Data and artificial intelligence

Bag code: SCSAI119315026 Days: 3 days "Today training 5 hours" Time: 15 training hours Targeted participants: • Trainers • Students interested in artificial intelligence and data science Program overview: This program revolves around data and artificial intelligence (definition – types – characteristics – objectives – positives – negatives .. etc.) and will be presented through the following units: First training unit: Data and Artificial Intelligence Basics Second training unit: Data analysis and initial applications Third training unit: Artificial intelligence and its applications Program objectives: • To familiarize the trainee with the concept of data and its types. • To enable the trainee to identify the basic tools used in data analysis. • To apply basic statistical concepts and graphical visualization methods in data analysis. • To distinguish between types of machine learning. • To demonstrate awareness of the importance of artificial intelligence applications in daily life. • To complete a simple applied project in data analysis and participate in performance evaluation. Program topics: First unit: (Data and Artificial Intelligence Basics) 1. Concept of data and its types • Types of data • Definition of data in the digital age • Organized and unorganized data • Big data 2. Introduction to artificial intelligence • What is artificial intelligence? • The difference between artificial intelligence and machine learning • Fields of artificial intelligence use 3. Data lifecycle • Data collection • Data cleaning and filtering • Data storage • Preparing data for analysis 4. Basic data analysis tools • Data analysis programs (Excel, Python, R) • Graphical tables and visualizations • Filtering and aggregation tools • Introduction to data queries Second unit: (Data analysis and initial applications) 5. Concepts of statistical data analysis • Frequencies, mean, standard deviation • Statistical distributions • Using statistics to infer results 6. Data visualization • Types of data visualization • Heat maps • Visual data tools 7. Principles of machine learning • The difference between artificial intelligence and machine learning • Types of machine learning 8. Data and predictive analytics • What is predictive analytics? • Simple prediction models • Using data to forecast future results • Measuring the performance of prediction models Third unit: (Artificial intelligence and its applications) 9. Artificial intelligence in daily life • AI in education • AI in health • AI in commerce • Practical examples 10. Ethics, data, and artificial intelligence • Protecting personal data • Bias in AI systems • Responsibility for automated decisions • Regulations governing AI 11. Virtual data analysis using artificial intelligence • How AI analytics work • Preparing data analysis reports • Sharing results 12. Measuring and evaluating trainees • Evaluation tools in training • Short test (multiple choice questions) • Practical task assessment • Constructive feedback Contents of the PowerPoint package: 150 Trainer’s guide: 121 Trainee’s guide: 119 Activities: 6 Introductory guide: 4 References: 6 Evaluation and data forms: 6 Pre- and post-tests: 6 Videos: 6 Images: 18 Mind maps: 1 Brochure: 1 Contents of the package: 12 Training package model: Please click here to view the training package model