Course Outline
Introduction to Data Science and Artificial Intelligence
- Acquiring knowledge through data analysis
- Methods of knowledge representation
- Generating organizational value
- Overview of Data Science principles
- The AI ecosystem and advanced analytical approaches
- Core technologies
Data Science Methodology
- CRISP-DM framework
- Data preparation processes
- Model planning strategies
- Model development
- Communication of findings
- Deployment and implementation
Data Science Technology Stack
- Programming languages for prototyping
- Big Data infrastructure
- Comprehensive solutions for standard challenges
- Fundamentals of the Python programming language
- Integration of Python with Apache Spark
Artificial Intelligence in Organizational Settings
- The AI ecosystem
- Ethical considerations in AI
- Strategies for implementing AI in business operations
Data Sources and Management
- Categories of data
- SQL versus NoSQL architectures
- Data storage solutions
- Data preprocessing techniques
Data Analysis – Statistical Frameworks
- Probability theory
- Statistical methodologies
- Statistical modeling techniques
- Business applications using Python
Machine Learning Applications
- Supervised versus unsupervised learning
- Predictive forecasting
- Classification tasks
- Clustering analysis
- Anomaly detection
- Recommendation systems
- Association pattern discovery
- Resolving machine learning problems with Python
Deep Learning
- Limitations of traditional machine learning algorithms
- Addressing complex problems with Deep Learning
- Introduction to TensorFlow
Natural Language Processing
Data Visualization
- Visualizing model outcomes
- Common visualization errors
- Creating visualizations with Python
From Data to Decision – Effective Communication
- Driving impact through data-driven narratives
- Enhancing influence and effectiveness
- Overseeing Data Science initiatives
Requirements
No specific prerequisites are required for participation in this course.
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
