Predictive Analytics Using R and SQL

Overview

Explore predictive analytics concepts, applications, and integration with BI environments. Learn data science processes, methodologies, and practical techniques using R and SQL for effective predictions.

Syllabus

• Introduction to Predictive Analytics
Overview of predictive analytics
Importance and applications in business intelligence (BI)
Key concepts and terminology
• Fundamentals of R for Data Science
Introduction to R programming
Data structures in R
Basic data manipulation and visualization
• Fundamentals of SQL for Data Science
Introduction to SQL and relational databases
SQL queries for data extraction and manipulation
Joining, aggregating, and filtering data
• Data Science Process and Methodologies
CRISP-DM framework
Data preparation and cleaning
Feature engineering and selection
• Exploratory Data Analysis (EDA)
Descriptive statistics and data visualization
Identifying patterns and relationships
Preparing data for predictive modeling
• Building Predictive Models in R
Introduction to common predictive modeling techniques (e.g., linear regression, decision trees)
Model training and evaluation
Hyperparameter tuning and model optimization
• Advanced SQL for Predictive Modeling
Implementing predictive models with SQL
Using SQL for data preprocessing and transformations
Integrating SQL with R for model deployment
• Integration with Business Intelligence (BI) Environments
Understanding BI tools and interfaces
Embedding predictive models within BI platforms
Case studies of BI and predictive analytics integration
• Practical Techniques and Case Studies
Real-world applications of predictive analytics
Hands-on projects using R and SQL
Industry case studies and best practices
• Ethical Considerations and Future Trends
Ethical implications of predictive analytics
Data privacy and security concerns
Emerging trends and technologies in predictive analytics
• Course Review and Capstone Project
Review of key concepts and methodologies
Capstone project presentation and feedback
Future learning paths and resources

Subjects

Conference Talks