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The Dilemma of Data Science: SAS or R or Python

The Dilemma of Data Science: SAS or R or Python

For a lot of quantitative corporate personnel, the raging debate between the tools of choice for analytics has been known to cause some rival enthusiasm instead of the age-old political debates on Thanksgiving!

The SAS vs. R debate was already hotly underway for the past couple of years, but recently many analytics professionals and aspiring analysts have requested us to include a comparison of Python in our debates. So, we decided to keep things light and simple and only asked a single question – “which analytics tool do your prefer to use: SAS, R Programming or Python?”

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Gradually our survey results have been showing a growing demand for open source tools over the past few years. In fact so much so, that this year almost 61.3% of respondents in a survey conducted by KDnuggets chose R and Python over 38.6 percent of people still opting for SAS. As it is SAS is a great tool for large companies to conduct their data analytics.

Read Also: 13 Advantages of R over Excel in Data Analytics

Are you keen on learning more about these numbers? So were we, so tallied a few survey results and opinions of analytics professionals to determine which is the better data analytics tool to learn first. And here is what we found…

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Big Data Analytics is The New Nostradamus of The 21st Century


The debate of US elections did rage on full swing before the shocking results were obtained and Donald Trump was elected the president. There were numerous people tweeting, blogging, and updating other social media platforms with their thoughts, and opinions. But all these were based on the data offered by the data researchers who had a rich source of information about what were the general view of the people about this infamous electoral race.

The power of data analytics is such now that an associate Prof Stantic was so confident about the results that he publicly announced his predictions including for the swing states, and what is more his predictions were right about them as well.

He stated on the issue by saying – “my algorithms showed clearly that based on previous patterns and sentiments in the social media about Trump, by the end of the November 8th would take a massive lead despite it only being a 10 percent chance to win as per all the polling surveys at the time.

He further added, “That a day before that in a public address I was even able to pinpoint the exact states where Trump would win like Florida, Pennsylvania, and North Carolina. Someone in the audience even challenged me by going online and checking the data that Hillary was the favorite of 84 percent.

To that all I have to say is people are more likely to be honest about their preferences when telling their friends and family and not just answering polls. It is almost nerve wracking that completely accurate predictions can be made using just social media analysis.”

Even other Big Data analytics specialists have been able to correctly predict the outcomes of the US Presidential elections 2016 much ahead of time. Griffith’s Big Data and Smart Analytics Lab analyzed simple twitter comments during the end of July and were able to predict that if the elections had taken place at the time, Trump would have been the clear winner over Clinton. The results were even shared at that time in an article by The Conversation.

The same analytics lab was able to predict using similar methods and publically announced that the coalition would win over ALP at the Australian federal elections.

Over the past several years presidential and political elections have proved to be ideal test beds for the social media analytics, big data researchers and data analysts which can offer great details on how even the campaign gain more insight about potential voters and try and win over more. “These analytics methods can offer much better insights that simple telephone polling, especially at a time when landlines are barely in use and everybody has a caller-ID”, said professor Stantic.

“And that is why polls leading up to the elections had such an inconsistent outcome.”

He further went on to speak about Big Data by saying – “The amount of information or data we generate is a truly staggering one and that is continuing to grow. This publicly available data is secret treasure-trove of useful information, for those who know how to use it right.”

Professor Stantic further stated that Big Data analytics is a discipline that is faced with numerous challenges that comes with managing the sheer amount of data that no one has noticed. Similar predictions await us for a better and smarter world, about environmental changes of the Great Barrier Reef on Human sensors, gold coast visitor satisfaction etc. which have already been done on projects funded by the National Environmental Science Program and the City of Gold Coast.”

“We can further improve the predictive power of Big Data Analytics as there is a growing need for better, smarter and faster algorithms to perform deep learning on humungous volumes of data, that are being drawn from diverse areas and we are working on it.”

Thus it is evident that Big Data training if made feasible for the right hands can help change the world into a data-driven, smart-opia!

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.

How Big Data is Improving The Quality of Professional Sports


According to a Statista report, the global sports industry has crossed US$ 145.34 billion mark in 2015 in terms of revenue, thus making it one of the most valuable sectors. As professional sports are gaining more popularity, the leaders of the industry are feeling the need to improve its quality to ensure that the fans are delighted and at the same time, the future of the game can be sustained.

Here are different ways how Big Data is used for improving the quality of the game and the overall fan experience.

Avoiding injury                                                           

An injury is the biggest threat for a professional sportsman’s career. Several wearable products have been developed for collecting real time data from the athlete’s body. These devices can analyse these datasets to figure out the chances of injury that the athlete is exposed to, which can be used to take appropriate measures that may save the career of the star player.

Better fan experience

Fans are the single most important factor that decides whether any tournament will be successful or not. Fans are the lifeline of any professional sport and in order to enhance the sustainability of the sport, fans must be provided with additional benefits. Several technology firms are working together to initiate measures needed to improve the experience of sports fans while they are watching a live game in the stadium by allowing them to order foods from the stands, and detecting empty parking spots when they are entering the stadium.

Better on-field decisions

Sports Vision, a USA based sports technology company, has been using the latest innovations to make things easier for the match officials of different sports to take accurate decisions, which in turn may contribute largely to promote the quality of the game. The company has recently installed the ‘Pitch f/x’ technology across 30 stadiums that host games in the Major League Baseball in USA. According to the company, this particular technology would help the umpires to make judgements based on the real-time data gathered by the same.

Sports Vision operates across a wide array of different sports other than baseball.

Let us look into the biggest mistakes committed by the match officials during a live game. These unintended mistakes later proved to be the decider of the match result.

Case study: The biggest mistakes in sports

Every sport lover wants a game to be error free and the decisions taken by the officials must be flawless. But in many cases, the situation has not been desirable. One such controversy was seen during India’s tour of Australia in 2008, when these two teams locked horns on Day 5 of the second test at Sydney on January 6, 2008.

Former Indian skipper Sourav Ganguly had just scored his 2nd consecutive half century in the match and was looking dangerous for the mighty Australians. India needed 200 more runs to win the test, which seemed possible with Ganguly and Rahul (Dravid) on the crease. Brett Lee was in charge with the ball and he was running fiercely towards the in-form batsman. He delivered at nearly 145 kilometres per hour, which kissed the edge of Ganguly’s blade, only to reach the second slip. The Australian vice-captain Michael Clarke was fielding in the slip position who made an excellent effort to reach towards the ball and complete the catch.


The umpire was not sure whether the batsman was out or not, so he asked Ricky Ponting- the Australian captain at that time, about the result. Ponting confirmed that the ball carried to the slip and Clarke did no mistake to take the catch. As a result, the umpire Mark Benson declared Ganguly out. Nevertheless, India’s most successful test captain was sure that he was not out and later the TV replay showed that the ball actually dropped on the ground before Clarke caught it. Later in that innings, three of the other Indian batsmen including Dravid, Tendulkar and VVS Laxman, also fell prey to the wrong judgements by the umpires. India was finally all out for 210 and lost the match by 122 runs.

The hand of God


Wrong decisions are not familiar in only cricket, but are also witnessed in other sports as well. Can you recall the event when the Argentinean football legend Diego Maradona scored a goal using his hand? The referees were unable to detect the handball even when the TV camera captured the event.  This goal destroyed England’s World Cup dreams of 1986 and allowed Argentina to proceed to the next round. Later that week, Argentina went on to lift the FIFA Football World Cup for the second time.

Technology at the rescue

Thankfully, we are living in an era when technology is more advanced than ever. The advent of big data certification courses have proved to be an important event for various sectors, as more professionals are entering the field of business intelligence in order to make impactful decisions that can change the world. More companies are coming forward to contribute to the welfare of the world of sports. We hope that innovations are made in order to transform each professional sport into an excellent campaign.


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.

Big Data’s Evolution in Business Decision Making

Big Data's Evolution in Business Decision Making

We have all established that Big Data is big and all the noise about Big Data is not just hype but reality. With the increase in technology the data generated on Earth is doubling in every 40 months and huge heaps of data keeps coming in from multiple sources. Let’s look at some data to really understand how Big Data is evolving:

  1. The population of the world is 7 billion, and out of these 7 billion, 5.1 billion people use a smart phone device.
  2. On an average everyday almost 11 billion texts are sent across the globe.
  3. The global number of Google searches everyday is 5 billion

But there is an imbalance as we have been creating data but not consuming it enough for proper use. We generate 25 quintillion bytes of data daily through our regular online activities including online communications, online behaviour, video streaming services and much more.

Studies carried out in 2012 showed that the world generated more than 2 zetabytes of data which is roughly equal to 2 trillion gigabytes. By the year 2020, we will generate 35 trillions of data and to manage this growing amount of data we will need 10 times the servers we use now and at least 50 times more data management systems and 75 times the files to manage it all.

The industry is still not equipped to handle such an explosion of data as 80% of it is unstructured data. It is beyond the scope of traditional statistical analysis tools to handle this amount of data as it is too complicated and unorganized.

The talent pool required to effectively manage Big Data will fall short by at least 100 thousand minds as there are only 500 thousand computer scientists but less than 3000 mathematicians. But to truly utilize the complete potential of Big Data we need more human resource and more tools.

The solution to tackle this even bigger problem of Big Data is Big Data Analytics. It is fresh new way of thinking about the company objectives and the strategies created to achieve them. Big Data analytics is the answer behind where the hidden opportunities lie.

SAS, R programming , Hadoop, Pig, Spark and Hive are a few advanced tools that are currently in use in the data analysis industry. SAS experts are higly in demand in the job market recently as it is slowly emerging to be an increasingly popular tool to handle data analysis problems. To learn more about SAS training institutes follow our latest posts in DexLab Analytics.

For more information please read our blog at


How R is Used in Education? A Survey Report

HOW R IS USED IN EDUCATION A SURVEY REPORTThe fun fact with R is that it first originated in academia, the creators of R Programming Ross Ihaka and Robert Gentlemen developed this programming language at the University of Auckland in New Zealand and it has been widely used in graduate programs ever since. In programs that require that include strong statistical analysis. This programming language has often been used in MOOCs i.e. Massive Open Online Courses. In fact this programming language is extensively used in graduate educational programs that involve crunching data and students of statistics will encounter R in their academic life. And like everything else that is exposed to students in schools, R will naturally also be widely adopted for industrial use as well. As R is widely used in higher education, thus it is evident that its demand will increase in business and this is the reason why people who miss the R train in college often seek, R Programming Online Training programs like the one from DexLab Analytics.

Why drive for adoption of technology?

While technology makes things easier for us and could be deemed as fun, but then again most us who use technology also do it for a living. To the advantage of R users it is not only a pleasure to use this software but also due to its high demand in business it is also hugely profitable with fat checks for those who are well-versed.

The survey conducted by Dice Technology Salary Survey suggested that R is the highest paying skill as of last year. In a recent survey conducted by O’Reilly Data Science Salary Survey also put R as one of the most used statistical tools by the highest paid data scientists.

R has a diverse community:

The professionals working with R come from a diverse range of backgrounds; the list consists of scientists, academics, business analysts, statisticians and professional programmers. The diversity can be well perceived in the packages maintained by the community CRAN (Comprehensive R Archive Network) which brings the colorful backgrounds of the community members to the forefront.

The packages available with R can take care of several types of tasks like – creating maps, stock market analysis, high throughput genomic analysis, usual language processing. Moreover, people can get access to all the latest R-based news, from R Bloggers, which is a blog aggregation site which serves as a hub for latest news and updates related to R.

R is easy to use:

Many people get drawn to R due to its ease of use. One can generate complex charts and maps in R with only a few lines of code. This is an advantage of using R as other languages will require several lines of codes to complete these tasks. Though the popular notion about this software is that it is quirky, but it has several powerful features especially geared towards Data Analysis.

For more news and updates on R programming and details about the best R Programming Online Training programs stay hooked to our daily posts.

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Installation Guide from DexLab Analytics

Dexlab Analytics presents a handy installation guide to all aspiring data analysts to test their hands on Hadoop ecosystems. It only works in a Linux environment and hence, can be tricky to handle. This step-by-step guide will help you through to get this useful software installed in your computer and to start making sense of all the chaos surrounding data.

Commonly Found Dashboard Mistakes in Excel


There are a number of mistakes commonly made by MS Excel users in their dashboards in the course of work conducted on behalf of organizations as we will elaborate through this blog post. So, today we have in store for you- Commonly found dashboard mistakes in MS Excel.

  • Data Dumping

By data dumping the practice of placing all data on a dashboard because either you are unsure of what is truly wanted or just with the purpose of keeping all of the people involved happy. Regardless of the reason behind the act, this usually results in a mess that only manages to confuse. Take this as a rule of thumb; dashboards that try to be everything to everyone end up being a total failure if not exactly a total disaster.

  • Improper Visualization of Data

It is not rare to find people who are convinced by vendors dealing in Business Intelligence that 3-dimensional charts, especially of the flashy kind, is exactly what was needed to save a business but research in data visualization is suggestive that they might do more harm than good. The option of sticking with charts that are already familiar to us is a better one than going for options that appear to be self-indulgent and complicated to say the very least. Remember you are not here to do marketing and that your dashboard does not need to be glitzy, flashy, manipulative or go over the top in order to be effective.

  • Do Not Place Too Much Emphasis on the Technology

It is so common to find scenarios where businesses begin dashboard projects only to find themselves spending 80% of their time and energy, two valuable resources, on conducting research on the integration of new BI platforms and only the rest 20% on creating the dashboard itself. There is a need for the numbers to be the reverse. Remember that Dashboards are required from the point of view of business and is not about demonstrating technology. And logically when we treat developing them like if it is a technological project we run the risk of encountering overruns of schedules which are all too familiar with departments that are dealing with Information Technology.

In case you wish to know more about this changing and exciting topic, you will do yourself a world of good by enrolling for the best advanced excel VBA institute that is near you.

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Machine Learning for Newbies

Machine Learning has slowly become a part and parcel of our lives without most of knowing anything about it. It is the element that drives the smartness of smart devices that have become an integral part of our lives. If this is topic that arouses your curiosity then you simply must watch this video.

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Packages That make Life Easier for R Users


When you use R, at first the going is slow. The syntax is not all that intuitive and is quite tricky too and it takes time for person to feel settled within the environment and get accustomed to the finer nuances of the language. If one is new to R, he or she might miss out on the vibrant community that revolves around R and the available packages available that go towards adding to the diverse uses of the program.

R, sometimes, tends to be a bit obscure and prickly when compared to other languages like Java or Python. But the boon of availability of loads of packages that add to its functionality and even create a familiar and simple interface lying on top of Base R. Today we take a look at ten packages that make life easier for R Programmers.

  • sqldf

The syntax R is perhaps the hardest part of the R learning curve and it takes a while to get used to <- over = and other nuances of the R Programming language. R excels at munching data but mastering it has a steep learning curve. What sqldf lets you do is to perform SQL queries on the data frames of R. It is familiar to users migrating from SAS and should present no trouble to anyone with basic skills in SQL. Sqldf makes use of the SQLite syntax.

  • forecast

forecast is the library r users most often turn to while making a time series analysis. With forecast it is very easy to fit time series models like ARMA, ARIMA, AR, Exponential Smoothing amongst others. The forecast plot is a long standing feature endeared by forecast users.

  • plyr

The plyr feature of R lets you perform data manipulation, the smart way. When you want to call a particular function on each of the elements of a vector or list you want to turn to the apply function family. The plyr package is a good substitute for the functionality resulting from the combination of split, combine and apply functions in Base R.

You get a whole set of functions namely daply,ddply, adply, dlply and ldply which share a common blueprint- Split the structure of data into groups, apply them to each group and finally return the results in a proper data structure.

  • stringr

Many users complain the string functionality of R to be tedious and highly difficult to use. Here also stringr, a package written by Hadley Wickham provides an R string operator that was long overdue. In stark contrast to Base R, stringr is really easy to use. All functions have the prefix of ‘str’ and remembering them is really easy.

  • ggplot2

Yet another package from Hadley Wickham and probably the one that is most well known, ggplot2 is one of the most favorite packages in R. It is characterized by its ease of use and outputs some stunning plots. ggplot2 provides you with the best way with which you want to present your work.

These are just some of the packages that make it easy to work with R. You will surely find more with the progression of time and your continued involvement with the R World.

And if you are serious about making R the passion that fast forward your career then R Analytics Certification is highly recommended.

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The Hottest Job Locations for Data Analysis Personnel are:

The current salary trends of analytics personnel

In India the hottest job locations for a data analyst position according to our pay-scale and job scenario survey are – Gurgaon, Mumbai and Bangalore. For more details on payment packages on offer for various data analysis positions view our infographic with numbers based on industry-based survey.

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