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R Programming: The Language Marketers Use to Tame Data

R Programming: The Language Marketers Use to Tame Data

How to manage data? This is a question that’s baffles us each and every time, whenever we look at data.

The real challenge is not about managing data, but how to synchronize processes to expose the issues with data. Today’s marketers may have a tough time tackling these challenges. Even more for non-tech-savvy marketers, they may be feel a bit overwhelmed, but we’ve a solution – R programming language is capable of performing specific tasks while preparing data for machine learning models or advanced analytics.

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Basics of R

R programming is a popular open source language ideal for smart data visualization and statistical modeling. Generally, it functions through a terminal on a laptop, but you can also enjoy development environment software that makes R quite user-friendly.

One of the most sought after Integrated Development Environment (IDE) is RStudio – it’s very popular amongst practitioners mostly owing to its quad-window view, which let users view their results in the terminal beside the whiteboard platform.

Exploring Data with R

Data importing is the starting point of analyzing data. Fortunately, a more than sufficient number of R programming libraries exist today that are up to interface with a database or an API. Some of these libraries are: twitteR, RMongo and Jsonlite. A quick search across Comprehensive R Archive Network will help you find them.

Next, you have to turn your attention to data wrangling. It’s the method of mapping one row format to another, while amalgamating, dividing and rearranging rows and columns. Map out the metrics after ascertaining whether a task falls under one of the following mathematical categories:

  • Discrete Metrics
  • Continuous Metrics

Another significant step is corroborating the columns decided: are headers from the data source given? R Programming helps add headers on data as soon as data is imported. Furthermore, another question that pops up here is that are the headers from the same labels of parties who have access to data? Now, this question is instrumental in answering whether there is any more efficient way to have access to data consecutively without manually rectifying columns before placing the data in a model.

For R programming, some of the basic libraries to consider are as follows:

Readr – It helps estimate functions and read data in rectangular tabular formats

Tidyr – It helps in organizing missing field values and arranging tabular data in an effective and compatible structure

Dplyr – Ideal for transforming data after it’s added in R

Marketing Knowledge Is Still an Add-On Factor

Lastly, marketers should never ignore their domain knowledge, while modeling data. At times, your experience will help you tackle an outlier for a model in the best way possible. Or else, you might ask your technical team to adjust and manage data in cloud in a situation where other teams try to downstream assess data.

Thus, a relevant marketing knowledge is essential. It will help decide which data to be queried or how to parse it well.

If you are thinking of learning a popular yet effective programming language to tame your data, R Programming certification in Delhi NCR is the best solution for you. A good R programming training will help you understand and evaluate data like a pro.

 

The blog first appeared on ― www.cmswire.com/digital-marketing/how-marketers-can-plan-data-mining-with-r-programming

 

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Most Popular R Programming Interview Questions with Answers to Help You Get Started

Most Popular R Programming Interview Questions with Answers to Help You Get Started

Brainchild of Ross Ihaka and Robert Gentleman, R programming language was first developed in 1993 with an exclusive and extensive catalog of statistical and graphical techniques and processes, including machine learning, time series, linear regression, statistical inference and lot more.

In the following section, we’re about to talk about top interview questions on R programming –perfect for both freshers and experienced consultants, this interesting interview guide covers almost all the major concepts of R and its applications.

Dive Down!

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What is R programming?

R programming is an ideal language used for data analysis, and to build incredible statistical software. It’s widely used for a wide range of machine learning applications.

How to write syntax for R commands?

When you start writing commands in R, start using # in the beginning of the line, so that the commands are written as #division.

How to project data analysis outcome through R language?

The best way to convey the results would be by combining the results of data, code and analysis on a document and present the data for further reproducible research. It would help the user recheck the result and take part in the following discussions. The reproducible research aids in performing experiments easily and solving crucial problems.

What are the data structures found in R programming?

Homogenous and Heterogeneous are two data structures found in R programming. For same kinds of objects, we suggest using homogenous data structures as for Array, Vectors and Matrix. And for different types of objects, it’s better to stick to heterogeneous data structures.

How should you import data in R language?

Importing of data in R is done with the help of R commander GUI – it’s used to type commands and is also known as Rcmdr.

Here are 3 ways to import data into R:

  • As soon as you select data set from the dialog box, enter the date set name as asked.
  • R command can also be used to enter data – Data-> New Data Set (It’s only applicable for small data sets).
  • The user can also import data directly from URL, through simple ASCII file, statistical package or from clipboards.

Highlight the advantages of R programming language.

  • The user doesn’t get entangled in license restrictions and norms for using R programming.
  • It’s an open source software and completely free of cost.
  • It has several graphical capabilities.
  • It is easily run on a majority of hardware and OS (including 32 and 64-bit processors).

Mention the limit for memory in R.

For a 32-bit system, the memory of R is limited to 3GB. And for a 64-bit system, the limit is extended to 8TB.

With this, hope you are ready to crack a tough job interview on R programming – however, for those, who want to dig deeper into the intricacies of this fascinating programming language, we have fabulous R programming courses in Gurgaon. With them discover the path towards a dream career!

 

The blog has been sourced from www.janbasktraining.com/blog/r-interview-questions-answers

 

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R Programming, Python or Scala: Which is the Best Big Data Programming Language?

R Programming, Python or Scala: Which is the Best Big Data Programming Language?

For data science and big data, R, Python and Scala are the 3 most important languages to master. It’s a widely-known notion, organizations of varying sizes relies on massive structured and unstructured data to predict trends, patterns and correlations. They are of expectation that such a robust analysis will lead to better business decisions and individual behavior predictions.

In 2017, the adoption of Big Data analytics has spiked up to 53% in companies – says Forbes.

The story of evolution

To start with, big data is just data, after all. The entire game-play depends on its analysis – how well the data is analyzed so as to churn out valuable business intelligence. With years, data burgeoned, and it’s still expanding. The evolution of big data mostly happened because traditional database structures couldn’t cope with such multiplying data – scaling data became an important issue.

For that, here we have some popular big data programming languages. Dive down:

R Programming

R Programming is mainly used for statistical analysis. A set of packages are available for R named Programming with Big Data in R (pbdR), which encourages big data analysis, across multiple systems via R code.

R is robust and flexible; it can be run on almost every OS. To top that, it boasts of excellent graphical capabilities, which comes handy when trying to visualize models, patterns and associations within big data structures.

According to industry standards, the average pay of R Programmers is $115,531 per year.

For R language training, drop by DexLab Analytics.

Python

Compared to R, Python is more of a general-purpose programming language. Developers adore it, because it’s easy to learn, a huge number of tutorials are available online and is perfect for data analysis, which requires integration with web applications.

Python gives excellent performance and high scalability for a series of complicated data science tasks. It is used with high-in-function big data engines, like Apache Spark through available Python APIs.

Their Machine Learning Using Python courses are of highest quality and extremely student-friendly.

Let’s Take Your Data Dreams to the Next Level

Scala

Last but not the least, Scala is a general-purpose programming language developed mainly to address some of the challenges of Java language. It is used to write Apache Spark cluster computing solution. Hence, Scala has been a popular programming language in the field of data science and big data analysis, in particular.

There was a time when Scala was mandatory to work on Spark, but with the proliferation of many API endpoints approachable with other languages, this problem has been addressed. Nevertheless, it’s still the most significant and popular language for several big data tools, including Finagle. Also Scala houses amazing concurrency support, which parallelizes a whole many processes for huge data sets.

The average annual salary for a data scientist with Scala skills is $102,980.

In the end, you can never go wrong with selecting any one of the big data programming languages. All of them are equally good, productive and easy to excel on. However, Python is probably the best one to start off with.

For more updates or information on big data courses, visit DexLab Analytics.

The original article is here at – http://www.i-programmer.info/news/197-data-mining/11622-top-3-languages-for-big-data-programming.html

 

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Periscope Data Adds Python, R and SQL on A Single Platform for Better, Powerful Data Analysis

Periscope Data Adds Python, R and SQL on A Single Platform for Better, Powerful Data Analysis

Recently, a veteran data analytics software provider, Periscope Data announced some brand new developments while updating their Unified Data Platform for Python, R programming and Structured Query Language. This new Unified Data Platform will enable data professionals to work in sync with 3 key skills all on a single platform.  Far more better analysis will be conducted using less time by altering data in SQL, executing complex statistical analyses in Python or R, followed by improved visualization, collaboration and reporting of results – all performed on Periscope’s dynamic analytics platform.

A massive data explosion is taking place around the world around us. More than 90% of the world’s data has been created in the past two years, and the numbers are still on the rise. To this, new levels of sophistication needs to be added to analyze the complexity of data – “The addition of Python and R support to our Unified Data Platform gives our customers a unique combination of tools – from machine learning to natural language processing to predictive analytics, analysts will be able to answer new questions that have yet to be explored,” says Harry Glaser, co-founder and CEO of Periscope Data.

The inclusion of Python and R support in Periscope framework comes with ample benefits, and some of them are highlighted below:

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All data at a single place

Instead of relying on several data sources, Periscope Data prefers to combine data together collected from various databases to bring them to a single platform, where nothing but a single source of truth for data is established. The data collected is updated and in crisp format.

Predictive analytics

It’s time to leverage Python and R libraries and move beyond the conventional historical reporting for the sake of modeling predictions. With lead scoring and churning prediction, businesses are now in a better position to derive significant insights about a future of a company.

No more switching between tools

Seamlessly, users can switch between querying data in SQL and analyzing data in R or Python, all at the same time on a same platform. Data professionals will be able to modify their datasets, enhance the performance of their models and update visualizations from a single location.

Mitigate data security concerns

The integration of R, Python and SQL by Periscope Data ensures the data professionals can run and share all sorts of models securely and in full compliance with all the norms, instead of seeking open source tools. Periscope Data is SOC2 and HIPAA compliant. It performs regular internal audits to check compliance requirements and safety issues.

Efficient collaboration with teams

As all the analysis takes place in a central location, be sure all your insights will be thoroughly consistent, secure and free of any version-control issues. Also, Periscope Data allows you and your team members the right to read and write access when required.

Easy visualization of analysis

To develop powerful visualizations that reach one’s heart and mind, leverage Periscope’s resources to the optimum levels. Data teams allow users to easily visualize through R packages and Python libraries so as to nudge users to explore the better horizons of data.

To learn more about R programming or Python, opt for Python & Spark training by DexLab Analytics. R language certification in Delhi NCR empowers students and professionals to collaborate and derive better insights faster and efficiently.

 

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Top 5 Programming Languages to Learn in 2018

Who doesn’t want to ace the rat race!! Owing to robust technological innovations and globalization, staying on top has become an essential factor for professional success.

 
Top 5 Programming Languages to Learn in 2018
 

Amidst this, technology plays a key role. The job profiles of data scientists are fetching maximum attention. At present, they are among the most in-demand professionals around the globe bagging in handsome paychecks. Nevertheless, it’s no mean feat to become one, the process of education and training is highly intricate and demands unparalleled acumen, expertise and skill.

 

And with more than 600 incredible programming languages to learn, data scientists go haywire when it comes to the choosing part. While Java, Python, JavaScript, R remains the top-priority languages to impress the employers, newer, more innovative languages are also blocking the space, time and again.

Continue reading “Top 5 Programming Languages to Learn in 2018”

How R Programming is Transforming Business for Good

Today, every business is putting efforts to understand their customers and themselves, better. But, how? What methods are they applying? Do mere Excel pivot tables help analyze vast pool of data? The answer to the latter question is in the negative – Excel pivot tables are not that great at analyzing data – so a wide number of companies look forward to SAS and R Programming to cull Business Intelligence.

 
How R Programming is Transforming Business for Good
 

Besides SAS, R-Programming is another open-source language that is used by most of the budding data scientists in the world of analytics. The R Programming language is more oriented towards the correct implication of data science, while ensuring business the cutting edge data analysis tools. Continue reading “How R Programming is Transforming Business for Good”

Open a World of Opportunities: Web Scraping Using PHP and Python

Open a World of Opportunities: Web Scraping Using PHP and Python

The latest estimates says, the total number of websites has crossed one billion mark; everyday a new site is being added and removed, but the record stays.

Having said that, just imagine how much data is floating around the web. The amount is so huge that it would be impossible for even hundreds of humans to digest all the information in a lifetime. To tackle such large amounts of data, you not only need to have easy access to all the information but should also process some scalable way to gather data in order to organize and analyze it. And that’s exactly where web data scraping comes into picture.

Web scraping, data mining, web data extraction, web harvesting or screen scraping – they all means the same thing – a technique in which a computer program fetches huge piles of data from a website and saves them in your computer, spreadsheet or database in a normal format for easy analysis.

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Web Scraping with Python and BeautifulSoup

In case, you are not satisfied with the internet sources of web scraping, you are most likely to develop your very own data scraping tools, which is quite easier. In this blog we will show you how to frame a web scraper with Python and very simple yet dynamic BeautifulSoup Library:

First, import the libraries we will use: requests and BeautifulSoup:

# Import libraries
import requests
from bs4 import BeautifulSoup

Secondly, point out the variable for the URL using request.get method and gain access to the HTML content right from this page:

import requests
URL = "http://www.values.com/inspirational-quotes"
r = requests.get(URL)
print(r.content)

Next, we will parse a webpage, and for that, we need to create a BeautifulSoup object:

import requests 
from bs4 import BeautifulSoup
URL = "http://www.values.com/inspirational-quotes"
r = requests.get(URL)

 # Create a BeautifulSoup object
soup = BeautifulSoup(r.content, 'html5lib')
print(soup.prettify())

Now, let’s extract some meaningful information from HTML content. Look at the HTML content of the webpage, which was printed using the soup.pretify()method..

table = soup.find('div', attrs = {'id':'container'})

Here, you will find each quote inside a div container, belonging to the class quote.

We will repeat the process with each div container, belonging to the class quote. For that, we will use findAll()method and repeat the process with each quote using variable row.

After which, we will create a dictionary, in which all the data about the quote will be saved in a list, and is called ‘quotes’.

    quote['lines'] = row.h6.text

Now, coming to the final step – write down the data to a CSV file, but how?

See below:

filename = 'inspirational_quotes.csv'
with open(filename, 'wb') as f:
    w = csv.DictWriter(f,['theme','url','img','lines','author'])
    w.writeheader()
    for quote in quotes:
        w.writerow(quote)

This type of web scraping is used on a small-scale; for larger scale, you can consider:

Scraping Websites with PHP and Curl

To connect to a large number of servers and protocols, and download pictures, videos and graphics from several websites, consider Scraping Websites with PHP and cURL.

<?php

function curl_download($Url){

    if (!function_exists('curl_init')){
        die('cURL is not installed. Install and try again.');
    }

    $ch = curl_init();
    curl_setopt($ch, CURLOPT_URL, $Url);
    curl_setopt($ch, CURLOPT_RETURNTRANSFER, true);
    $output = curl_exec($ch);
    curl_close($ch);

    return $output;

print curl_download('http://www.gutenberg.org/browse/scores/top');

?>

In a nutshell, the scopes of using web scraping for analyzing content and applying it to your content marketing strategies are vast like the horizon. Armed by endless types of data analysis, web scraping technology has proved to be a valuable tool for the content producers. So, when are you feeding yourself with web scraping technology?

Discover the perfect platform for excellent R programming using Python courses. For more information on R programming training institute drop by DexLab Analytics.

 
This post originally appeared ondzone.com/articles/be-leading-content-provider-using-web-scraping-php
 

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How to Create Repeat Loop in R Programming

In this tutorial, we will learn to make a repeat loop with the use of R programming.

How to Create Repeat Loop in R Programming

A repeat loop is used to iterate over a block of code over several number of times.

In case of a repeat loop, there is no condition to check in for exiting repeat loop.

Hence, we must ourselves put a condition explicitly within a repeat loop body and make use of the break statement to exit the loop. Failing to do so will result into an infinite loop.

 Syntax of repeat loop

repeat {
   statement
}

When in the statement block, we must use the statement ‘break’ to exit the loop.

 r-repeat-loop-flowchart-120

Example: repeat loop

x <- 1

repeat {
   print(x)
   x = x+1
   if (x == 6){
       break
   }
}

 Output

[1] 1
[1] 2
[1] 3
[1] 4
[1] 5

Note that in the example above, we have only made use of a condition to check and exit the loop when x equals the value of 6.

That is why we see in our output that only values from 1 to 5 get printed.

Why not pull the strings of your career by enrolling for an intensive R programming certification course in Delhi!  DexLab Analytics, being a premier R programming training institute can help you on your endeavour.


This post originally appeared onwww.datamentor.io/r-programming/repeat-loop

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ANZ uses R programming for Credit Risk Analysis

ANZ uses R programming for Credit Risk Analysis

At the previous month’s “R user group meeting in Melbourne”, they had a theme going; which was “Experiences with using SAS and R in insurance and banking”. In that convention, Hong Ooi from ANZ (Australia and New Zealand Banking Group) spoke on the “experiences in credit risk analysis with R”. He gave a presentation, which has a great story told through slides about implementing R programming for fiscal analyses at a few major banks.

In the slides he made, one can see the following:

How R is used to fit models for mortgage loss at ANZ

A customized model is made to assess the probability of default for individual’s loans with a heavy tailed T distribution for volatility.

One slide goes on to display how the standard lm function for regression is adapted for a non-Gaussian error distribution — one of the many benefits of having the source code available in R.

A comparison in between R and SAS for fitting such non-standard models

Mr. Ooi also notes that SAS does contain various options for modelling variance like for instance, SAS PROC MIXED, PRIC NLIN. However, none of these are as flexible or powerful as R. The main difference as per Ooi, is that R modelling functions return as object as opposed to returning with a mere textual output. This however, can be later modified and manipulated with to adapt to a new modelling situation and generate summaries, predictions and more. An R programmer can do this manipulation.

 

Read Also: From dreams to reality: a vision to train the youngsters about big data analytics by the young entrepreneurs:

 

We can use cohort models to aggregate the point estimates for default into an overall risk portfolio as follows:

A comparison in between R and SAS for fitting such non-standard models
Photo Coutesy of revolution-computing.typepad.com

He revealed how ANZ implemented a stress-testing simulation, which made available to business users via an Excel interface:

The primary analysis is done in r programming within 2 minutes usually, in comparison to SAS versions that actually took 4 hours to run, and frequently kept crashing due to lack of disk space. As the data is stored within SAS; SAS code is often used to create the source data…

While an R script can be used to automate the process of writing, the SAS code can do so with much simplicity around the flexible limitations of SAS.

 

Read Also: Dexlab Analytics' Workshop on Sentiment Analysis of Twitter Data Using R Programming

 

Comparison between use of R and SAS’s IML language to implement algorithms:

Mr. Ooi’s R programming code has a neat trick of creating a matrix of R list objects, which is fairly difficult to do with IML’s matrix only data structures.

He also discussed some of the challenges one ma face when trying to deploy open-source R in the commercial organizations, like “who should I yell at if things do now work right”.

And lastly he also discussed a collection of typically useful R resources as well.

For people who work in a bank and need help adopting R in the workflow, may make use of this presentation to get some knowledge about the same. And also feel free to get in touch with our in-house experts in R programming at DexLab Analytics, the premiere R programming training institute in India.

 

Refhttps://www.r-bloggers.com/how-anz-uses-r-for-credit-risk-analysis/

 

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