The Data Science Applications in Decision-Making course in Budapest is a specialized training course designed to help professionals integrate data science techniques into their decision-making processes.
From:
10-08-2026
Course Overview
Data science combines statistical analysis, machine learning, and business intelligence to improve the quality and speed of decision-making. By applying data science frameworks, organizations can identify patterns, forecast outcomes, and make evidence-based choices that drive performance and resilience.
This course provides participants with tools and techniques for applying data science in strategic and operational contexts. It covers data-driven forecasting, predictive modeling, AI integration, and visualization to support evidence-based decision-making.
At EuroQuest International Training, the course blends technical knowledge with strategic insights, ensuring professionals can confidently apply data science to real-world business challenges.
Key Benefits of Attending
Apply data science tools to optimize business decisions
Strengthen predictive and prescriptive analytics capabilities
Enhance risk management with evidence-based forecasting
Translate complex data into clear executive insights
Build a data-driven culture across organizations
Why Attend
This course enables professionals to transition from intuition-driven to analytics-driven decision-making, harnessing data science for improved accuracy, agility, and innovation.
Course Methodology
Instructor-led sessions with data science case studies
Hands-on labs with analytics and visualization tools
Predictive modeling simulations
Group projects on data-driven decision frameworks
Peer discussions on best practices and challenges
Course Objectives
By the end of this ten-day training course, participants will be able to:
Understand the role of data science in decision-making
Collect, clean, and structure data for analysis
Apply predictive and prescriptive models to real-world scenarios
Use visualization techniques to communicate insights effectively
Integrate AI and machine learning into business strategies
Align analytics outcomes with organizational goals
Manage risks and uncertainty using data-driven approaches
Ensure ethical and transparent use of data science
Build performance dashboards for executives
Drive organizational change toward evidence-based culture
Measure ROI and business impact of analytics initiatives
Develop a long-term roadmap for data science integration
Target Audience
Executives and business leaders
Data analysts and scientists
Strategy and innovation managers
Operations and finance professionals
Risk and compliance managers
Target Competencies
Data analysis and interpretation
Predictive and prescriptive modeling
Visualization and communication of insights
AI and machine learning applications
Ethical and compliant data use
Strategic decision-making frameworks
Organizational data-driven leadership
Course Outline
Unit 1: Introduction to Data Science in Decision-Making
Defining data science and business value
Evolution of data-driven decision-making
Case studies from leading organizations
Key challenges in adoption
Unit 2: Data Collection, Cleaning, and Preparation
Sources of structured and unstructured data
Data cleaning and transformation techniques
Ensuring accuracy, reliability, and consistency
Tools for data preparation
Unit 3: Exploratory Data Analysis and Visualization
Using visualization to uncover insights
Correlation, distribution, and trend analysis
Dashboards for exploratory decision-making
Tools for EDA (Python, R, BI tools)
Unit 4: Predictive Analytics and Forecasting
Regression models for prediction
Time series forecasting methods
Scenario analysis for risk management
Applications in finance, sales, and operations
Unit 5: Machine Learning for Business Decisions
Supervised and unsupervised learning
Classification and clustering applications
Business case studies of ML-driven insights
Evaluating model performance
Unit 6: Prescriptive Analytics and Optimization
Decision optimization frameworks
Simulation and “what-if” modeling
Linking prescriptive analytics to strategy
Real-world applications in resource allocation
Unit 7: AI and Cognitive Technologies in Decisions
Integrating AI into decision support
Natural language processing for insights
Automation of decision workflows
AI ethics and governance
Unit 8: Risk Management with Data Science
Using analytics to identify and mitigate risks
Predictive modeling for operational resilience
Fraud detection and anomaly analysis
Regulatory implications of data-driven risk
Unit 9: Communicating Data Science Insights
Data storytelling for executives
Designing effective dashboards
Translating complex models into business terms
Stakeholder engagement and communication
Unit 10: Building a Data-Driven Culture
Change management for analytics adoption
Encouraging evidence-based decisions
Training and awareness programs
Overcoming cultural barriers
Unit 11: ROI and Performance Measurement
Metrics for data science effectiveness
Tracking cost savings and revenue growth
Linking analytics outcomes to KPIs
Continuous improvement approaches
Unit 12: Capstone Data Science Decision Project
Group-based data-driven decision simulation
Building an end-to-end analytics workflow
Presenting insights to a mock executive board
Action plan for organizational application
Closing Call to Action
Join this ten-day training course to master data science applications in decision-making, enabling your organization to harness analytics for smarter, faster, and more effective strategies.
The Data Science Applications in Decision-Making Training Courses in Budapest equip professionals with the analytical and technical skills required to translate complex data into clear, actionable insights that support organizational strategy and operational performance. Designed for business analysts, data scientists, managers, and decision-makers, these programs focus on how modern data science techniques can enhance forecasting accuracy, improve resource planning, and enable evidence-based decision-making across diverse business environments.
Participants gain a practical understanding of data science fundamentals, including data exploration, statistical modeling, machine learning approaches, and predictive analytics. The courses emphasize how data-driven insights support real-world scenarios such as market evaluation, customer behavior analysis, performance measurement, and risk assessment. Through hands-on exercises and real dataset applications, attendees learn how to structure analytical problems, build models, interpret outputs, and communicate results in ways that influence strategic and operational decisions.
These data science training programs in Budapest also highlight the importance of connecting analytical methods with organizational context. The curriculum explores best practices for integrating data science into workflows, selecting the right analytical tools, and aligning insights with business priorities. Participants examine the role of data governance, quality control, and ethical considerations to ensure that decision-making processes remain transparent, accountable, and aligned with institutional values.
Interactive learning sessions and case-based group activities provide opportunities to collaborate, share experiences, and apply data science concepts to industry-relevant challenges. Participants gain familiarity with widely used analytics platforms, visualization tools, and automated reporting environments that enhance communication and stakeholder engagement.
Attending these training courses in Budapest offers a dynamic and globally oriented learning environment enriched by the city’s expanding data and innovation ecosystem.
By completing this specialization, participants will be prepared to leverage data science tools effectively, strengthen analytical decision-making, and contribute to improved strategic planning and organizational performance in a competitive and data-driven landscape.