Course Outline
Overview of Data Science and AI
- Gaining insights from data
- Representing knowledge
- Generating value
- General perspective on Data Science
- The AI landscape and modern analytical approaches
- Core technologies
Data Science Methodology
- CRISP-DM framework
- Data pre-processing
- Strategy for modeling
- Developing models
- Communication of results
- Implementation and deployment
Data Science Tools and Technologies
- Languages utilized for prototyping
- Big Data infrastructure
- Comprehensive solutions for standard issues
- Basics of the Python programming language
- Connecting Python with Apache Spark
AI Applications in Business
- Understanding the AI ecosystem
- Ethical considerations in AI
- Driving AI adoption within business operations
Data Sources and Management
- Various types of data
- Comparison of SQL and NoSQL databases
- Data storage solutions
- Preparing data for analysis
Data Analysis: Statistical Methods
- Probability concepts
- Statistical fundamentals
- Building statistical models
- Applying Python for business statistics
Business-Oriented Machine Learning
- Distinguishing between supervised and unsupervised learning
- Predictive forecasting tasks
- Classification challenges
- Clustering tasks
- Identifying anomalies
- Developing recommendation systems
- Extracting association patterns
- Addressing ML problems with Python
Deep Learning
- Limitations of traditional machine learning
- Resolving complex issues via Deep Learning
- Getting started with TensorFlow
Natural Language Processing
Data Visualization
- Presenting model outcomes visually
- Avoiding common visualization errors
- Creating visualizations using Python
From Data to Decision: Effective Communication
- Creatin impact through data-driven narratives
- Enhancing influence effectiveness
- Oversight and management of Data Science projects
Requirements
No specific prior knowledge or requirements are necessary 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
