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Predictive Analytics using Oracle Data Mining Course Overview

Predictive Analytics using Oracle Data Mining Course Overview

The "Predictive Analytics using Oracle Data Mining" course is a comprehensive training program designed to equip learners with the skills necessary to perform predictive analytics within the Oracle database environment. Utilizing the Oracle Advanced Analytics Option, students will learn the concepts and techniques of data mining, understand the different types of Learning algorithms, and gain hands-on experience with the Oracle Data Miner 4.1 tool.

Through a series of modules, participants will explore Classification, Regression, Clustering models, and Market basket analysis, as well as Anomaly detection and mining both structured and unstructured data. Predictive queries and the Deployment of predictive models are also covered, providing a full spectrum of knowledge from model creation to implementation.

Learners will benefit from the course by gaining the ability to uncover valuable insights from their data, which can drive decision-making and create competitive advantages. The course is structured to provide a blend of theoretical understanding and practical application, ensuring that participants are well-prepared to utilize Oracle Data Mining tools effectively in their respective organizations.

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Course Prerequisites

To ensure a successful learning experience in the Predictive Analytics using Oracle Data Mining course, the following prerequisites are recommended:


  • Basic understanding of database concepts and familiarity with SQL (Structured Query Language).
  • Knowledge of Oracle Database fundamentals, including the ability to navigate through the database and execute basic queries.
  • An introductory-level comprehension of statistics and data analysis techniques.
  • Familiarity with data warehousing principles and practices.
  • Basic understanding of machine learning concepts, including supervised and unsupervised learning.
  • A general awareness of business analytics and its importance in decision-making processes.

Please note that these prerequisites are meant to provide a foundation for the course material and are not intended to discourage anyone from enrolling. Individuals with a strong desire to learn and a willingness to engage with the course content may overcome any gaps in knowledge through dedicated study and practice.


Target Audience for Predictive Analytics using Oracle Data Mining

This course offers a deep dive into predictive analytics using Oracle Data Mining, tailored for IT professionals keen on data science.


  • Data Scientists
  • Data Analysts
  • Business Analysts
  • BI (Business Intelligence) Professionals
  • Database Administrators
  • Data Warehouse Specialists
  • IT Professionals interested in data mining and analytics
  • Oracle Database Professionals
  • Machine Learning Engineers
  • Software Developers with a focus on data analysis and mining
  • Research Analysts
  • Statisticians
  • Information Systems Managers
  • Technical Consultants specializing in Oracle databases
  • Data Mining Practitioners


Learning Objectives - What you will Learn in this Predictive Analytics using Oracle Data Mining?

Introduction to the Learning Outcomes:

This course offers a comprehensive understanding of predictive analytics using Oracle Data Mining, focusing on key concepts, processes, and practical applications in data mining with Oracle's tools.

Learning Objectives and Outcomes:

  • Understand the fundamentals of predictive analytics and data mining, and their applications in real-world scenarios.
  • Learn to use Oracle Advanced Analytics (OAA) to apply predictive analytics within Oracle's database environment.
  • Differentiate between supervised and unsupervised learning, and identify appropriate algorithms for various data mining tasks.
  • Gain proficiency in using SQL Developer and Oracle Data Miner 4.1 for managing data mining projects.
  • Develop skills to create, evaluate, and deploy classification models to predict outcomes based on historical data.
  • Acquire the ability to construct and assess regression models for forecasting numerical values.
  • Understand clustering models to identify natural groupings within data and interpret the results for strategic insights.
  • Perform Market Basket Analysis to analyze transactional data and generate association rules for decision support.
  • Learn to detect anomalies in datasets using appropriate models and algorithms, assessing the results for further action.
  • Explore methods to mine both structured and unstructured data, including text mining and handling nested data.
  • Discover how to construct predictive queries to anticipate future trends or behaviors directly within Oracle Database.
  • Learn deployment strategies for predictive models, including requirements and options for operational use.

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