Course Outline
Foundations of Data Science and Artificial Intelligence
- Acquiring intelligence through data utilization
- Frameworks for knowledge representation
- Strategies for value generation
- Comprehensive overview of data science
- Artificial intelligence ecosystem and advanced analytical methodologies
- Core enabling technologies
Data Science Lifecycle
- Cross-Industry Standard Process for Data Mining (CRISP-DM)
- Data preparation and preprocessing
- Model strategy and planning
- Model construction and development
- Stakeholder communication and reporting
- Operational deployment
Data Science Technology Stack
- Prototyping languages and tools
- High-volume data processing technologies
- Integrated solutions for common analytical challenges
- Python language fundamentals for government applications
- Integration of Python with Apache Spark
Artificial Intelligence in Organizational Contexts
- Composition of the AI ecosystem
- Principles of ethical AI
- Strategies for implementing AI in organizational operations
Data Acquisition and Management
- Classification of data types
- Comparative analysis of SQL and NoSQL databases
- Data storage architectures
- Data preparation processes
Statistical Analysis Methods
- Probability theory
- Statistical principles
- Statistical modeling techniques
- Business applications using Python
Machine Learning for Enterprise Solutions
- Supervised versus unsupervised learning approaches
- Forecasting methodologies
- Classification tasks
- Clustering analysis
- Anomaly detection
- Recommendation systems
- Association rule mining
- Implementation of machine learning solutions using Python
Deep Learning Fundamentals
- Limitations of traditional machine learning algorithms
- Application of deep learning to complex problems
- Introduction to TensorFlow
Natural Language Processing
Data Visualization Standards
- Visual reporting of modeling outcomes
- Common pitfalls in data visualization
- Implementation of data visualization using Python
Translating Data into Actionable Insights
- Data-driven narrative development
- Enhancing influence effectiveness
- Management of data science projects for government
Requirements
Testimonials (7)
Hands-on exercises related to content really helps to understand more about each topic. Also, style of start class with lecture and continue with hands-on exercise is good and helpful to relate with the lecture that presented earlier.
Nazeera Mohamad - Ministry of Science, Technology and Innovation
Course - Introduction to Data Science and AI using Python
Trainer expertise and ability to engage students
Nikita - EY GLOBAL SERVICES (POLAND) SP Z O O
Course - Introduction to Data Science and AI using Python
Ania has great knowledge and knows how to explain even complex topics.
Kasia - EY GLOBAL SERVICES (POLAND) SP Z O O
Course - Introduction to Data Science and AI using Python
The course is very interesting being the main focus nowdays
mohamed taher - FAB banak Egypt
Course - Introduction to Data Science and AI (using Python)
Ahmed was very interactive and didn’t mind answering any kind of questions Well presentation and smooth flow of the course
Mohamed Ghowaiba - FAB banak Egypt
Course - Introduction to Data Science and AI (using Python)
Helpful and good listener .. interactive
Ahmed El Kholy - FAB banak Egypt
Course - Introduction to Data Science and AI (using Python)
Subject presentation knowledge timing
