Data Science Modeling using SAS
Data science has evolved over time, as have its tools, and today, SAS remains at the forefront of intelligent solutions.
With the accelerated digital Data generation today, the need to evolve Data science techniques has intensified, just as updating and improving advanced Analytics technologies and tools has become a requirement.
SAS allows you to perform multiple tasks on a single platform. This way, the Data science procedure's steps can be carried out faster, orderly, and more efficiently. Read on to discover how it does it.
How can Data processing through SAS be made more efficient?
- The platform is a complete and integrated base that offers the following functions to users with respect to Data: access to Data sets; exploring them; transforming them; analyzing them; controlling them; as well as allowing more than one user to collaborate in the process.
- The integration of functions into the same platform favors the governance process within the company since it links all the areas involved in the stages of the analysis process during the implementation of Data science models, avoiding the use of different tools that do not necessarily integrate with each other.
In addition to the above, regarding the interpretation of the models, it has integrated the function of using metrics such as PD, ICE, LIME, Kernel SHAP, and the implementation of Natural Language Processing (PLN or NLP) to process the interactions between humans and machines through the use of natural language.
What other features do you offer for Data science models?
A useful function that SAS offers to make Data models more effective is the possibility for different users to upload Data sets to the platform even when they are in different processes, thus saving steps and obtaining an analysis optimization of the set.
Through its use, SAS offers the possibility of developing machine learning and artificial intelligence models, as it has included, over time, machine learning techniques such as Neural Networks, Random Forests, and Gradient Boosting, among others, favor the creation of models more efficiently and faster.
How does your interface facilitate the tasks of the Data scientist?
The SAS interface has the option of "drag and drops" for implementing models with computational languages such as R and Python, facilitating a better understanding of the Data scientist and business Analyst. It also has the option of "point and clicks" to execute various tasks.
This interface assists users in Data science processes ranging from Data manipulation, graph generation, descriptive statistics, the multiplicity of analyses, and Data processing, to time series, without requiring complex programming systems. This will also make the generation of insights more efficient by doing it in less time.
What are the benefits of using SAS tools?
- SAS is a tool that serves to streamline the implementation of Data science in organizations and is useful for Data scientists, statisticians, or business Analysts.
- Through its platform, the exploration, analysis, and visualization of Data will be more understandable for users, thanks to the fact that it integrates programs that allow the automation and efficiency of the process, resulting in more reliable information by reducing manual work and, therefore, human errors.
- The information extracted from the Data set will be of greater utility value for organizations. This is because it makes it possible to maintain "open" analytical models, a resource of the utmost importance. After all, it allows the use of various Data libraries and tools through the same platform, saving time and resources.
- Through the SAS platform, the complete life cycle of analytical Data models can be managed, from their implementation, monitoring, or follow-up to the training of the models developed in SAS, Python, and R, to obtain maximum Data processing performance.
In summary, you will be able to automate the steps involved in analytical models and decision flows, but if you want to know more details about how to optimize the transformation of Data into strategic assets to increase the profitability and utility of your organization, in addition to improving internal performance, Contact one of our specialized advisers and solve all your doubts.
|Course||Data Science Modeling using SAS|