Initially developed for analysing statistics in the 1960s, SAS programming has come a long way since then. From statistical analysis to the graphical representation of data, you can learn SAS to customise it to your field.
Whether you’re into business analysis or medicinal research, SAS can handle huge sets of data and display it in a readable format. So, should you learn SAS?
Check these fifteen stats about SAS programming to find out –
1) SAS is a programming language
While SAS offers a GUI (short for – Graphical User Interface), at its core, it’s a fourth-generation programming language. But it’s designed to reduce the time and efforts you need in analytics.
2) SAS programs only have two kinds of steps
The SAS programs have a combination of only two types of steps – DATA (to manage the data) and PROC (to process and present the data).
3) SAS is the most used tool
It’s the most used tool when it comes to data analysis and business decision making. In fact, SAS programming platform holds about 30.5 per cent of the market share of advanced and predictive analytics. It’s more than double the market share of its nearest competitor.
4) The fifth largest player in Business Intelligence software
SAS is the largest independent software vendor for Business Intelligence software products. With 6.9% market share, the SAS programming language is the fifth largest.
5) 42.6 market share in Health Research
As per a 2011 study, 42.6 per cent of data analysis in health was done with the SAS programming software.
6) SAS reinvests 26% of revenue
Investment in R&D by SAS is more than double the industry average of 12.5%. It invests 26% of its revenue. One of the reasons why you should learn SAS and keep yourself updated.
7) $60 million donations to non-profits
In the US, the company donated more than $59 million worth of software, hardware and training among other things in 2017. And then, there was $1.3 million in cash.
8) Joined 30,000 organisations for volunteering
In 2017, SAS employees in 16 countries volunteered for almost 30,000 organisations as the #GivingTuesday initiative. They raised $300 million in the process.
9) SAS programming software can read other statistical files
If you learn SAS, you can also handle data files created by other packages like SPSS, Excel, Systat and incorporate those in the present system.
10) More than 200 components present
From basic procedures to graphics, data mining, quality control, the SAS software suite has over 200 components.
11) Free learning resources in more than 120 countries
More than 3 million teachers and students access online resources to get prepared for a technology-driven workforce.
12) 65 Analytics Degree Programs worldwide
With the aim to help people learn SAS and other analytical skills, the company has helped to launch 72 masters and undergraduate courses. There also are 172 certification programs.
13) 4000+ people got trained in using analytics
During the guest lectures to universities and various colleges in 2017, more than 4000 students and professors learnt their way into analytics.
14) SAS is more than 40 years old
From its initial release in 1976, SAS programming has been present in the market for 42 years now. One more reason to learn SAS and trust its analysis.
15) Free educational software
By 2017, there were 1.45 million registrations on its free SAS University Edition and SAS OnDemand for Academics software.
16) SAS is easy-to-use
The point-and-click interface of SAS – the SAS Enterprise Guide generates codes to analyse data without the need of programming experience.
Even though with so many features, it’s easy to learn SAS and then, use it for various analysis.
Day: January 11, 2018
What are The Top 4 Roles To Data Analyst Look Out For?
What is the job of a data engineer? How is it different from the role of a data analyst? What does a data scientist do? Confused? Let us put all the different aspect of data analytics into different buckets and make it all more manageable for you.
If you are looking to migrate to data analytics from any other branch (technical and non-technical), then read this article till the end to comprehend the different choices available for you. Please go through this article even if you belong to the field and are looking for options to upgrade your career and earn some better pay from the area itself. The field of data analytics is a continually changing one, and the roles mentioned here are defined loosely.
There are four significant roles under data analyst responsibilities –
Data Engineer
A data engineer is the one who designs the platform and the structure which gathers and stores all the data from the users such as what item they are buying from an online store and what are the contents of their cart currently as well as on their list. Data engineers ensure that all the data are stored efficiently and are easily retrievable.
Data engineers are adept at working with a range of sources and framing ETL queries to collect data from them quickly. They then organise all the data in databases for the companies and individuals or others to use it from there.
You will need to acquire knowledge of various languages such as Java, Python, C++, SQL, Hadoop, Spark, Ruby, etc. on top of data analytics certification. It should be noted however that there is no need to learn all these languages as the requirement varies from company-to-company.
Being a data engineer provides you with the rare opportunity to work as both a software engineer and a data analyst.
Data Analyst
Data analytics role expects you to prepare insights from the available data which directly impacts decisions in businesses. There is direct involvement of data analysts in everyday business activities. A data analyst or business analyst is expected to perform a significant amount of ad hoc analysis every day. For instance, data analysts help an e-commerce’s marketing team to identify the customer segments for the marketing or the ideal time to market any product or the reasons of the failure of the marketing campaign and how to avoid the mistakes in future. A good understanding of business, statistics and data manipulation is thus required in a data analyst.
The languages and tools which are required to be known by a data analyst include SQL, R, Excel and Tableau for some cases.
Data Visualizer
Every kind of company or organisation in today’s time possess a data visualizer or a business intelligence professional who is/are responsible for creating and presenting weekly insights and chart boards informing the management about the various metrics. The metrics may include the weekly sales of products manufactured by the company, the average time required for the delivery, or the total number of cancellations of orders and the reason for them.
Data Scientist
A data scientist is a person who utilises the data in possession of the organisation to design business-oriented learning models and types of machinery.
For instance, data scientists go through all the available data of the company and look at the buying patterns of the consumers, identify the favourite items and more frequent users. Then they design algorithms based on that to automatically recommend the more popular products to frequent buyers with their navigation histories, purchasing histories or other similar parameters.
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