Statistical analysis systems came into the picture in the late 1970s and provided a new direction to research in the fields of data analysis, statistics, and business intelligence. The tools provided in this suite are of pivotal importance to data scientists around the world. A report by Statista notes that the number of jobs that would rely on SAS training would increase by 44% in the next 10 years. This also draws our attention to the fact that there would be new updates to the various components of SAS in the next few years.
The scope of SAS: Numbers don’t lie
The first occupation that we take up is data mining analyst. There are about 11 percent of job postings that require SAS skills. The occupational growth projected is about 10% in the next five years. Next in line is the occupation of a data scientist. The number of jobs requiring SAS skills would be around 79% and the occupational growth projected in the next 10 years would be about 39%. When it comes to the jobs relating to different types of merchandise, there would be about 4% of job postings requiring SAS skills. The occupational growth projected would be around 3%. Another important profession in this category is that of software developer and software engineer. The number of job postings requiring SAS training would be around 10% and the occupational growth projected would be around 41%. Similarly, the occupations of a business analyst, statistician, and social science researcher would witness an occupational growth of 34% in the next 10 years.
The milestones
The knowledge about statistical analysis systems can be acquired both in the online and offline format. Of late, there have been a number of colleges that have added various tools of SAS in their curriculum. This is because SAS is becoming extremely important from the point of view of machine learning and data science. Both these domains heavily rely on SAS training for different application purposes. Other types of data-driven platforms that are powered by artificial intelligence required the skills of SAS for deriving operational insights.
There are three important milestones when it comes to the acquisition of knowledge about SAS systems. The first milestone is provided by the official SAS website which contains various details about tools and techniques relating to the software suite. The second milestone is that of academic institutions like the University Of California that offer different types of courses in SAS. There is also a hybrid platform like the learning resources provided by Udemy which acts as the third milestone when it comes to SAS.
A blend of simplicity and complexity
SAS is neither easy nor very difficult to learn. It provides a great platform for solving a complex array of problematic issues. The popularity of SAS is on account of its applications in sectors like banking, logistics, and retail. These factors utilize the different tools and techniques of SAS to derive insights from unstructured and segregated data sets. It needs to be noted at this point in time that the programming language of SAS is constructed on base SAS which has a lot of similarities when we compare it with SQL. Other important tools provided by statistical analysis systems are related to visualization techniques. The different visual analytics tools provided by SAS include drag and drop features which enable the users to draw different graphics even without the use of programming language. However, adequate knowledge of calculus and statistics including machine learning is important for gaining deep insights into SAS.
The training duration
Statistically speaking, the training of SAS is limited to few days for very simple topics and few weeks for relatively complex ones. In order to gain a competitive edge in the working language, tools, techniques, and methodology of SAS, a time period of several months are crucial. A complete training course with professional skills and complex business challenge spans for more than 2 months. It needs to be noted that SAS is not a different learning platform altogether. The fundamentals of statistical analysis systems are in consonance with the domains of machine learning, Artificial Intelligence, and data science. When it comes to sector-specific training, an additional time period may be required to gain specialized skills. This means that SAS may prove very helpful to solve complex challenges in the domains of medicine and finance. Of late, it has been observed that these two domains are increasing their reliance on SAS for better technological output.
Concluding remarks
For professionals who are already equipped with the knowledge of python, it would be very easy to understand and specialize in SAS. For amateurs, it would not be very difficult either.
