Course Outline
Foundations of Data Science and AI
- Extracting knowledge from data
- Methods of knowledge representation
- Generating value through insights
- Comprehensive overview of Data Science
- The AI landscape and modern analytical approaches
- Essential technologies
Data Science Process
- Crisp-dm methodology
- Preparation of data
- Strategic model planning
- Construction of models
- Effective communication of results
- Implementation and deployment
Technological Tools for Data Science
- Languages suitable for prototyping
- Big Data infrastructure
- Complete solutions for common challenges
- Basics of the Python programming language
- Integration of Python with Spark
AI Applications in Business
- The AI ecosystem
- Ethical considerations in AI
- Strategies for driving AI adoption in business
Data Sources and Management
- Categories of data
- SQL versus NoSQL databases
- Data storage strategies
- Data preprocessing and preparation
Statistical Data Analysis
- Probability concepts
- Statistical principles
- Statistical modeling techniques
- Business applications implemented in Python
Machine Learning in Business Contexts
- Differences between supervised and unsupervised learning
- Forecasting challenges
- Classification tasks
- Clustering techniques
- Identifying anomalies
- Building recommendation engines
- Mining association patterns
- Addressing ML problems with Python
Deep Learning
- Scenarios where traditional ML algorithms fall short
- Tackling complex issues with Deep Learning
- Getting started with TensorFlow
Natural Language Processing
Data Visualization
- Presenting modeling outcomes visually
- Avoiding common visualization errors
- Creating visualizations with Python
Turning Data into Decisions – Communication
- Creating impact through data-driven storytelling
- Enhancing influence effectiveness
- Oversight and management of Data Science projects
Requirements
No specific prerequisites are required to enroll 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
