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3 Most Used Data Science Tools in 2018

The humongous amount of data calls for advanced data science tools – to completely understand and analyze the information.

Data analytics fuels digital transformation. The best way to do this is by arming an expert pool of statisticians, math pundits and business analysts with suitable data science tools with which they can squelch out crucial insights from the ever-growing silos of corporate data. This kind of initiatives promote a data-driven business culture, which acts as a present prerequisite – and this why here we’ve jotted down top 3 data science tools that’s weaving wonders with the new oil of the world, data:

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Python

Both, well-performing software and a powerful programming language perfect for developing custom algorithms, Python is the most must-have tool for all data scientists. In a recent KDnuggets survey of 2052 users, Python language was recommended by 65.6% of respondents.

“We use Python both for data science and back end, which provides us with rapid development and machine learning model deployment,” shared Alexander Osipenko, lead data scientist at Cindicator Inc. “It’s also of great importance for us to ensure the security of implemented tools.”

Leslie De Jesus, innovation director and lead data scientist at Wovenware emphasized on the importance of Python libraries. “[We use] Python Libraries, including Scrapy, for web scraping and being able to extract data from the internet and upload it into a data frame for analysis,” said De Jesus.

Few others vouched for Python because of its multifaceted nature and strong optimization skills.

For Python Certification Training in Delhi, drop by DexLab Analytics.

R

Quite similar to Python, R is the go-to programming language for many data scientists and they depend on it wholly because it’s simpler and more specifically-built for data science. According to the KDnuggets poll, 48.5% respondents voted it to be one of the leading data science tools.

As for all, R programming language is blessed with cultivated capabilities for machine learning and statistics, and professionals love using it. It’s another favorite of data analysts, especially those who deals with a lot of data exploration.

“I can quickly see summary stats like mean, median and quartiles; quickly create different graphs; and create test data sets, which can be easily shared and exported to CSV format,” said Jon Krohn, chief data scientist at Untapt Inc.

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Tableau

Bridging the gap between skilled data science teams and more business-oriented analytics consultants, Tableau Software is the fastest data visualization and dashboard tool. “It is a fantastic tool for data scientists and noobs working on data science,” said Pooja Pandey, senior executive for SEO at Entersoft Security. “[It’s a] quick dashboarding tool to visualize insights and analytical data with a very short learning curve.”

The lightening speed of Tableau’s visualization and reporting functions is commendable. It’s easy to learn, quick to implement and intuitive to use. Moreover, it helps different segments of a company to customize exhaustive reports according to their requirements.

Now, if you are looking for ways to hone your visualization skills, we would recommend Tableau BI training courses from DexLab Analytics. Their training courses are comprehensive, well-research and as per industry standards.

 

The blog has been sourced fromsearchbusinessanalytics.techtarget.com/feature/Data-scientists-weigh-in-5-data-science-tools-to-consider

 

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Why R is the Most Suitable Programming Language for Encompassing Data Science Projects?

Why R is the Most Suitable Programming Language for Encompassing Data Science Projects?

Since the early 1990’s, when R was first conceptualized, it has been leading the show in the field of data science. In the past few years, however, the popularity of R has increased exponentially – thanks to the advancement in data analytics environment. From data scientists to statisticians and researchers, R has become a hot-favorite for all. And, why not? It’s a GNU package and a free software package for statistical computing.

In this era of evolution, finding the best tool to stay ahead of the curve is the need of the hour. For that and more, we have selected R and given below are the points proving why R is the best programming tool in the competitive environment of data science.

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R is a substitute for data science for non-technical data enthusiasts

Well, if you are aware of leading data science trends and programming languages, you will find two high-end data science tools – R and Python – they tend to be the topic of conversation for all data-related matter. Python is a top of the line programming language for software professionals who have a knack in mathematics, statistics and machine learning, but lacks big time in offering library support on subjects like Econometrics and a bunch of communication tools, including reporting.

Most of the consultants working in the field of data science belongs from business community, and have no particular interest in technical know-how about developing software and acing programming languages. Learning python would not be as much of help as it would be mastering R programming – R is a programming language that supports libraries for stats, machine learning and data science. Thus, R is the best fit for data science enthusiasts not belonging from technical background. Also, R offers support packages or libraries for Econometrics, Finance, etc. – all of this is widely used for data analytics.

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After Tidyverse, mastering R is easy

Previously, learning R was no mean feat. It was considered one of the toughest languages to learn and largely inconsistent; the reasons being structuring and formality. But the things started to change when Tidyverse was introduced – it’s a robust set of packages and tools that offers steady structural programming interface.

In fact, after the launch of ‘dplyr’ and ‘ggplot2’, curve complexities got reduced even more. Just like any other languages, R went on getting better with its programming interface and achieving more structural and consistent – thanks to Tidyverse – it turned out to be efficient as it includes support packages for visualization, modeling, manipulation, iteration and communication – all of these turned R a super easy language to ace on.

R is mostly used for business purposes

The biggest advantage of R as compared to other programming languages is its capability to create industry-ready reports and infographics, and ML-powered web applications. For business-related matter, no other tool is as efficient as R.

But have you wondered what makes R so popular among the business community? It’s the two special R-enabled frameworks – RMARKDOWN and Shiny.

RMARKDOWN helps in developing reconstructable reports, which are regarded as the stepping stone for building blogs, websites, presentations, books and journals. On the other hand, Shiny is a powerful framework for creating interactive web applications for R. It is handy and widely popular.

DexLab Analytics offers leading R programming courses in Gurgaon for all the data enthusiasts. Check out the course itinerary and decide for yourself.

 

The blog has been sourced from – 

www.technotification.com/2018/06/r-programming-data-science.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”

Master These Piping Hot Data Analytics Techniques and Stay Ahead of the Curve [Video]

Big Data, Business Intelligence, Data Science – the digital revolution is here, and it’s evolving steadfastly.

 
Master These Piping Hot Data Analytics Techniques and Stay Ahead of the Curve [Video]
 

Soon, data analytics is becoming the life-source of IT. The range of technologies is varied, and the way data is expanding, we are fast moving towards a juncture where analysis of vast volumes of data will be done in a jiffy.

Continue reading “Master These Piping Hot Data Analytics Techniques and Stay Ahead of the Curve [Video]”

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”

We Feel Honored To Conduct Training for Mercer in R Programming

We are back again with some awesome news! DexLab Analytics is organizing a comprehensive one-week training program for super-efficient data analytics and big data team of Mercer – a top notch multinational corporation that provides top-of-the-line solutions in Talent, Retirement and Investments worldwide.

 
We Feel Honored To Conduct Training for Mercer in R Programming
 

The training module has started from Thursday, 21st September 2017 and our in-house senior consultants are imparting cutting edge technological knowledge about R Programming to the data-hungry Mercer professionals. The training is taking place at Mercer’s corporate in DLF Phase 3, Gurgaon Office: with the rising demand of data analytics and R skills to imbibe, the advancement in the field of health, wealth and careers is witnessing a steady growth. To target a larger audience and loyal clientele base, R Programming skills need to be harnessed properly so as to flourish the future of business on a larger scale.

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How to create Chart Templates with R Functions

R functions are used to produce chart templates to keep the look and feel of the reports intact.

 
How to create Chart Templates with R Functions
 

In this post you will come across how to create chart templates with R functions – all the R users should be accustomed to the calling functions so as to perform calculations and outline plots accurately. Remember what colors and fonts to use each time: R functions are used as a short-cut for producing customary-looking charts.

Continue reading “How to create Chart Templates with R Functions”

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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