Course Outline
Foundations of Data Science and AI
- Gaining insights from data
- Methods for representing knowledge
- Generating value through analytics
- Overview of Data Science concepts
- The AI ecosystem and modern analytics approaches
- Essential technologies
Data Science Methodologies
- CRISP-DM framework
- Preparing data for analysis
- Strategic model planning
- Constructing predictive models
- Effective communication of results
- Deployment strategies
Technologies in Data Science
- Languages suitable for prototyping
- Tools for Big Data processing
- Comprehensive solutions for common challenges
- Getting started with the Python language
- Integrating Python with Spark
AI Applications in Business
- Understanding the AI landscape
- Ethical considerations in AI
- Driving AI adoption in corporate settings
Data Sources and Management
- Categorizing data types
- Comparing SQL and NoSQL databases
- Data storage solutions
- Pre-processing data pipelines
Data Analysis via Statistical Methods
- Understanding probability
- Statistical analysis
- Building statistical models
- Business applications using Python
Machine Learning in Business
- Distinguishing between supervised and unsupervised learning
- Addressing forecasting challenges
- Handling classification tasks
- Solving clustering problems
- Identifying anomalies
- Building recommendation systems
- Mining association patterns
- Implementing ML solutions with Python
Deep Learning
- Scenarios where traditional ML methods are insufficient
- Complex problem solving with Deep Learning
- Introduction to TensorFlow
Natural Language Processing
Data Visualization
- Presenting modeling outcomes visually
- Avoiding common visualization errors
- Crafting visualizations with Python
From Data to Decision: Communication
- Creating impact through data storytelling
- Enhancing persuasive effectiveness
- Oversight of Data Science projects
Requirements
No prior specific prerequisites are required to participate 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
