Artificial Intelligence and Data Analysis

Details

AI and data analysis are reshaping industries by enabling organizations to process vast amounts of data, extract meaningful insights, and automate complex tasks. This course bridges the gap between AI technologies and data analysis, empowering participants to use advanced tools and methodologies to solve real-world problems. Participants will learn the fundamentals of AI, data preprocessing, predictive modeling, and visualization techniques while exploring applications of AI in decision-making and strategic planning.Through hands-on exercises, case studies, and group discussions, participants will gain practical experience in leveraging AI and data analysis tools to derive actionable insights and enhance organizational performance.

Target Group

Target Group:

• Data analysts and business analysts
• IT and AI professionals seeking to integrate AI with data analysis
• Managers and decision-makers looking to adopt AI-driven insights
• Professionals in marketing, finance, healthcare, and operations seeking data-driven strategies
• Individuals interested in building AI and data analysis expertise

Goals

• Understand the fundamentals of Artificial Intelligence and its applications in data analysis.
• Learn techniques for preprocessing and cleaning data for AI applications.
• Gain proficiency in predictive modeling, machine learning, and AI-driven data analysis.
• Explore tools and platforms for AI-powered data analytics.
• Develop skills to visualize and communicate AI-driven insights effectively.
• Implement AI and data analysis techniques in solving business challenges.
• Understand ethical considerations and best practices in AI-driven data analysis.

Target Competencies

Targeted Competencies

• Proficiency in AI techniques and their integration with data analysis
• Skills in data preprocessing, predictive modeling, and machine learning
• Expertise in using AI tools and platforms for advanced analytics
• Ability to visualize and communicate AI-driven insights effectively
• Knowledge of ethical considerations and best practices in AI applications

Outlines

Introduction to Artificial Intelligence and Data Analysis

• Overview of AI and its role in modern data analysis
• Understanding key AI concepts: Machine learning, deep learning, and NLP
• Differences between traditional data analysis and AI-driven analysis
• Case studies on AI applications in data-driven decision-making

Data Preparation and Preprocessing for AI

• Techniques for cleaning, organizing, and structuring data
• Handling missing data, outliers, and imbalanced datasets
• Feature selection and engineering for AI models
• Hands-on exercises in preparing datasets for AI applications