Euroasia Training Euroasia Training
AR

Data Analytics, Artificial Intelligence and Decision Sciences

Business Intelligence Powered by Machine Learning

12 units

Please select a city/session before registration.

About this program

Traditionally, business intelligence has depended on descriptive analytics; however, the incorporation of machine learning empowers organizations to advance further by forecasting results, customizing customer interactions, and revealing concealed data patterns. This training provides participants with the skills necessary to embed machine learning techniques into BI systems and processes. Topics include supervised and unsupervised learning, predictive modeling, automation, and visualization to strengthen intelligence-led business strategies. EuroQuest International Training combines technical rigor with strategic insights to ensure professionals can implement machine learning solutions that deliver quantifiable business value.

Key outcomes

  • Explain the significance of machine learning within business intelligence
  • Prepare and organize data for ML-based BI applications
  • Utilize supervised and unsupervised machine learning techniques
  • Create predictive dashboards to forecast business results
  • Incorporate ML algorithms into existing BI frameworks
  • Align BI initiatives with overall organizational objectives
  • Leverage automation to optimize data workflows
  • Effectively communicate complex AI findings to stakeholders
  • Maintain transparency and governance in ML implementations
  • Assess the return on investment for ML-enhanced BI projects
  • Promote a data-centric culture and encourage analytics adoption across teams
  • Formulate a strategic plan for advancing ML-integrated BI maturity

Who should attend

  • Professionals in business intelligence
  • Data analysts and data scientists
  • Managers in IT and innovation
  • Leaders in operations and strategy
  • Executives responsible for data-driven projects

Course outline

1

Unit 1: Overview of Machine Learning Applications in Business Intelligence

  • Transition from descriptive analytics to predictive business intelligence
  • Importance of ML in business decision processes
  • Value creation through ML-enhanced BI
  • International examples of ML implementation
2

Unit 2: Data Management for Business Intelligence and Machine Learning

  • Processes of data gathering, cleansing, and conversion
  • Maintaining data quality and uniformity
  • Managing both structured and unstructured datasets
  • Automated ETL tools and their applications
3

Unit 3: Core Concepts of Machine Learning Algorithms

  • Introduction to supervised versus unsupervised learning techniques
  • Basics of classification, regression, and clustering
  • Training models and evaluating performance metrics
  • Hands-on session: developing a basic ML model
4

Unit 4: Applying Predictive Analytics within BI

  • Techniques for forecasting sales, demand, and market trends
  • Detection of risks and anomalies
  • Using predictive models for scenario analysis
  • Practical uses in business forecasting
5

Unit 5: Unsupervised Machine Learning and Pattern Analysis

  • Methods for clustering and segmenting data
  • Market basket analysis and recommendation engine basics
  • Dimensionality reduction techniques for enhanced BI insights
  • Industry-specific application examples
6

Unit 6: ML Integration with Business Intelligence Systems

  • Connecting machine learning models to BI visualization tools
  • Employing Python, R, and APIs for seamless integration
  • Cloud-based solutions for BI and ML
  • Practical exercise: designing AI-powered dashboards
7

Unit 7: Communicating and Visualizing Machine Learning Findings

  • Crafting data narratives with AI-generated insights
  • Creating dashboards for executive leadership
  • Guidelines for effective and actionable reporting
  • Bridging gaps between technical experts and business stakeholders
8

Unit 8: Machine Learning-Driven Automation in BI

  • Automating processes for data preparation and analytics
  • Self-service BI and AI-powered querying
  • Real-time analytics to support decision-making
  • Case study reviews on BI automation
9

Unit 9: Ethical, Governance, and Responsible AI Practices

  • Ensuring transparency and explainability in BI algorithms
  • Mitigating bias and promoting fairness
  • Compliance and regulatory considerations in AI for BI
  • Ethical frameworks for AI adoption
10

Unit 10: Leveraging Machine Learning for Customer and Market Insights

  • Building personalized recommendation systems
  • Predicting customer behavior patterns
  • Utilizing AI in pricing, marketing, and customer engagement
  • Using ML for competitive market intelligence
11

Unit 11: Evaluating the ROI of Machine Learning in BI

  • Key performance indicators for measuring success
  • Connecting BI results to KPIs and financial outcomes
  • Analyzing cost versus benefits of ML deployment
  • Developing business cases to support executive decision-making
12

Unit 12: Final Project: BI Solutions Powered by Machine Learning

  • Collaborative design of ML-enhanced dashboards
  • Creation of predictive BI process workflows
  • Delivering presentations to simulated executive boards
  • Strategizing enterprise-wide ML adoption plans