Hbase, and the document repository Lily developed by NGData), the architecture of the E-ARK integrated prototype implementation, and data mining use cases related to geographical data, named entitity extraction, and OLAP data analysis.
An update on the EXTRA project - an open source rules based classifier for news content. Including the application for additional funding from Google DNI for FRANCIS
SCAPE Information Day at BL - Characterising content in web archives with NaniteSCAPE Project
This presentation was given by Will Palmer at ‘SCAPE Information Day at the British Library’, on 14 July 2014. The information day introduced the EU-funded project SCAPE (Scalable Preservation Environments) and its tools and services to the participants.
In this presentation Will Palmer introduced the SCAPE developed tool Nanite which can help institutions analyze their web archive data.
A Framework for Multi-source Studies based on Unstructured Data.Sebastiano Panichella
Mathias Birrer, Pooja Ruhal, Sebastiano Panichella, and Oscar Niestrasz: Makar: A Framework for Multi-source Studies based on Unstructured Data. International Conference on Software Analysis, Evolution and Reengineering (SANER 2021).
The Mint is an authority control / vocabulary server designed to supply authority services to repositories. It is designed to be a practical tool for working towards Linked Data repositories, making it easy build high-quality metadata collection and discovery system.
Visualising Research Graph using Neo4j and Gephiamiraryani
This presentation discusses visualizing research graphs using Neo4j and Gephi. It introduces Research Graph, which connects research datasets, ORCID profiles, and grants to enable cross-platform discovery. The presentation demonstrates loading DSpace data into Neo4j, exploring the graph, and filtering the graph for export to Gephi for visualization. The goal is to automate connecting research information across platforms through authors, funding, and other relationships.
This document discusses research objects (ROs) and their role in reproducible science. It makes three key points:
1. Publications should convince readers of validity through reproducible results, but current systems do not fully facilitate reproducibility. ROs can address this by explicitly representing methods used.
2. Reproducibility reinforces results and is a key factor in scientific discovery. ROs provide a reproducible representation of methods.
3. ROs bundle together essential resources from a computational study, such as data, results, methods, people involved, and annotations for understanding, interpretation, and reuse. They support the full experimental lifecycle from problem definition to publication.
This document provides an overview of key concepts in R including:
- Common plots like basic plots, lattice plots, and ggplot2 plots that can be saved in different file formats
- Functions for reading and writing data from files, databases, and the web
- Common programming structures like functions, classes, control flow, and vectors
- How to get help in R using the ? operator and resources like RjpWiki
- That R can be used on different operating systems even without a GUI
Introduction to Business Analytics Course Part 9Beamsync
Beamsync is providing "Business Analytics Training in Bangalore" with experience faculty. If you are looking for analytics courses in Bengaluru consult beamsync.
For more details visit: http://beamsync.com/business-analytics-training-bangalore/
Introduction to Business Analytics Course Part 10Beamsync
Are you looking for Business Analytics training courses in Bangalore? then consult Beamsync.
Beamsync is providing business analytics training in Bengaluru / Bangalore with experience trainers. For schedules visit: http://beamsync.com/business-analytics-training-bangalore/
Introduction to Business Analytics Course Part 7Beamsync
Beamsync is providing analytics training courses in Bangalore. If you are looking for classroom training or online training for analytics courses, then contact Beamsync.
For more details visit: http://beamsync.com/business-analytics-training-bangalore/
This document provides an introduction to R Markdown. It explains that R Markdown combines Markdown syntax and R code chunks to create dynamic reports and documents. The document outlines the key topics that will be covered, including what Markdown and R Markdown are, Markdown syntax like headers, emphasis, lists, links and images, R code chunks and options, and RStudio settings. Resources for learning more about Markdown, R Markdown, and related tools are provided.
In this tutorial, we learn to access MySQL database from R using the RMySQL package. The tutorial covers everything from creating tables, appending data to removing tables from the database.
Introduction to Business Analytics Part 1 published by BeamSync.
BeamSync is providing business analytics training course in Bangalore. If you are looking for analytics training then visit BeamSync. Regular classes are running during the weekend.
For details visit: http://beamsync.com/business-analytics-training-bangalore/
This is an introduction to the use of analytics in eBusiness. It examines the rationale of using analytics (WHY), it highlights the areas that should be monitored (WHAT) and it considers the principles of conducting analytics in an eBusiness.
These slides were used as part of a guest talk to eBusiness undergraduate students at Oxford Brookes University (UK).
Tableau Software - Business Analytics and Data Visualizationlesterathayde
Tableau boasts drag-and-drop features that allow users to visualize information from any structured format. Tableau is the only provider of data visualization and business intelligence software that can be installed and used by anyone while also adhering to IT standards making it the fastest growing tool on the planet for Business Intelligence. Gartner has recently named us in the magic Quadrant among the Top 27 vendors for BI tool. We are no 1 in ease of use, no 1 in reporting and dashboard creation, interactive visualization, etc.
. Feel free to download the product, see the sample reports & dashboards for other industries from
http://www.tableausoftware.com
Please use the below link to download a 15 Day trial version of Tableau Desktop and Server Versions.
http://www.tableausoftware.com/products/trial
You can also do a self-training by going through the Videos in the below link.
http://www.tableausoftware.com/learn/training.
Step-1 Tableau Introduction
Step-2 Connecting to Data
Step-3 Building basic views
Step-4 Data manipulations and Calculated fields
Step-5 Tableau Dashboards
Step-6 Advanced Data Options
Step-7 Advanced graph Options
R is a statistical software environment used for data analysis and statistical graphics. It was created in 1991 at the University of Auckland. Some key features of R include its large collection of statistical and graphical techniques, the ability to display and manipulate data, and the ability to write custom code for new applications. R has a standard interface called RStudio that allows users to write code, view output, and access help documentation. Common tasks in R include importing data, generating descriptive statistics, creating visualizations, and performing statistical tests and modeling.
This document provides an introduction to the R programming language. It discusses reasons for using R such as its free and open-source nature, wide range of analysis methods, and growing popularity. It also covers basic R concepts like data frames, metadata, packages, and functions. The document emphasizes that R allows outputs from functions to be reused as inputs for other functions, and discusses saving and loading workspaces, managing directories, and redirecting output and graphs.
This document outlines the agenda for a two-day workshop on learning R and analytics. Day 1 will introduce R and cover data input, quality, and exploration. Day 2 will focus on data manipulation, visualization, regression models, and advanced topics. Sessions include lectures and demos in R. The goal is to help attendees learn R in 12 hours and gain an introduction to analytics skills for career opportunities.
R is a programming language and software environment for statistical analysis and graphical display of data. It is widely used among data scientists and researchers for developing statistical software and data analysis. Some key features of R include its large number of statistical and graphical techniques, ability to produce publications-quality plots, and availability of a vast collection of add-on packages. R also has disadvantages such as being an interpreted language and thus relatively slow, and having a difficult learning curve.
The presentation was given by Mr. Bas Kempen, ISRIC, during the GSOC Mapping Global Training hosted by ISRIC - World Soil Information, 6 - 23 June 2017, Wageningen (The Netherlands).
This document provides an agenda for a training session on AI and data science. The session is divided into two units: data science and data visualization. Key Python libraries that will be covered for data science include NumPy, Pandas, and Matplotlib. NumPy will be used to create and manipulate multi-dimensional arrays. Pandas allows users to work with labeled and relational data. Matplotlib enables data visualization through graphs and plots. The session aims to provide knowledge of core data science libraries and demonstrate data exploration techniques using these packages.
R is a programming language for statistical analysis and graphics. It is an open-source language developed by statisticians to allow for easy statistical analysis and visualization of data. The document provides an overview of R, discussing its origins, functionality, uses in data science, and popular packages and IDEs used with R. Examples are given of basic R syntax for vectors, matrices, data frames, plotting, and applying functions to data.
Introduction to Data Science, Prerequisites (tidyverse), Import Data (readr), Data Tyding (tidyr),
pivot_longer(), pivot_wider(), separate(), unite(), Data Transformation (dplyr - Grammar of Manipulation): arrange(), filter(),
select(), mutate(), summarise()m
Data Visualization (ggplot - Grammar of Graphics): Column Chart, Stacked Column Graph, Bar Graph, Line Graph, Dual Axis Chart, Area Chart, Pie Chart, Heat Map, Scatter Chart, Bubble Chart
This document provides a step-by-step guide to learning R. It begins with the basics of R, including downloading and installing R and R Studio, understanding the R environment and basic operations. It then covers R packages, vectors, data frames, scripts, and functions. The second section discusses data handling in R, including importing data from external files like CSV and SAS files, working with datasets, creating new variables, data manipulations, sorting, removing duplicates, and exporting data. The document is intended to guide users through the essential skills needed to work with data in R.
Ross King, Project Director of SCAPE, gave a short presentation of the EU funded project SCAPE, including descriptions of tools for planning and monitoring digital preservation, scalable computation and repositories, SCAPE Testbeds and where to learn more.
The presentation was given at the workshop ‘Preservation at Scale’ http://bit.ly/17ppAln in connection with the iPres2013 conference in Lissabon, Portugal, in September 2013.
This document provides an overview of key concepts in R including:
- Common plots like basic plots, lattice plots, and ggplot2 plots that can be saved in different file formats
- Functions for reading and writing data from files, databases, and the web
- Common programming structures like functions, classes, control flow, and vectors
- How to get help in R using the ? operator and resources like RjpWiki
- That R can be used on different operating systems even without a GUI
Introduction to Business Analytics Course Part 9Beamsync
Beamsync is providing "Business Analytics Training in Bangalore" with experience faculty. If you are looking for analytics courses in Bengaluru consult beamsync.
For more details visit: http://beamsync.com/business-analytics-training-bangalore/
Introduction to Business Analytics Course Part 10Beamsync
Are you looking for Business Analytics training courses in Bangalore? then consult Beamsync.
Beamsync is providing business analytics training in Bengaluru / Bangalore with experience trainers. For schedules visit: http://beamsync.com/business-analytics-training-bangalore/
Introduction to Business Analytics Course Part 7Beamsync
Beamsync is providing analytics training courses in Bangalore. If you are looking for classroom training or online training for analytics courses, then contact Beamsync.
For more details visit: http://beamsync.com/business-analytics-training-bangalore/
This document provides an introduction to R Markdown. It explains that R Markdown combines Markdown syntax and R code chunks to create dynamic reports and documents. The document outlines the key topics that will be covered, including what Markdown and R Markdown are, Markdown syntax like headers, emphasis, lists, links and images, R code chunks and options, and RStudio settings. Resources for learning more about Markdown, R Markdown, and related tools are provided.
In this tutorial, we learn to access MySQL database from R using the RMySQL package. The tutorial covers everything from creating tables, appending data to removing tables from the database.
Introduction to Business Analytics Part 1 published by BeamSync.
BeamSync is providing business analytics training course in Bangalore. If you are looking for analytics training then visit BeamSync. Regular classes are running during the weekend.
For details visit: http://beamsync.com/business-analytics-training-bangalore/
This is an introduction to the use of analytics in eBusiness. It examines the rationale of using analytics (WHY), it highlights the areas that should be monitored (WHAT) and it considers the principles of conducting analytics in an eBusiness.
These slides were used as part of a guest talk to eBusiness undergraduate students at Oxford Brookes University (UK).
Tableau Software - Business Analytics and Data Visualizationlesterathayde
Tableau boasts drag-and-drop features that allow users to visualize information from any structured format. Tableau is the only provider of data visualization and business intelligence software that can be installed and used by anyone while also adhering to IT standards making it the fastest growing tool on the planet for Business Intelligence. Gartner has recently named us in the magic Quadrant among the Top 27 vendors for BI tool. We are no 1 in ease of use, no 1 in reporting and dashboard creation, interactive visualization, etc.
. Feel free to download the product, see the sample reports & dashboards for other industries from
http://www.tableausoftware.com
Please use the below link to download a 15 Day trial version of Tableau Desktop and Server Versions.
http://www.tableausoftware.com/products/trial
You can also do a self-training by going through the Videos in the below link.
http://www.tableausoftware.com/learn/training.
Step-1 Tableau Introduction
Step-2 Connecting to Data
Step-3 Building basic views
Step-4 Data manipulations and Calculated fields
Step-5 Tableau Dashboards
Step-6 Advanced Data Options
Step-7 Advanced graph Options
R is a statistical software environment used for data analysis and statistical graphics. It was created in 1991 at the University of Auckland. Some key features of R include its large collection of statistical and graphical techniques, the ability to display and manipulate data, and the ability to write custom code for new applications. R has a standard interface called RStudio that allows users to write code, view output, and access help documentation. Common tasks in R include importing data, generating descriptive statistics, creating visualizations, and performing statistical tests and modeling.
This document provides an introduction to the R programming language. It discusses reasons for using R such as its free and open-source nature, wide range of analysis methods, and growing popularity. It also covers basic R concepts like data frames, metadata, packages, and functions. The document emphasizes that R allows outputs from functions to be reused as inputs for other functions, and discusses saving and loading workspaces, managing directories, and redirecting output and graphs.
This document outlines the agenda for a two-day workshop on learning R and analytics. Day 1 will introduce R and cover data input, quality, and exploration. Day 2 will focus on data manipulation, visualization, regression models, and advanced topics. Sessions include lectures and demos in R. The goal is to help attendees learn R in 12 hours and gain an introduction to analytics skills for career opportunities.
R is a programming language and software environment for statistical analysis and graphical display of data. It is widely used among data scientists and researchers for developing statistical software and data analysis. Some key features of R include its large number of statistical and graphical techniques, ability to produce publications-quality plots, and availability of a vast collection of add-on packages. R also has disadvantages such as being an interpreted language and thus relatively slow, and having a difficult learning curve.
The presentation was given by Mr. Bas Kempen, ISRIC, during the GSOC Mapping Global Training hosted by ISRIC - World Soil Information, 6 - 23 June 2017, Wageningen (The Netherlands).
This document provides an agenda for a training session on AI and data science. The session is divided into two units: data science and data visualization. Key Python libraries that will be covered for data science include NumPy, Pandas, and Matplotlib. NumPy will be used to create and manipulate multi-dimensional arrays. Pandas allows users to work with labeled and relational data. Matplotlib enables data visualization through graphs and plots. The session aims to provide knowledge of core data science libraries and demonstrate data exploration techniques using these packages.
R is a programming language for statistical analysis and graphics. It is an open-source language developed by statisticians to allow for easy statistical analysis and visualization of data. The document provides an overview of R, discussing its origins, functionality, uses in data science, and popular packages and IDEs used with R. Examples are given of basic R syntax for vectors, matrices, data frames, plotting, and applying functions to data.
Introduction to Data Science, Prerequisites (tidyverse), Import Data (readr), Data Tyding (tidyr),
pivot_longer(), pivot_wider(), separate(), unite(), Data Transformation (dplyr - Grammar of Manipulation): arrange(), filter(),
select(), mutate(), summarise()m
Data Visualization (ggplot - Grammar of Graphics): Column Chart, Stacked Column Graph, Bar Graph, Line Graph, Dual Axis Chart, Area Chart, Pie Chart, Heat Map, Scatter Chart, Bubble Chart
This document provides a step-by-step guide to learning R. It begins with the basics of R, including downloading and installing R and R Studio, understanding the R environment and basic operations. It then covers R packages, vectors, data frames, scripts, and functions. The second section discusses data handling in R, including importing data from external files like CSV and SAS files, working with datasets, creating new variables, data manipulations, sorting, removing duplicates, and exporting data. The document is intended to guide users through the essential skills needed to work with data in R.
Ross King, Project Director of SCAPE, gave a short presentation of the EU funded project SCAPE, including descriptions of tools for planning and monitoring digital preservation, scalable computation and repositories, SCAPE Testbeds and where to learn more.
The presentation was given at the workshop ‘Preservation at Scale’ http://bit.ly/17ppAln in connection with the iPres2013 conference in Lissabon, Portugal, in September 2013.
The document discusses various methods for reading data into R from different sources:
- CSV files can be read using read.csv()
- Excel files can be read using the readxl package
- SAS, Stata, and SPSS files can be imported using the haven package functions read_sas(), read_dta(), and read_sav() respectively
- SAS files with the .sas7bdat extension can also be read using the sas7bdat package
Learntek is global online training provider on Big Data Analytics, Hadoop, Machine Learning, Deep Learning, IOT, AI, Cloud Technology, DEVOPS, Digital Marketing and other IT and Management courses.
This document summarizes a presentation given by Thomas Hütter on using R for data analysis and visualization. The presentation provided an overview of R's history and ecosystem, introduced basic data types and functions, and demonstrated connecting to a SQL Server database to extract and analyze sales data from a Dynamics Nav system. It showed visualizing the results with ggplot2 and creating interactive apps with the Shiny framework. The presentation emphasized that proper data understanding is important for reliable analysis and highlighted resources for learning more about R.
Unit I - introduction to r language 2.pptxSreeLaya9
1. The document discusses loading and manipulating data in R. It covers reading data from built-in and external datasets, as well as transforming data using the dplyr and tidyr packages.
2. The dplyr package allows for efficient data manipulation through functions that select, filter, arrange, and summarize data.frame objects.
3. The tidyr package contains functions like pivot_longer that reshape data from wide to long format, making it easier to visualize and analyze relationships between variables.
An introduction to R is a document usefulssuser3c3f88
R is a language and environment for statistical computing and graphics. It provides functions for data manipulation, calculation, and graphical displays. Key features of R include its ability to produce publication-quality plots, perform statistical tests, fit models to data, and develop statistical software. R has an extensive library of additional user-contributed packages that extend its capabilities. The document provides information on downloading and using R, reading data into R, customizing plots, and interactive plotting functions.
R is a programming language and environment for statistical analysis and graphics. It has many built-in statistical and graphical techniques. R can be installed from CRAN and runs on Windows, MacOS, and UNIX systems. The basic R interface is the console, but RStudio provides an integrated development environment. In RStudio, you can write scripts, see outputs and plots, and access help and packages. Packages extend R's functionality through additional functions and data. Common data types in R include numeric, integer, character, factor, and logical. Vectors are the basic data structure, but R also supports matrices, arrays, data frames and lists.
This document provides an introduction to R and covers topics such as installing R and RStudio, basic data types and structures in R, importing and viewing data, manipulating data through filtering, binding, transforming and sorting, joining datasets, creating summary tables, exporting data, and making plots and visualizations. The goal is to help users get familiar with basic commands/functions in R and be able to do basic analysis on any dataset.
Managing Your Security Logs with ElasticsearchVic Hargrave
The ELK stack (Elasticsearch-Logstash-Kibana) provides a cost effective alternative to commercial SIEMs for ingesting and managing OSSEC alert logs. This presentation will show you how to construct a low cost SIEM based on ELK that rivals the capabilties of commercials SIEMs.
R is a free programming language and software environment for statistical analysis and graphics. It contains functions for data manipulation, calculation, and graphical displays. Some key features of R include being free, running on multiple platforms, and having extensive statistical and graphical capabilities. Common object types in R include vectors, matrices, data frames, and lists. R also has packages that add additional functions.
Beyond Degrees - Empowering the Workforce in the Context of Skills-First.pptxEduSkills OECD
Iván Bornacelly, Policy Analyst at the OECD Centre for Skills, OECD, presents at the webinar 'Tackling job market gaps with a skills-first approach' on 12 June 2024
🔥🔥🔥🔥🔥🔥🔥🔥🔥
إضغ بين إيديكم من أقوى الملازم التي صممتها
ملزمة تشريح الجهاز الهيكلي (نظري 3)
💀💀💀💀💀💀💀💀💀💀
تتميز هذهِ الملزمة بعِدة مُميزات :
1- مُترجمة ترجمة تُناسب جميع المستويات
2- تحتوي على 78 رسم توضيحي لكل كلمة موجودة بالملزمة (لكل كلمة !!!!)
#فهم_ماكو_درخ
3- دقة الكتابة والصور عالية جداً جداً جداً
4- هُنالك بعض المعلومات تم توضيحها بشكل تفصيلي جداً (تُعتبر لدى الطالب أو الطالبة بإنها معلومات مُبهمة ومع ذلك تم توضيح هذهِ المعلومات المُبهمة بشكل تفصيلي جداً
5- الملزمة تشرح نفسها ب نفسها بس تكلك تعال اقراني
6- تحتوي الملزمة في اول سلايد على خارطة تتضمن جميع تفرُعات معلومات الجهاز الهيكلي المذكورة في هذهِ الملزمة
واخيراً هذهِ الملزمة حلالٌ عليكم وإتمنى منكم إن تدعولي بالخير والصحة والعافية فقط
كل التوفيق زملائي وزميلاتي ، زميلكم محمد الذهبي 💊💊
🔥🔥🔥🔥🔥🔥🔥🔥🔥
This presentation was provided by Racquel Jemison, Ph.D., Christina MacLaughlin, Ph.D., and Paulomi Majumder. Ph.D., all of the American Chemical Society, for the second session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session Two: 'Expanding Pathways to Publishing Careers,' was held June 13, 2024.
Elevate Your Nonprofit's Online Presence_ A Guide to Effective SEO Strategies...TechSoup
Whether you're new to SEO or looking to refine your existing strategies, this webinar will provide you with actionable insights and practical tips to elevate your nonprofit's online presence.
2. ● Get a basic introduction to R
● Understand exploration of data
● Explore data using R
● Visualize data using R
● Understand diagnostic analytics
● Implement diagnostic analytics using R
Objective Slide
After completing
this course, you will
be able to:
Copyright 2016,Beamsync, All rights reserved.
3. Introduction to R
• Programming language for graphics and statistical computations
• Available freely under the GNU public license
• Used in data mining and statistical analysis
• Included time series analysis, linear and non linear modeling among others
• Very active community and package contributions
• Very little programming language knowledge necessary
• Can be downloaded from http://www.r-project.org/
• R Studio - optional
Copyright 2016,Beamsync, All rights reserved.
4. • Packages -
• install.packages('package_name')
• library(package_name)
• Loading data –
• data(dataset_name)
• read and write functions
• getwd() and setwd(dir)
• read and write functions use full path name
• Example : read.csv(“C:/Rtutorials/Sampledata.csv”).
• Assignment operator : “ <- ”
• Help - ?function_name
Basic R
Copyright 2016,Beamsync, All rights reserved.
5. • Basic functions for data exploration in R
• Data stored as “data frames”
• Data frames – tabular representation of data with rows and columns
• Every row denotes a particular “case”
• Sample data frame (iris data set)
Data exploration using R
Sepallength Sepalwidth Petallength Petalwidth Species
7.9 3.8 6.4 2 I. virginica
7.7 3 6.1 2.3 I. virginica
5.6 2.5 3.9 1.1 I. versicolor
5.6 2.8 4.9 2 I. virginica
5.5 4.2 1.4 0.2 I. setosa
5.5 3.5 1.3 0.2 I. setosa
7.1 3 5.9 2.1 I. virginica
7 3.2 4.7 1.4 I. versicolor
Copyright 2016,Beamsync, All rights reserved.
6. • Iris dataset – built in
data frame
• View data set
• iris
• View first few rows of
the data set
• head(iris, n)
• View last few rows of
the data set
• tail(iris, n)
Viewing data frame
Copyright 2016,Beamsync, All rights reserved.
7. • View the dimensions of the data set
• dim(iris)
• View the number of columns
• ncol(iris)
• View the number of rows
• nrow(iris)
Dimensions of data frame
Copyright 2016,Beamsync, All rights reserved.
8. • View column names/headers
• names(iris)
• View all attributes
• attributes(iris)
Attributes of Data frame
Copyright 2016,Beamsync, All rights reserved.
10. Thank You
Beamsync is providing business analytics training in Bangalore along with
certification. If you are looking for upcoming training schedules then visit:
http://beamsync.com/business-analytics-training-bangalore/