Predictive Analytics using Oracle Data Mining
Duration : 2 Days (16 Hours)
Predictive Analytics using Oracle Data Mining Course Overview:
The Predictive Analytics using Oracle Data Mining certification empowers individuals to utilize Oracle’s Data Mining (ODM) technology for predicting customer behavior, detecting anomalies, and discovering hidden patterns within vast datasets. This certification holds significant value for industries like healthcare, finance, and retail, enabling them to forecast future trends and make data-driven business decisions. Predictive analytics models, such as classification, regression, clustering, and feature selection/extraction, analyze historical and real-time data to provide valuable insights. This information enhances strategic decision-making, optimizes business operations, mitigates risks, and fosters a competitive edge for businesses.
Intended Audience:
- Data scientists and analysts
- Professionals working in predictive modeling and machine learning
- Database administrators and IT experts
- Business intelligence professionals
- ICT students and graduates
- Professionals pursuing a career in data mining
- Decision-makers relying on analytics.
Learning Objectives of Predictive Analytics using Oracle Data Mining:
The learning objectives of the Predictive Analytics using Oracle Data Mining course are:
- Develop a strong foundational understanding of predictive analytics concepts.
- Learn to use Oracle Data Mining to analyze and interpret large volumes of data.
- Explore data to identify patterns and trends that can be used for predictive modeling.
- Create predictive models using various algorithms and techniques available in Oracle Data Mining.
- Test and evaluate the performance of predictive models to ensure accuracy and reliability.
- Interpret and communicate the results of predictive models to support data-driven decision making.
- Apply predictive analytics in real-world scenarios to solve business problems effectively.
- Gain practical experience in using Oracle Data Mining to make accurate predictions and recommendations based on data analysis.
- Introduction
- Course Objectives
- Suggested Course Prerequisites
- Suggested Course Schedule
- Class Sample Schemas
- Practice and Solutions Structure
- Review location of additional resources
- Predictive Analytics and Data Mining Concepts
- What is the Predictive Analytics?
- Introducting the Oracle Advanced Analytics (OAA) Option?
- What is Data Mining?
- Why use Data Mining?
- Examples of Data Mining Applications
- Supervised Versus Unsupervised Learning
- Supported Data Mining Algorithms and Uses
- Understanding the Data Mining Process
- Common Tasks in the Data Mining Process
- Introducing the SQL Developer interface
- Introducing Oracle Data Miner 4.1
- Data mining with Oracle Database
- Setting up Oracle Data Miner
- Accessing the Data Miner GUI
- Identifying Data Miner interface components
- Examining Data Miner Nodes
- Previewing Data Miner Workflows
- Using Classification Models
- Reviewing Classification Models
- Adding a Data Source to the Workflow
- Using the Data Source Wizard
- Using Explore and Graph Nodes
- Using the Column Filter Node
- Creating Classification Models
- Building the Models
- Examining Class Build Tabs
- Using Regression Models
- Reviewing Regression Models
- Adding a Data Source to the Workflow
- Using the Data Source Wizard
- Performing Data Transformations
- Creating Regression Models
- Building the Models
- Comparing the Models
- Selecting a Model
- Using Clustering Models
- Describing Algorithms used for Clustering Models
- Adding Data Sources to the Workflow
- Exploring Data for Patterns
- Defining and Building Clustering Models
- Comparing Model Results
- Selecting and Applying a Model
- Defining Output Format
- Examining Cluster Results
- Performing Market Basket Analysis
- What is Market Basket Analysis?
- Reviewing Association Rules
- Creating a New Workflow
- Adding a Data Source to the Workflow
- Creating an Association Rules Model
- Defining Association Rules
- Building the Model
- Examining Test Results
- Performing Anomaly Detection
- Reviewing the Model and Algorithm used for Anomaly Detection
- Adding Data Sources to the Workflow
- Creating the Model
- Building the Model
- Examining Test Results
- Applying the Model
- Evaluating Results
- Mining Structured and Unstructured Data
- Dealing with Transactional Data
- Handling Aggregated (Nested) Data
- Joining and Filtering data
- Enabling mining of Text
- Examining Predictive Results
- Using Predictive Queries
- What are Predictive Queries?
- Creating Predictive Queries
- Examining Predictive Results
- Deploying Predictive models
- Requirements for deployment
- Deployment Options
- Examining Deployment Options
Predictive Analytics using Oracle Data Mining Course Prerequisites:
• Strong understanding of basic statistics and data modeling concepts
• Basic proficiency in SQL and Oracle PL/SQL
• Familiarity with Oracle Database features and functionality
• Knowledge of Oracle Data Miner
• Understanding of machine learning algorithms and techniques
• Experience in data analysis and interpretation
• Prior exposure to data mining concepts and techniques.
Discover the perfect fit for your learning journey
Choose Learning Modality
Live Online
- Convenience
- Cost-effective
- Self-paced learning
- Scalability
Classroom
- Interaction and collaboration
- Networking opportunities
- Real-time feedback
- Personal attention
Onsite
- Familiar environment
- Confidentiality
- Team building
- Immediate application
Training Exclusives
This course comes with following benefits:
- Practice Labs.
- Get Trained by Certified Trainers.
- Access to the recordings of your class sessions for 90 days.
- Digital courseware
- Experience 24*7 learner support.
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