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Category: R Programming

Are you keeping up with R? Here are a few free PDFs and online resources

Posted on by Dexlab

Chances are if you found your way into this blog, then you are very well familiar with R programming, it is an open-source statistical and data mining programming language. Though a relatively new id around the block it is slowly catching up to its other commercial counterparts like those of SAS and SPSS. Many data analyst even believe that R programming will eventually replace other paid languages that are currently of choice for data analysts for modelling purposes.

 

Are you keeping up with R? Here are a few free PDFs and online resources

 

Why so?

 

The main reason we believe is solely for commercial purposes. Many organizations are already questioning the massive amount of annual cost of other commercially available data analytics tools for their statement in the P&L statements. Furthermore this ongoing trend has escalated with the presence of R as a free and feasible replacement. Continue reading “Are you keeping up with R? Here are a few free PDFs and online resources”

What Makes A Data Analyst?

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Recently a world’s top magazine crowned the job role of Data Analyst as the sexiest job of the 21st Century. But there is still much confusion as to why this title is so much in demand in the job market presently and what makes it such a desirable gig. So, we would like to take this opportunity to describe in detail what the job role of data analysts entail, why is it so much in demand, what can an aspiring data analyst expect in this job, what are the payment packages and what the future holds for this breed of pseudo-scientists. Continue reading “What Makes A Data Analyst?”

How Predictive Analysis Can Be Used In Healthcare For The Better

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Medical predictive analysis is slowly being recognized as a system that if utilized well can completely change the very face of medicine and healthcare practices.

 

How Predictive Analysis Can Be Used In Healthcare For The Better

 

We have all been a patient at least once in our lives and there is a high likely that we will be so again. While some of us may require medical attention more frequently than others and some do not, but we have all been to the clinic at some point and we all desire the best of medical care. We believe that the doctors and technicians there are equipped to provide us with that and that there has been good research and understanding behind all their medical decisions. But that is often not the case. Continue reading “How Predictive Analysis Can Be Used In Healthcare For The Better”

Is Data an Asset or Liability

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While many firms are stating that they leverage their data to gain valuable insights and translate them into profit. But the basic question remains whether data is an asset or a liability. This is the mind-numbing question that haunts all IT managers and must be given ample concentration on what is collected in terms of data and how can it be managed efficiently.

 

Is data an asset or liability

 

There can be two approaches to answer this question, the first being that data could be an asset if used ethically and correctly. But when no actionable insight can be gathered from data, it is a liability in the same lines as an old non-performing loan. Optimum use of data is elemental to the operations of any data driven initiative. The main reason behind this data-drive remains to be to obtain faster and better decision making abilities with more accuracy. Nowadays organizations across the board leverage their data to achieve their goals. Currently sales organizations are the frontrunners who mine their data to get the best results and maximize their revenue from already customers. Also crediting companies use their data to evaluate the risks associated with different individual debtors and then act accordingly when setting rates and fees for their loans that seem to be fair based on this information. In these scenarios the companies use real information to make decisions.

Continue reading “Is Data an Asset or Liability”

The Limitation of R Programming

Posted on by Dexlab

R Programming is sort of the darling of the academia and researchers as well due to the cutting edge tools of data science and analytics that it offers. Not only does its open source nature ensure that contributors to the project are able to come out with packages that facilitates in making R Programming be able to sport the latest advances in its field but also make it a option that may be implemented with burning a hole in ones pockets.

 

The Limitation of R Programming

The Disadvantages of R

In spite of all its flexibility, R is found want in a number of specific situations. R cannot scale properly with large sets of data. There have been a number of efforts to overcome this significant disadvantage of R, but these efforts have not met with much success and the bottleneck remains an issue which needs to be dealt with seriously.

Continue reading “The Limitation of R Programming”

The evolution of Big Data in business decision making

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The evolution of Big Data in business decision making

Big Data is big. We have all established that, and now we know that all the noise about Big Data is not just hype but is reality. The data generated on earth is doubling in every 1.2 years and the mountainous heap of data keep streaming in from different sources with the increase in technology.

Let us look at some data to really understand how big, Big Data is growing:

  • The population of the world is 7 billion, and out of these 7 billion, 5.1 billion people use a smart phone device
  • On an average every day almost 11 billion texts are sent across the globe
  • 8 million videos are watched on YouTube alone
  • The global number of Google searches everyday is 5 billion

But the balance has long been tipped off as we have only been creating data but not consuming it enough for proper use. What we fail to realize is the fact that we are data agents, as we generate more than 25 quintillion bytes of data everyday through our daily online activities. The behaviors that add more numbers to this monstrous hill of data are – online communications, consumer transactions, online behavior, video streaming services and much more.

The numbers of 2012 suggest that world generated more than 2 Zetabytes of data. In simpler terms that is equal to 2 trillion gigabytes. What’s more alarming is the fact that by the year 2020, we will generate 35 trillions of data. To manage this growing amount of data we will need 10 times the servers we use now by 2020 and at least 50 times more data management systems and 75 times the files to manage it all.

The industry still is not prepared to handle such an explosion of data as 80 percent of this data is mainly unstructured data. Traditional statistical tools cannot handle this amount of data, as it is not only too big, but is also too complicated and unorganized to be analyzed with the limited functions offered by traditional statistical analysis tools.

In the realm of data analysts there are only 500 thousand computer scientists, but less than 3000 mathematicians. Thus, the talent pool required to effectively manage Big Data will fall short by at least 100 thousand minds prepared to untangle the complex knots of intertwined data hiding useful information.

But to truly harness the complete potential of Big Data we need more human resource and more tools. For finding value we need to mine all this data.

Then what is the solution to this even bigger problem of tackling Big Data? We need Big Data Analytics. This is more than just a new technological avenue, but on the contrary this is fresh new way of thinking about the company objectives and the strategies created to achieve them. True understanding of Big data will help organizations understand their customers. Big Data analytics is the answer behind where the hidden opportunities lie.

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A few advanced tools that are currently in use in the data analysis industry are: SAS, R Programming, Hadoop, Pig, Spark and Hive. SAS is slowly emerging to be an increasingly popular tool to handle data analysis problems, which is why SAS experts are highly in-demand in the job market presently. To learn more about Big Data training institutes follow our latest posts in DexLab Analytics.

 

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Python Vs R- Which You Want To Learn First

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Python Vs R- Which You Want To Learn First

If Big Data interests you as a career choice and you are pretty much aware of the skills you need in order to be proficient in this field, in all likelihood you must be aware that R and Python are two leading languages used for analyzing data. And in case you are not really sure as to learn which of the mentioned articles first, this post will help you in making that decision.

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In the field of analysis of data, R and Python both are free solutions that are easy to install and get started with. And it is normal for the layman to wonder which to learn first. But you may thank the heavens as both are excellent choices.

Let’s Make Visualizations Better In Python with Matplotlib – @Dexlabanalytics.

A recent poll on the most widely used programming languages for analytics and data science reveal the following:

Python Vs R- Which You Want To Learn First

 

Reasons to Choose R

R has an illustrious history that stretches for a considerable period of time. In addition you receive support from an active, dedicated and thriving community. That translates to the fact that you are more likely to be helped in case you are in need of some assistance or have any queries to resolve. In addition another factor that works in the favor of R is the abundance of packages that contribute greatly to increasing its functionality and make it more accessible which put R as one of the front runners to being the data science tool of choice. R works well with computer languages like Java, C and C++.

How to Parse Data with Python – @Dexlabanalytics.

In situations that call for heavy tasks in statistical analysis as well as creating graphics R programming is the tool that you want to turn to. In R, you are able to perform convoluted mathematical operations with surprising ease like matrix multiplication. And the array-centered syntax of the language make the process of translating the math into lines of code far easier which especially true of persons with little or no coding knowledge and experience.

Reasons to Opt for Python

In contrast to the specialized nature of R, Python is a programming language that serves general purposes and is able to perform a variety of tasks like munging data, engineering and wrangling data, building web applications and scraping websites amongst others. It is also the easier one to master among the two especially if you have learned an OOP or object-oriented programming language previously. In addition the Code written in Python is scalable and may be maintained with more robust code than it is possible in case of R.

The Choice Between SAS Vs. R Vs. Python: Which to Learn First? – @Dexlabanalytics.

Though the data packages available are not as large and comprehensive as R, Python when used in conjunction with tools like Numpy, Pandas, Scikit etc it comes pretty close to the comprehensive functionality of R. Python is also being adopted for tasks like statistical work of intermediate and basic complexity as well as machine learning.

 

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To learn more about Data Analyst with R Course – Enrol Now.
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To learn more about Machine Learning Using Python and Spark – Enrol Now.
To learn more about Data Analyst with SAS Course – Enrol Now.
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Role of R In Business Intelligence

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To put it simply Business Intelligence is the action of extracting and to derive information that may be of use from the available data. As might be evident the process is a broad one where the quality and the source of the data structure is variable. Transformations like this might in technical terms be described as ETL or extract, transform and load in addition to the presentation of information that is of use.

 

role of r in business intelligence

R Programming in Business Intelligence

Some R Programming Experts hold that R is fully able to take on the role of the engine for processes related to BI. Here we will focus only on the BI function of R i.e. to extract, transform load and present information and data. The following packages correspond to indicated processes in Business Intelligence.

 

Extract

 

Extraction

 

  •  RODBC
  • DBI
  • data.table’s fread
  • RJDBC

 


 

In addition to these, there are several other packages that support data in a variety of formats.

 

Transform

 

  • data.table
  • dplyr

 

Load

 

  • DBI
  • RODBC
  • RJDBC

 

Let’s Take Your Data Dreams to the Next Level

 

Prsentation

 

Presenting data is a wholly different ball game than the previously mentioned process of ETL. Never fear, it may be outsourced with ease to tools of BI dashboard with ease by populating the structure of data according to the expectations of the particular data tool. R is able to create a dashboard of a web app directly from within itself through packages like:

 

  •  shiny
  • httpuv
  • opencpu
  • rook

 

These packages let you play host to interactive web apps. They have the ability to query the data in an interactive manner and generate interactive plots. The basis for all of these is an R session engine and is able to execute all functions of R and may leverage the capabilities of statistics of all packages in R.

 

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Extras

 

The above mentioned packages serve as the core whose functionality may be simplified through the use of the packages mentioned below:

 

  • db.r
  • ETLUtils
  • Sqldf
  • Dplyr
  •  shinyBI
  • dwtools

 


 

The following factors are critical while R is adopted by businesses:

 

  • Extraction / Loading
  • Performance and scalability
  • Presentation
  • Support and licensing

 

For more details on R Programming, get yourself enrolled in superior R programming courses in Pune. R programming certification in Pune by DexLab Analytics is extremely popular.

 

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New R Packages- 5 Reasons for Data Scientists to Rejoice

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5-Reasons-for-Data-Scientists-to-Rejoice

One of the fundamental advantages of the ecosystem related to R and the primary reason that lie behind the phenomenal growth of R is the practice and facility to contribute new packages to R. When this is added to the highly stable CRAN which happens to be the primary repository of packages of R,gives it a great advantage. The effectiveness of CRAN is further enhanced by the ability of people with sufficient technical expertise and to contribute packages through a proper system of submission.

It is only with sufficient effort and time that one realizes the system of packages submitted through proper procedures can yield integrated software of high quality.Even those who are relatively new to R Programming the process of discovering the packages that serves as the bedrock of R language growth. Such packages add value to the language in a reliable way.

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The following 5 new packages listed in the paragraphs that follow may trigger the curiosity of data scientists.

  •  AzureML V0.1.1

Cloud computing is and will continue to be of great interest to all data scientists. The AzureML provides Python and R Programmers a rich environment for machine learning. If you are yet to be initiated to Azure as a user this package will go long ways in helping you get started. It provides functions that let you push R code from your local system to the Azure cloud in addition to publishing models and functions as web services.

  •  Distcomp V0.25.1

Using distributed computing when dealing with large sets of data is invariable an irksome problem. This is truer in cases where sharing data amongst collaborators is difficult or simply not possible. The distcomp package implements a crafty partial likelihood algorithm which lets users build statistical models of complexity and sophistication on data sets that are not aggregated.

  • RotationForest V0.1

If there is any primary ensemble method that performs well on diverse sets of data on a constant basis is the forests algorithm. This particular variety performs principal analysis of components on subsets taken at random in the feature space and holds great promise.

  • Rpca V0.2.3

In case there is a matrix that forms a superposition of a component that is lowly ranked along with a sparse component, rcpa calls in a robust PCA method that recovers all of these components. The algorithm was publicized by the data scientists at Netflix.

  •  SwarmSVM V0.1

One of the primary machine learning algorithm happens to be the support vector machine. SwarmSVM has for its basis an approach that may be said to be as a clustering approach and makes provisions for 3 different ensemble methods that train support vector machines. A practical introduction to this particular method is also attached with the vignette that comes with the package.

For more such interesting technical blogs and insights, follow us at DexLab Analytics. We are a pioneering R programming training institute. Our industry experts impart the best possible R programming courses, so when are you contacting us!!

 

Interested in a career in Data Analyst?

To learn more about Data Analyst with Advanced excel course – Enrol Now.
To learn more about Data Analyst with R Course – Enrol Now.
To learn more about Big Data Course – Enrol Now.

To learn more about Machine Learning Using Python and Spark – Enrol Now.
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