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Building and Optimizing Decision Systems

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Building and Optimizing Decision Systems

Instructor: Edureka

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Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Identify key concepts and components of decision intelligence used to structure business problems.

  • Analyze data-to-decision pipelines that transform analytics into optimized business workflows.

  • Evaluate the effectiveness and performance of decision systems using metrics and visualization tools.

  • Design responsible AI principles to ensure fairness, transparency, and accountability in decision-making.

Details to know

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Assessments

14 assignments¹

AI Graded see disclaimer
Taught in English

Build your subject-matter expertise

This course is part of the AI-Powered Decision Intelligence Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

There are 4 modules in this course

This course dives into how AI-powered decision systems are designed, modeled, and governed. Built for professionals who turn analytics into real business impact, it guides you through engineering intelligent decision workflows from end to end.

You’ll kick off by transforming business challenges into structured decision models designing fast, intuitive data flows and building ethical, human-aware processes that make every choice sharper, clearer, and more confident. Next, you’ll turn raw data into intelligence using forecasting, optimization, and scenario simulations that reveal hidden patterns, anticipate outcomes, and fuel high-impact decisions that propel the business forward. Finally, you’ll elevate trust across your pipelines with explainable AI, dynamic dashboards, and responsible governance, ensuring every decision is transparent, fair, and reliable enough to inspire confidence at every level. By the end of this course, you will be able to: - Analyze business challenges and define structured, data-driven decision problems. - Design scalable, real-time data pipelines that power decision intelligence. - Build predictive and prescriptive models for forecasting and optimization. - Evaluate decision system performance through interactive dashboards. - Apply responsible AI principles to ensure fairness, transparency, and accountability. This course is ideal for data professionals, business analysts, AI engineers, and decision scientists seeking to turn analytics into high-value decisions. Prior experience in data analytics or machine learning will help deepen your learning. Join this course to master intelligent, transparent, and responsible decision system design and learn how to unlock stronger business performance through data-driven intelligence.

Discover how to design and engineer end-to-end Decision Intelligence pipelines that transform business objectives into actionable outcomes. Explore methods for framing decision problems, building data-to-decision workflows, and implementing intelligent decision support systems. Understand how to connect analytics, automation, and business strategy to create scalable, high-performance decision ecosystems that continuously learn and improve.

What's included

13 videos9 readings4 assignments

13 videos•Total 74 minutes
  • Specialization Introduction•6 minutes
  • Course Introduction•4 minutes
  • Identifying Decision Opportunities•6 minutes
  • Structuring Decision Workflows•6 minutes
  • Frameworks for Aligning Decisions with Business Goals•7 minutes
  • Demonstration: Decision Problem Framing in Orange•6 minutes
  • Designing Data Flow Architecture for DI Systems•6 minutes
  • Real-Time Data Reliability and Continuity Frameworks•5 minutes
  • Implementing End-to-End Data Pipelines•5 minutes
  • Demonstration: Predictive Decision Analytics with Scikit-learn•6 minutes
  • Core Architecture of Decision Support Systems (DSS)•6 minutes
  • DSS Visualization and Reporting Components•6 minutes
  • Assessing DSS Impact on Business Performance•5 minutes
9 readings•Total 100 minutes
  • Course Overview•15 minutes
  • Decision Framing in Business Analytics: Turning Problems into Models•10 minutes
  • Translating Business Objectives into Data Problems•10 minutes
  • Problem Framing and Workflow Design in Orange•10 minutes
  • Event-Driven Data Integration Concepts•10 minutes
  • Data Continuity Patterns in Modern Cloud Architectures•10 minutes
  • Building Predictive Models with Scikit-learn•10 minutes
  • Integrating Predictive and Prescriptive Models•10 minutes
  • Module Summary: Designing and Engineering Decision Pipelines•15 minutes
4 assignments•Total 33 minutes
  • Knowledge Check: Framing Business Problems for Decision Systems•6 minutes
  • Knowledge Check: Building Data-to-Decision Pipelines•6 minutes
  • Knowledge Check: Implementing Decision Support Systems•6 minutes
  • Knowledge Check: Designing and Engineering Decision Pipelines•15 minutes

Master advanced techniques in predictive, prescriptive, and simulation-based analytics to drive strategic and optimized business decisions. Learn how to forecast trends using predictive models, design optimization frameworks for efficient resource allocation, and evaluate decision outcomes through scenario and Monte Carlo simulations. This module empowers learners to build robust, data-driven models that enhance foresight, improve efficiency, and strengthen decision confidence in dynamic business environments.

What's included

12 videos9 readings4 assignments

12 videos•Total 66 minutes
  • Forecasting and Predictive Modeling Overview•6 minutes
  • Building Predictive Models for Business Decisions•6 minutes
  • Evaluating Model Accuracy in Forecasting•5 minutes
  • Demonstration: Building a Linear Regression Forecasting Model•6 minutes
  • Prescriptive Analytics for Business Efficiency•5 minutes
  • Multi-Objective Optimization for Resource Planning•5 minutes
  • Scenario-Based Decision Optimization•5 minutes
  • Demonstration: Optimizing Production with Linear Programming using PuLP•6 minutes
  • Introduction to Monte Carlo Simulation•5 minutes
  • Sensitivity and Scenario Testing in Decision Models•5 minutes
  • Evaluating Decision Robustness through Simulation•5 minutes
  • Demonstration: What-If Analysis and Simulation for Retail Pricing and Traffic•7 minutes
9 readings•Total 95 minutes
  • Time Series and Regression Approaches for Forecasting•10 minutes
  • Developing Predictive Models with Scikit-learn•10 minutes
  • Forecasting Business Trends with Linear Regression•10 minutes
  • Core Optimization Fundamentals•10 minutes
  • Resource Optimization with PuLP•10 minutes
  • Prescriptive versus Predictive analytics•10 minutes
  • Building Probabilistic Models for Risk Analysis•10 minutes
  • Scenario-Driven Revenue Forecasting•10 minutes
  • Module Summary: Advanced Decision Modeling and Optimization•15 minutes
4 assignments•Total 33 minutes
  • Knowledge Check: Predictive Analytics for Strategic Decision-Making•6 minutes
  • Knowledge Check: Prescriptive Analytics and Optimization Techniques•6 minutes
  • Knowledge Check: Simulation and Scenario Modeling•6 minutes
  • Knowledge Check: Advanced Decision Modeling and Optimization•15 minutes

Explore how to evaluate, explain, and govern AI-driven decision systems with transparency and accountability. This module focuses on measuring decision performance, building explainable and human-centered models, and establishing responsible AI governance frameworks. Learners will gain hands-on experience with tools for performance visualization, interpretability techniques like SHAP, and governance methods that ensure fairness, compliance, and trust in Decision Intelligence systems.

What's included

10 videos6 readings4 assignments

10 videos•Total 56 minutes
  • Key Performance Indicators for Decision Intelligence Systems•6 minutes
  • Visualizing Performance Trends using Dashboards•5 minutes
  • Interpreting Model Results for Business Impact•6 minutes
  • Demonstration: Building an Interactive Retail Analytics Dashboard with Streamlit•7 minutes
  • Explainable AI for Decision Intelligence Systems•5 minutes
  • Designing Human-Centered AI for Stakeholder Confidence•5 minutes
  • Visualizing Model Decisions and Attribution Values•5 minutes
  • Demonstration: Explaining Machine Learning Models with SHAP•6 minutes
  • Foundations of Responsible AI and Governance Principles•5 minutes
  • Demonstration: Building a Responsible AI Governance Dashboard using Streamlit•6 minutes
6 readings•Total 65 minutes
  • Metrics for Model Evaluation - Accuracy, Precision, Recall, and F1-Score•10 minutes
  • Building Interactive Dashboards with Streamlit•10 minutes
  • Interpreting SHAP Visualizations•10 minutes
  • Fairness, Accountability, and Transparency (FAT) Framework in AI•10 minutes
  • Streamlit as a Learning Tool for Responsible AI•10 minutes
  • Module Summary: Evaluation, Explainability, and Governance in Decision Intelligence•15 minutes
4 assignments•Total 33 minutes
  • Knowledge Check: Evaluating Decision System Performance•6 minutes
  • Knowledge Check: Explainable and Human-Centered Decision Systems•6 minutes
  • Knowledge Check: Responsible AI Governance and Fairness•6 minutes
  • Knowledge Check: Evaluation, Explainability, and Governance in Decision Intelligence•15 minutes

This final module is designed to measure your proficiency in the critical concepts and analytical approaches covered in the course, providing a formal opportunity to showcase your knowledge through an in-depth, graded evaluation.

What's included

1 video1 reading2 assignments1 discussion prompt

1 video•Total 4 minutes
  • Course Summary•4 minutes
1 reading•Total 30 minutes
  • Practice Project: Designing and Optimizing an AI-Driven Business Decision System•30 minutes
2 assignments•Total 60 minutes
  • End Course Knowledge Check: Building and Optimizing Decision Systems•30 minutes
  • The Smart Core: AI-Powered Decision Systems•30 minutes
1 discussion prompt•Total 5 minutes
  • Describe Your Learning Journey•5 minutes

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Instructor

Edureka
203 Courses•185,285 learners

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Frequently asked questions

This course is ideal for data analysts, AI/ML practitioners, business intelligence professionals, and decision engineers who want to design, build, and optimize intelligent decision systems that connect analytics to real-world business outcomes.

The course covers the foundations of decision pipeline design, predictive and prescriptive modeling, simulation and scenario optimization, explainability, and responsible AI governance within enterprise decision frameworks .

Yes. You’ll work directly with platforms like Scikit-learn, PuLP, and Streamlit to build predictive and prescriptive models, perform what-if simulations, and design explainable dashboards for decision intelligence.

You’ll gain skills in decision system design, optimization modeling, scenario simulation, AI explainability, and responsible governance, equipping you to engineer transparent and effective decision pipelines.

The course can typically be completed in 3 weeks, depending on your learning pace and engagement with readings, demonstrations, and graded hands-on assessments.

Basic familiarity with Python and data analytics is required. The course provides guided exercises using Python-based tools like Scikit-learn, PuLP, and Streamlit to make concepts approachable for all learners.

Completing this course can open roles such as Decision Intelligence Engineer, AI Solutions Architect, AI/ML Engineer, Business Intelligence Specialist, or AI Governance Consultant.

Yes, you’ll receive a verified certificate of completion, which can be added to your professional portfolio or shared on LinkedIn to demonstrate your expertise in Decision Intelligence.

The course includes videos, readings, demonstrations, and graded assessments across three progressive modules from foundational decision pipeline design to advanced optimization and governance frameworks.

Unlike traditional analytics or AI courses, this program focuses on the end-to-end decision lifecycle, integrating data, analytics, AI, and governance to create human-centered, explainable, and optimized decision systems.

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Financial aid available,

¹ Some assignments in this course are AI-graded. For these assignments, your data will be used in accordance with Coursera's Privacy Notice.