It is quite a gamble these days when as a part of your travel itinerary you have to book the airline tickets. Most people in recent times have given up the traditional travel agents for booking tickets and prefer booking tickets themselves. Booking airline tickets has become a massive decision making activity, where you have to do all your thinking hats, to make an informed decision. Should I buy now, or wait for a better rate, or should I use the price trend forecast to get recommendations on when to book so that huge fluctuations can be avoided.
Post regulation, airline industry, around 1980 – 2001, was a time of price wars, fluctuating fuel prices, and a general increase in the number of passengers opting to fly, along with great technological advances, between the handful of airline providers. Airlines with conservative overheads could offer low airfares as a blanket to all its customers. But in the long run, could not sustain on low margins and had to shut shop. On the other hand, few airlines, used a scientific and analytical approach in offering reduced pricing over a certain sector, during a particular period and maintained regular premium cost airfares for the remaining slots, and with this approach not only did they survive but also accounted profit.
This article will help you understand how could they do that….
The low airfares could be offered by airlines, by following a dynamic pricing mechanism for tickets, based on the demand and supply trend.
Predictive analytics takes stock of what happened in the past, that is historical data, and the forecasts are done by using scientific methods, and statistical models for reliable information, about possible outcomes in the future. Predictive analysis is most accurate because it tries to understand the behaviour of a person or a customer, by not only looking at the past information, but is able to predict what they will also do in the future based on the customer, and about that entire population. Thus by grouping people in segments, predictive analysis will be able to give predictions and recommendations on how people will react in the future.
Predictive Analytics has continued to factor essentially into how airfare prices are decided and fluctuate to maintain airline profitability even today. It is mainly because the airline industry is getting very competitive and is a low margin industry. The ability to forecast travel demand is crucial to gain market share. Predictive analysis tools are no longer the luxury of airlines selling tickets, travel booking sites offering Price Predictors Features, can now be used by consumers to see and make decisions for their own projection. Price predicting feature will help them decide whether the price of a particular route is likely to fluctuate, higher or lower, in the coming weeks.
Keeping in line with the trend Indian government is contemplating on introducing an analytical tool which will present the travellers with future ticket price trends, similar to the travel booking sites which offer price predicting features.
This is a positive step towards addressing the travellers concerns of facing steep fluctuation in airfares. Of course, this proposal is subject to sanction from the civil aviation industry. Since air ticket prices are dependent on supply and demand metrics, this step from the aviation industry will get some amount of transparency in the entire process.
This endeavour is crucial and a part of the digitization wave, under the ‘Digiyatra’ Initiative. Since India’s Domestic aviation market is projected to be the world’s 3rd largest by 2022, it is only fair that the industry uses analytical tools to provide a digitally unified flying experience to its travellers.
Using ‘Historical Data Analysis’ along with ‘Price Curves with Predictive Data Analytics’ will help travellers better project and plan their vacations, so as to gain maximum advantage over airfares.
In conclusion, it could be said that predictive analysis is touching our lives positively, in ways that we might not be aware, and the penetration is only going to increase with time.
To learn more about Analytics watch this space until next week for the big news!
People who wish to embark or make a career shift in coding, or have a passion for coding always find themselves standing at crossroads with the dilemma of choosing the most appropriate programming language. Coding is an essential skill set for any data scientist or a professional working in the data analysis field. SAS often comes as the most preferred option in programming languages. Specially, if you are beginning your career, then this is the most obvious and logical thought to have.
Is SAS the right language to learn? Is knowledge in SAS enough to start your career in data science and coding? SAS v/s R or Python which is best? few questions you should have answers to.
As a general answer, there is no language in programming that can be termed as ‘Best’ purely because it is knowledge that you are acquiring, the same knowledge can be transferred.
All programming languages are good and developmental. Learning a programing language can be compared to driving, if you take the analogy further, you learn driving a particular car, but later you can apply the same skill while driving a truck, or a tractor, or an automatic vehicle, left hand or a right-hand drive. In the same way, all programming languages implement, Input, Output, Variables, Loops, Conditions, and Functions.
Learning one language will make learning the other one simpler, it is majorly only a different Syntax.
SAS is popular and can be considered as the undisputed market leader in the enterprise analytics scenario, it has a good GUI hence learning becomes fairly structured and easier. It has a good array of statistical functions and also offers great technical support.
SAS is majorly popular in established organisations because they are synonymous with great customer support and service. SAS is an expensive tool, therefore in the financial sector, where a budget is not a concern, it is usually the preferred option.
For these reasons SAS is still considered a leader in the coding sector by dominating 80% of market and R and Python together at 20%, they are open source languages.
However, on conducting a survey with over 1000 quantitative professionals, on mapping their preference in programing language, only 39% of them voted for SAS, while 42% for R and the rest 20% for Python.
Retail, Marketing and majorly Healthcare, Pharma and Financial services are loyal to SAS, while Telecom and the Corporate Start-up sector, swings towards R and Python. These sectors have large volumes of unstructured data, machine learning techniques need to be applied, for which R and Python are more suitable.
Cost of learning is also one of the factors while making an informed decision. SAS is very expensive when compared to other languages, so unless your company is assisting you in training, on an individual level it’s a costly affair. Although expensive SAS is fairly easy to learn, plus you do not need any prior knowledge in programming, basic SQL knowledge is good enough.
With SAS it will be easy for you to get a job, it is a fourth generation language, it relies on user-written programs, that when requested know what to do. Based on your needs and interests, it can be said that SAS is versatile and flexible with a variety of input and output formats. SAS has an electronic network where resources are available and one can get connected to share knowledge.
One needs to base their decision on personal factors, if your dream is to join a start-up or the telecom sector, then perhaps R or Python should be your choice. If you want to join the financial sector or venture in healthcare, then maybe SAS should be your first preference before learning any other languages.
Like many great debates that run over centauries, comparison of SAS with other programming languages, discussing their pros and cons, is a common and continuous process. It is a point of consideration between analytics for long now, on the language of choice, SAS or Python or R.
The technological advancements are so dynamic, that this debate can take place every couple of years and get answers that can sway on either sides. In this blog, we will discuss the global trends and the ecosystem of SAS, by itself of what SAS offers, and about the advantages of knowing SAS as a language.
Commercial analytics has always seen a strong presence of SAS as SAS offers a huge collection of statistical functions. SAS has a good support system to aid quick adaptation of the language, SAS provides excellent technical support. The only areas that work against SAS is the cost, it is the most expensive option and it lags in terms of latest statistical functions, when compared with other languages like R and Python.
To make an informed decision about SAS being the best programming language, let’s understand all the attributes of the language.
In terms of Convenience and Price, lets accept that SAS is a commercial software, hence it is expensive and not very affordable for most of the professionals. So unless you are associated with an establish institution which has invested in SAS, it might be difficult for you to lay your hands on SAS.
SAS is comparatively Easy to Learn, precisely for analysts with SQL knowledge. Like mentioned above it has a good support system, with tutorials and comprehensive documentation, but they are costly when compared to other programming languages, which are also known for some amount of simplicity. However, the GUI interface of SAS is very stable.
Data Handling Capabilities of SAS used to be a USP of SAS till a while ago. But on recent comparisons specially with R and Python, it can be easily said that this is no longer the case.
The Graphical Capabilities of SAS are good and can be considered to be only functional, any customisations require great understanding of the SAS Graph Package, and even then customizing on SAS is difficult. A little disadvantage when compared with other languages.
Developments in Tools of SAS are more or less at par with other languages. Other open languages have new version roll outs, on open contributions, hence the chances of error are possible. SAS also releases updates and they are well tested.
SAS is still considered the market leader in the job scenario in most established corporations. R and Python along with other programming languages becomes a preferred option for new companies looking for cost efficiency.
To conclude, yes it looks like the market is opening up more to other programming languages as well. So it completely depends on your conditions. If you are a fresher, it is recommended that you learn SAS as a first language, purely because it holds a high market share of jobs and is fairly easy to learn. If you are a veteran in the analytics world, then diversifying and adapting a new programming language is recommended. After all, knowing more than one language only adds to the flexibility and opens that many opportunities for you.
The revolutionary transition of big data from, just an experimental mode, to accounting for record breaking revenues for e-giants like Google, Ebay, Snapchat and so on; had made big data the biggest key aspect of marketing today. This has gotten almost everyone into a flurry of activity, to try and master the various data analytics tools that allow the conversion of plain, logical data into value based insights for business expansion. The various data analytics tools include SAS Programming, R Programming, Big Data Hadoop, Python and so on. As almost everyone tries to grapple with the knowledge of using these data analytics tools, Imarticus Learning is becoming very sought after for their tailor made, industry relevant training programs. SAS Programming is one certification that is highly recommended by all the experts in the Data Science Industry, mainly due to the fact that its been out there, for quite a long time now.
The SAS, also known as the Statistical Analysis System is basically, a collection of software products, offered by the SAS Institute. These offerings primarily enable the user to perform a wide variety of tasks that cover almost every aspect of the business landscape. Working as a one stop shop location for performing a host of activities, is what makes for the most prominent advantage of a SAS Certification. The SAS programming tool helps a professional, to perform a host of functions from, information system support, and customer care products to even human resources management. This makes studying for the SAS Certification all the more worthwhile, mainly due to its wide scope of applications.
The amount of data generated every single day, is well beyond the efforts that are being made to analyse it. With almost every firm and company out there, regardless of what field they belong to, going out and trying to mine as much data as possible, the stakes of ensuring your data is transformed into asset-like decisions has almost become a norm today. In such an age of super competition and the rapid increase of the amount of data being generated, it becomes only logical to take the help of the most experienced technology. This is what makes having a SAS Certification extremely beneficial to one’s career. SAS has literally been ruling the roost for a long while now, where most of the companies have it as they default software. While having this certification under your belt will give you great rewards in the IT industry, it can also serve as a great pay-giver on the corporate side of the economy.
The IT Industry has already recognized the universal value of SAS and hence has begun to demand for skilled employees in this particular data analytics tool. As most of the companies have this as their default software, hiring a professional who is well versed with it, is extremely beneficial to them. Thus by extension, if you are an asset to the company, chances of your development are a lot more as well. These advantages of SAS are basically the reason why this data analytics tool has gained a lot of takers in the recent years.