Course Outline
Foundations of Data Science and AI
- Extracting insights and knowledge from data
- Methods for representing knowledge
- Creating value through analytics
- Overview of Data Science principles
- The AI ecosystem and modern analytics approaches
- Essential technologies
The Data Science Workflow
- CRISP-DM methodology
- Preparing data for analysis
- Strategic planning for models
- Constructing predictive models
- Effective communication of results
- Model deployment strategies
Technologies in Data Science
- Languages utilized for rapid prototyping
- Big Data infrastructure
- Comprehensive solutions for common challenges
- Getting started with the Python language
- Connecting Python with Spark
Applying AI in Business
- Understanding the AI landscape
- Ethical considerations in AI
- Driving business transformation with AI
Data Sources and Management
- Classifying data types
- Comparing SQL and NoSQL databases
- Data storage strategies
- Techniques for data preparation
Statistical Data Analysis
- Principles of probability
- Core statistical concepts
- Building statistical models
- Implementing business applications in Python
Machine Learning Applications
- Distinguishing between supervised and unsupervised learning
- Addressing forecasting challenges
- Solving classification tasks
- Handling clustering problems
- Detecting anomalies
- Developing recommendation systems
- Mining association patterns
- Implementing ML solutions using Python
Deep Learning
- Identifying limitations of traditional ML algorithms
- Tackling complex problems with Deep Learning
- Getting acquainted with TensorFlow
Natural Language Processing
Visualizing Data
- Presenting modeling outcomes through visual reports
- Avoiding common visualization errors
- Creating visualizations with Python
From Data to Decision: Communication
- Driving impact through data-driven storytelling
- Ensuring effective influence
- Oversight of Data Science projects
Requirements
No 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
