Course Outline
Foundations of Data Science and AI
- Extracting insights from data
- Methods of knowledge representation
- Generating business value
- Overview of Data Science
- The AI ecosystem and modern analytical approaches
- Essential technologies
Data Science Methodologies
- The CRISP-DM framework
- Data preprocessing and preparation
- Strategic model planning
- Constructing models
- Effective communication of results
- Deployment strategies
Technologies in Data Science
- Languages for rapid prototyping
- Big Data infrastructure
- Comprehensive solutions for common challenges
- Getting started with Python
- Integrating Python with Apache Spark
AI Applications in Business
- Understanding the AI landscape
- Ethical considerations in AI
- Implementing AI strategies in business operations
Data Sources and Management
- Categorizing data types
- Comparing SQL and NoSQL databases
- Data storage architectures
- Preprocessing data
Statistical Approaches to Data Analysis
- Probability theory
- Fundamentals of statistics
- Building statistical models
- Applying Python for business analytics
Business-Oriented Machine Learning
- Distinguishing between supervised and unsupervised learning
- Addressing forecasting challenges
- Tackling classification tasks
- Solving clustering problems
- Identifying anomalies
- Developing recommendation systems
- Mining association patterns
- Implementing ML solutions in Python
Deep Learning
- Addressing limitations of traditional ML algorithms
- Tackling complex issues with Deep Learning
- Introduction to TensorFlow
Natural Language Processing
Visualizing Data
- Presenting modeling results through visual reports
- Avoiding common visualization errors
- Creating visualizations using Python
Translating Data into Decisions: Communication
- Crafting impactful, data-driven narratives
- Enhancing the effectiveness of influence
- Overseeing Data Science projects
Requirements
There are no prior prerequisites or specific requirements for enrolling 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
