What Is Predictive Modeling with SAS Enterprise Miner?
Predictive modeling is the process of studying the data models. Various sets of statistical methods are utilized; multiple predictors make these models. SAS enterprise miner manages to provide us with several tools for predictive modeling. The methods used in predictive modeling come from several research areas, including statistics, pattern recognition, and machine learning. This SAS Predictive Modeler course will have full details of predictive modeling with SAS enterprise miner. The trainee will study the research of other predictors and predict data according to other concepts. It is currently the most commonly used in computer science, information technology, and information services.
Using objective data, the complex methods practically and straightforwardly to readers from different backgrounds and industries. Incorporating the latest version of Enterprise Miner also expands the section on time series. Written for business analysts, data scientists, statisticians, students, predictive modelers, and data miners, this comprehensive text provides examples that will strengthen your understanding of the essential concepts and methods of predictive modeling.
SAS Predictive Modeler topics covered include logistic regression, regression, decision trees, neural networks, variable clustering, observation clustering, data imputation, binning, data exploration, variable selection, variable transformation, and much more, including analysis of textual data. Develop predictive models quickly, learn how to test numerous models and compare the results, gain an in-depth understanding of predictive models and multivariate methods, and discover how to do in-depth analysis.
This SAS Predictive Modeler certification is suitable for an extended range of students; this needs to be checked to make things clear in your mind if predictive modeling with SAS enterprise miner is ideal for you or not. The students from mechanical or computer science fields from mathematics or statistics background are suitable. The working professionals from the software field, banking, insurance, share market, information technologies who want to drift to data analysis are more convenient. They comprise a significant crowd of class size.
This predictive modeling with the SAS enterprise miner course is also suitable for managers and experienced industry professionals who want to promote themselves as data scientists further. People from various fields also take this predictive modeling with SAS enterprise miner training to perform data analysis in their particular areas. Most commonly, students having graduate degrees and master’s or postgraduates can apply easily for this course.
SAS Predictive Modeling Using SAS Enterprise Miner 14: Career Benefits
The predictive modeling with SAS enterprise miner is currently the most commonly used in computer science for predicting the data. This SAS Predictive Modeler exam tends to fulfill the dream of those individuals who aim to become a data analyst and data scientist usually the career benefits of this course is the for the people who are willing to switch to a job according to their interest so after the completion of this predictive modeling with SAS enterprise miner course one can start sitting for interviews and look out of jobs according to his criteria.
After this predictive modeling with the SAS enterprise miner course, an individual generally becomes a senior data analyst, associate data scientist, data scientist, and data visualization expert in the respective domains. According to your knowledge, there is a salary increment where you will get improved pay after this predictive modeling with the SAS enterprise miner course. Sometimes, individuals get promotions if they get more responsibility and raise the corporate ladder after this course.
Today, analytics needs to be applied to structured and unstructured data. Then, big data analysts can use advanced analytics techniques on substantial data sets that include structured, semi-structured, and unstructured data from many sources. Their insights fuel faster decision-making, making it possible to create more accurate models and predict future trends with a higher degree of accuracy.
Big data analytics sits at the crossroads of many BI and analytics disciplines. Traditionally, analysts would attack small subsets of structured data and present their findings. Gradually, larger data sets were added as computing and memory resources became more available.
In conclusion, predictive modeling is crucial for big data and big data analytics. The analysis will help us understand, clean, transform and use the relevant data. The predictive model will help us predict the future, find a solution, and apply it to newer problems for further and accurate predictions.