SAS EM (SAS Enterprise Miner)

1. Data Preparation: SAS EM allows users to prepare and preprocess data for analysis. It provides tools for data cleansing, data transformation, missing value imputation, variable selection, and feature engineering to ensure data quality and relevance for modeling.

2. Exploratory Data Analysis (EDA): Users can perform exploratory data analysis to gain insights into the structure, patterns, and relationships within their data. SAS EM offers descriptive statistics, graphical visualization, correlation analysis, and clustering techniques to explore and understand the data.

3. Predictive Modeling: One of the core capabilities of SAS EM is predictive modeling. It offers a wide range of advanced modeling techniques, including regression analysis, decision trees, neural networks, support vector machines, ensemble methods (e.g., random forests, gradient boosting), and time series forecasting. Users can build and evaluate predictive models to make predictions and identify patterns in their data.

4. Model Assessment and Validation: SAS EM provides tools for assessing and validating predictive models. Users can evaluate model performance using metrics such as accuracy, precision, recall, ROC curves, lift charts, and confusion matrices. They can also perform cross-validation, holdout validation, and validation on independent datasets to ensure model robustness and generalization.

5. Model Deployment: Once models are built and validated, SAS EM allows users to deploy them into production environments for real-time scoring or batch scoring. Models can be deployed as SAS scoring code, PMML (Predictive Model Markup Language), or as RESTful web services for integration with other applications and systems.

6. Automation and Workflow: SAS EM supports automation and workflow management for streamlining the data mining and modeling process. Users can create and customize workflows to automate repetitive tasks, schedule analyses, and collaborate with team members. Workflow templates and reusable process flows facilitate efficient model development and deployment.

7. Integration with SAS Environment: SAS EM seamlessly integrates with other SAS products and solutions, including SAS Visual Analytics, SAS Visual Statistics, SAS Viya, and SAS Model Manager. Users can leverage the capabilities of these integrated solutions for end-to-end data analysis, visualization, and model governance.

8. Scalability and Performance: SAS EM is designed to handle large volumes of data and complex analytics tasks efficiently. It supports parallel processing, distributed computing, and in-memory analytics to deliver high performance and scalability for processing big data and conducting advanced analytics.

Before diving into learning SAS EM (SAS Enterprise Miner), it's beneficial to have a foundation in several key areas. Acquiring these skills will help you understand and effectively utilize SAS EM's features and functionalities. Here are some skills that can be beneficial before learning SAS Enterprise Miner:

1. Statistical Analysis: A basic understanding of statistical concepts is essential for working with SAS EM. Familiarize yourself with descriptive statistics, inferential statistics, probability distributions, hypothesis testing, and regression analysis.

2. Data Manipulation: Proficiency in data manipulation techniques is crucial for preparing and preprocessing data in SAS EM. Learn how to perform tasks such as data cleaning, data transformation, variable creation, missing value imputation, and data summarization using SAS programming or other tools like SAS Data Integration Studio.

3. Programming Skills: While SAS EM provides a user-friendly interface for building predictive models, having programming skills can be advantageous for advanced customization and automation. Learn SAS programming basics, including data step programming, PROC SQL, and SAS macro language.

4. Data Mining and Predictive Analytics: Understanding the principles of data mining and predictive analytics is essential for effectively utilizing SAS EM. Familiarize yourself with common data mining techniques, such as decision trees, neural networks, logistic regression, clustering, and association analysis.

5. Machine Learning Concepts: Gain familiarity with machine learning concepts and algorithms, including supervised learning, unsupervised learning, ensemble methods, feature selection, model evaluation metrics, and overfitting/underfitting.

6. Database Concepts: SAS EM can interface with various data sources, including databases. Understanding database concepts such as relational databases, SQL queries, data warehousing, and data modeling can be beneficial for accessing and integrating data from different sources.

7. Business Understanding: Having domain knowledge in the industry or business domain where SAS EM will be applied is valuable. Understand the business context, objectives, and challenges to effectively frame data mining and predictive analytics tasks and interpret model results in a meaningful way.

8. Data Visualization: While SAS EM provides some visualization capabilities, having skills in data visualization tools like SAS Visual Analytics, Tableau, or Power BI can enhance your ability to explore and communicate insights from your data effectively.

9. Critical Thinking and Problem-Solving: Develop strong critical thinking and problem-solving skills to formulate analytical questions, design experiments, evaluate model performance, and interpret results accurately.

10. Learning Attitude: Approach learning SAS EM with a positive and curious attitude. Be willing to explore new concepts, experiment with different techniques, and continuously learn and adapt as you progress in your understanding of data mining and predictive analytics with SAS EM.

Learning SAS EM (SAS Enterprise Miner) equips individuals with a diverse set of skills that are valuable for conducting advanced data mining and predictive analytics tasks. Here are some skills you can gain by learning SAS Enterprise Miner:

1. Data Exploration and Preparation: SAS EM enables users to explore and prepare data for analysis effectively. You'll gain skills in data exploration techniques, identifying patterns, outliers, and trends, and preparing data for modeling by handling missing values, outliers, and data transformations.

2. Predictive Modeling: One of the core capabilities of SAS EM is predictive modeling. You'll gain skills in building and evaluating predictive models using a variety of advanced techniques such as decision trees, neural networks, logistic regression, support vector machines, ensemble methods, and time series forecasting.

3. Model Evaluation and Validation: SAS EM provides tools for evaluating and validating predictive models to ensure their accuracy and reliability. You'll gain skills in assessing model performance using metrics such as accuracy, precision, recall, ROC curves, lift charts, and confusion matrices.

4. Feature Selection and Variable Importance: SAS EM allows users to identify important variables and select relevant features for predictive modeling. You'll gain skills in feature selection techniques, such as information gain, variable importance ranking, and dimensionality reduction, to improve model performance and interpretability.

5. Segmentation and Clustering: SAS EM offers capabilities for segmentation and clustering analysis to identify homogeneous groups within data. You'll gain skills in clustering techniques such as k-means clustering, hierarchical clustering, and self-organizing maps (SOM) to uncover hidden patterns and segments in your data.

6. Text Mining and Sentiment Analysis: SAS EM includes text mining features for analyzing unstructured text data. You'll gain skills in text preprocessing, sentiment analysis, topic modeling, and document clustering to extract insights from text documents and social media data.

7. Model Deployment and Scoring: SAS EM enables users to deploy predictive models into production environments for real-time or batch scoring. You'll gain skills in deploying models as SAS scoring code, PMML (Predictive Model Markup Language), or as RESTful web services for integration with other applications and systems.

8. Automation and Workflow Management: SAS EM supports workflow automation and management for streamlining the data mining process. You'll gain skills in creating and customizing workflows, scheduling analyses, and collaborating with team members to streamline model development and deployment.

9. Integration with SAS Environment: SAS EM seamlessly integrates with other SAS products and solutions, allowing users to leverage additional analytics capabilities and resources. You'll gain skills in integrating SAS EM with SAS Visual Analytics, SAS Visual Statistics, SAS Model Manager, and other SAS solutions for end-to-end analytics workflows.

10. Interpretation and Communication of Results: SAS EM helps users interpret and communicate the results of their analyses effectively. You'll gain skills in interpreting model outputs, explaining model predictions, and communicating insights to stakeholders in a clear and actionable manner.