SAS Visual Modeler has confirmed the ability to build and explore predictive and descriptive models, assess models, compare models, and score models using SAS Visual Statistics.
From the organization aspect, SAS skills are useful in many departments. With a Bachelor’s degree, jobs like data management and SAS programming are a good start. The position enables you to be familiar with the data and structure, access and process data, and make tables, listings, and graphics. With advanced degrees in master or Ph.D. in statistics or related field, there are more chances to be selected for statistician roles.
SAS Certified Visual Modeler Using SAS Visual Statistics 7.4 Exam
SAS Visual Modeler comes with an incredibly broad set of modern machine learning, deep learning, and text analytics algorithms that are all available within a single environment. This makes the solution ideal for all sorts of business users.
The solution’s diverse analytics capabilities include clustering, different modes of regression, random forests, gradient promoting models, support vector machines, natural language processing, and topic detection, to name a few. SAS users not only gain entrance to a highly functional platform but one that is provided with necessary predictive and decision-making capabilities that were previously limited.
SAS Interactive Model Building and Exploration Using SAS Visual Statistics 7.4 is not limited to big data environments. It is equally essential for departmental deployments or midsize businesses operating on different platforms, including database appliances from Pivotal and Teradata, Hadoop distributions from Cloudera and Hortonworks, or departmental servers.
SAS Visual Statistics adds a flexible predictive modeling environment for analytical professionals. They can quickly query, compile, filter, and visualize data and create, compare, and deploy the best predictive models. Users can choose between a visual point and click interface, the SAS Studio programming interface, or even use open source languages, including Python, R, Java, and Lua, to obtain powerful SAS statistical modeling and machine-learning techniques.
Success is often the sum of past failures, but because the margin for error is shrinking, fast model refinement is significant. The ability to build and fine-tune models repeatedly until suitable results are crucial to getting more value from your data. Models can be improved by applying new techniques to data that already exist or using variables that have never been used for analytical purposes. Analytical professionals can try new ways to test multiple scenarios and evaluate results, reducing uncertainty and risk.
With integrated model building and visual data research, analysts and data scientists can maintain an uninterrupted workflow, cycling quickly between hypotheses and verification. These capabilities boost model development, productivity, and accuracy of results in near-real-time.
After trying different modeling techniques and analyzing results, they can easily collaborate with business analysts from the same situation. Once the champion model is identified, it can be put into production by setting it into operational applications and scoring new data to produce faster penetrations.
You can teach yourself SAS Visual Modeler if you have a way to the tools you are trying to learn. Most programs have introductory tutorials, and there are many prints and online resources for learning.
Is SAS Visual Modeler Worth Learning?
SAS Visual Modeler is worth learning if you are interested in analytics. It is the best tool to learn for medical and finance fields because it is widely used in those industries. For other sectors, it may be better as a secondary choice to learning Python or R, which tends to be more popular.
If you have prior programming experience, adding SAS to your magazine will be a relatively simple task that is worth it. If you are learning a brand new skill, consider the careers you need to pursue and whether those will involve managing data.
Even when you are enough or experienced with those other fundamentals, SAS Visual Modeler is cumbersome, and the learning process is often ongoing.
SAS Visual Modeler reduces many of the standards; labor-intensive steps analysts have to go through to experiment with data and modeling techniques. Multiple users can collaboratively customize models and immediately see how changes influence outcomes. It is easy to find out.
Another productivity booster is your data scientists and programmers’ ability to obtain SAS Analytics from leading open source programming languages, including Python, R, Java, and Lua. They can program where they are most useful while taking advantage of highly tested and advanced SAS modeling techniques.