AI in Healthcare Policy and Innovation #254011

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This 5-day intensive course explores the powerful intersection of Artificial Intelligence (AI) and Data Analytics within the realm of Business Intelligence (BI). Participants will learn how to leverage AI techniques to enhance data analysis, automate insights generation, and create intelligent BI solutions that drive informed decision-making and improve business outcomes.

Upon successful completion of this course, participants will be able to:
• Understand the fundamental concepts of AI, Machine Learning (ML), and Deep Learning (DL).
• Apply AI techniques to enhance data analysis and business intelligence processes.
• Utilize AI tools and platforms for data mining, predictive modeling, and forecasting.
• Develop and deploy AI-powered dashboards and reports.
• Automate data cleaning, transformation, and integration tasks using AI.
• Extract actionable insights from complex datasets using AI algorithms.
• Communicate data-driven insights effectively to stakeholders.
• Understand the ethical considerations and best practices for AI in BI.

This course is designed for professionals involved in data analysis, business intelligence, and decision-making, including:
• Business Analysts
• Data Analysts
• Business Intelligence Developers
• Data Scientists
• IT Professionals
• Managers and Executives involved in strategic planning

• Pre-assessment
• Live group instruction
• Use of real-world examples, case studies and exercises
• Interactive participation and discussion
• Power point presentation, LCD and flip chart
• Group activities and tests
• Each participant receives a binder containing a copy of the presentation
• slides and handouts
• Post-assessment

• Introduction to Artificial Intelligence, Machine Learning, and Deep Learning
• Core Concepts of Business Intelligence: Data Warehousing, ETL, Reporting, Dashboards
• The Synergy of AI and BI: Enhancing Data Analysis and Insights
• Introduction to AI tools and platforms for BI

• Data Preprocessing and Feature Engineering with AI
• Data Mining Techniques: Clustering, Association Rule Mining
• Predictive Modeling: Regression, Classification, Time Series Analysis
• Hands-on Lab: Applying AI algorithms for data analysis using Python/R and relevant libraries

• Developing AI-powered dashboards and reports
• Automating data cleaning and transformation with AI
• Natural Language Processing (NLP) for BI: Text Analytics, Sentiment Analysis
• Hands-on Lab: Creating an intelligent dashboard with automated insights using a BI platform (e.g., Tableau, Power BI)

• Deep Learning for BI: Neural Networks and their applications
• AI for Forecasting and Predictive Analytics
• Recommendation Systems for Business Intelligence
• Case Study: Applying AI for business decisions in a specific industry (e.g., finance, healthcare)

• Ethical considerations in AI-driven BI: Bias, fairness, transparency
• Best practices for developing and deploying AI-powered BI solutions
• Future trends in AI and BI: Explainable AI (XAI), Generative AI
• Hands-on Project: Developing a comprehensive AI-driven BI solution for a real-world problem

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