SAS applications, tools and techniques have been found instrumental for the field of data mining. Data researchers and scientists have leveraged SAS time and again to arrive at crucial outcomes that help form intelligent business decisions. The below guest post highlights the most important uses of SAS in the data mining process.
SAS (Statistical Analysis System) has been credited with developing automated, machine based learning techniques and tools for data researchers for a long time now. This software works on a foundation of being whole-heartedly committed to research and development due to which the IT software industry has time and again witnessed the great support of analytical algorithms. SAS Certification for data mining therefore brings many data manipulation and processing tools that help finish a task in all its entirety – from the very beginning to the very end.
Data mining on the other hand is a defined process that discovers anomalies, correlations and other patterns to predict results. By using a wide variety of tools and techniques, researchers can use this data to boost their company’s revenues, curtail increasing costs, improve customer service, and reduce risks, to name a few. Data mining thus helps to easily sift through the large volumes of data, understand the derived information, and use it to predict and even arrive at useful outcomes.
Following are some recommended solutions for data mining processes delivered by SAS.
- SAS Enterprise Miner – The SAS Enterprise Miner strives to streamline every process of data mining in order to develop highly reliable, descriptive, as well as predictive models that are specially derived from large volumes of data.
- SAS Text Miner – The SAS Text Miner helps determine topics and other patterns within a full document collection, derived by mining unstructured data sources. The various sources used for this purpose comprise of supervised, semi-supervised and unsupervised techniques.
- Credit Scoring for SAS Enterprise Miner – This enables the creation, authentication, and deployment of credit risk models that leverage the in-house expertise of data scientists.
- SAS Visual Statistics – SAS Visual Statistics have helped develop and modify predictive models more promptly than ever before. These has been made possible by using the tools of visual interface and in-memory processing.
- SAS High-Performance Data Mining – This SAS technique promptly analyzes big data to mine more reliable and accurate insights and help data scientists to make sound business decisions on time.
Hence, by leveraging the SAS applications for the process of data mining brings many advantages to the forefront. More and more IT professionals are sitting for SAS A00-260 and SAS A00-211 certification exam and working hard towards earning the famous SAS and related BI certifications. All this proves the importance of SAS applications, tools, and other techniques in data mining processes and spells out a lucrative path for businesses who are able to form intelligent decisions based on reliable and accurate information.
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