Tag Archives: R language training in Pune

R is Gaining Huge Prominence in Data Analytics: Explained Why

Why should you learn R?

Just because it is largely popular..

Is this reason enough for you?

Budding data analytics professionals look forward to learn R because they think by grasping R skills, they would be able to nab the core principles of data science: data visualization, machine learning and data manipulation.

Be careful, while selecting a language to learn. The language should be capacious enough to trigger all the above-mentioned areas and more. Being a data scientist, you would need tools to carry out all these tasks, along with having the resources to learn them in the desired language.

In short, fix your attention on process and technique and just not on the syntax – after all, you need to find out ways to discover insight in data, and for that you need to excel over these 3 core skills in data science and FYI – in R, it is easier to master these skills as compared to any other language.

Data Manipulation

As rightly put, more than 80% of work in data science is related to data manipulation. Data wrangling is very common; a regular data scientist spends a significant portion of his time working on data – he arranges data and puts them into a proper shape to boost future operational activities. 

In R, you will find some of the best data management tools – dplyr package in R makes data manipulation easier. Just ‘chain’ the standard dplyr together and see how drastically data manipulation turns out to be simple.

For R programming certification in Pune, drop by DexLab Analytics.

Data Visualization

One of the best data visualization tools, ggplot2 helps you get a better grip on syntax, while easing out the way you think about data visualization. Statistical visualizations are rooted in deep structure – they consist of a highly structured framework on which several data visualizations are created. Ggplot2 is also based on this system – learn ggplot2 and discover data visualization in a new way.

However, the moment you combine dplyr and ggplot2 together, through the chaining technology, deciphering new insights about your data becomes a piece of cake.

Machine Learning

For many, machine learning is the most important skill to develop but if you ask me, it takes time to ace it. Professionals, who are in this line of work takes years to fully understand the real workings of machine learning and implement it in the best way possible.

Stronger tools are needed time and often, especially when normal data exploration stops producing good results. R boasts of some of the most innovative tools and resources.

R is gaining popularity. It is becoming the lingua franca for data science, though there are several other high-end language programs, R is the one that is used most widely and extremely reliable. A large number of companies are putting their best bets on R – Digital natives like Google and Facebook both houses a large number of data scientists proficient in R. Revolution Analytics once stated, “R is also the tool of choice for data scientists at Microsoft, who apply machine learning to data from Bing, Azure, Office, and the Sales, Marketing and Finance departments.” Besides the tech giants, a wide array of medium-scale companies like Uber, Ford, HSBC and Trulia have also started recognizing the growing importance of R.

Now, if you want to learn more programming languages, you are good to go. To be clear, there is no single programming language that would solve all your data related problems, hence it’s better to set your hands in other languages to solve respective problems.

Consider Machine Learning Using Python; next to R, Python is the encompassing multi-purpose programming language all the data scientists should learn. Loaded with incredible visualization tools, machine learning techniques, Python is the second most useful language to learn. Grab a Python certification Pune today from DexLab Analytics. It will surely help your career move!

Interested in a career in Data Analyst?

To learn more about Machine Learning Using Python and Spark – click here.
To learn more about Data Analyst with Advanced excel course – click here.
To learn more about Data Analyst with SAS Course – click here.
To learn more about Data Analyst with R Course – click here.
To learn more about Big Data Course – click here.

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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.

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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.

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Analyze Smartphone Sensor Data with R and the BreakoutDetection Package

Analyze-Smartphone-Sensor-Data-with-R-and-the-BreakoutDetection-Package
 

Quite interetsing. Juggling with sensor data is starkly different from economics data, document processing or social networking, but very worthwhile. In this blog, we will take a practical approach to analyze smartphone sensor data with R. We are going to use the accelerometer smartphone data that Datarella presented in its Data Fiction competition. The dataset signifies the stimulation along the three axes of the smartphone:

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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.

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Debugging Magrittr Pipelines in R with Bizarro Pipe and Eager Assignment

Debugging Magrittr Pipelines in R with Bizarro Pipe and Eager Assignment

 

Pipes in R

Pipe, written as “%>%“ is basically an efficient operator, supplied by magrittr R package. The pipe operator is notably famous due to its wide range of use in dplyr and by the proficient dplyr users. The usage of pipe operator allows one to write “sin(5)” as “5 %>% sin“,  which is inspired by F#‘s pipe-forward operator “|>” and is further characterised by:

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How To Visualize Multivariate Relationships in Large Datasets in R Programming:

How To Visualize Multivariate Relationships in Large Datasets in R Programming:
 

In this post, we will discuss how to use the package nmle in R programming, which includes the dataset MathArchieve. To install the package and load it into your R programming environment, use the code mentioned below:

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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.

 
ANZ uses R programming for Credit Risk Analysis
 

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.

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How to Assess Clustering Tendency: Unsupervised Machine Learning

The meaning of clustering algorithms include partitioning methods (PAM, K-means, FANNY, CLARA etc) along with hierarchical clustering which are used to split the dataset into two groups or clusters of similar objects.

 
How To Assess Clustering Tendency: Unsupervised Machine Learning
 

A natural question that comes, before applying any clustering method on the dataset is:

 

Does the dataset comprise of any inherent clusters?

 

A big problem associated to this, in case of unsupervised machine learning is that clustering methods often return clusters even though the data does not include any clusters. Put in other words, if one blindly applies a clustering analysis on a dataset, it will divide the data into several clusters because that is precisely what they are supposed to do.

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