Course Overview
The Advanced Data Analytics Using AI Algorithms course is a professional training program designed to empower participants to analyze big data, extract patterns and insights using artificial intelligence and machine learning techniques.
The course focuses on applying advanced algorithms for data analysis, trend prediction, and decision support, with a strong emphasis on practical application using cutting-edge tools and techniques.
Target Audiences
| · Data analysts and data scientists
· Artificial intelligence and machine learning specialists · Software developers · Strategic planning and analysis managers · Researchers and data enthusiasts · Anyone with a basic understanding of data analysis who wants to advance to a professional level |
Course Duration
- Total Hours: 25 ساعة تدريبية
- Number of Days: 5 أيام
- Hours per Day: 5 ساعات يومياً
Course Outline
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🗓️ Day 1: Fundamentals of Advanced Data Analysis 🔹Module 1: The Concept of Advanced Data Analysis Understanding the difference between traditional and advanced analysis and the role of artificial intelligence. 🔹Module 2: Data Types and Sources Identifying structured and unstructured data and how to work with them. 🔹Module 3: Data Preprocessing Cleaning and preparing data for analysis using advanced techniques. 🔹Module 4: Data Exploration (EDA) Initial data analysis to discover patterns and relationships. 🗓️ Day 2: Basic Machine Learning Algorithms 🔹Module 5: Regression Algorithms Using algorithms to predict numerical values. 🔹Module 6: Classification Algorithms Classifying data into categories using intelligent models. 🔹Module 7: Clustering Algorithms Dividing data into similar groups without supervision. 🔹Module 8: Model Evaluation Measuring model performance using precise metrics. 🗓️ Day 3: Deep Learning and Data Analysis 🔹Module 9: Introduction to Deep Learning Understanding how to use neural networks in data analysis. 🔹Module 10: Artificial Neural Networks (ANNs) Applying neural models to solve complex problems. 🔹Module 11: Data Analysis Using CNNs and RNNs Using advanced networks to process images and sequential data. 🔹Module 12: Improving Deep Model Performance Developing models to achieve more accurate results. 🗓️ Day 4: Tools and Practical Applications 🔹Module 13: Using Python for Data Analysis Applying analysis using the Python programming language. 🔹Module 14: Analysis Libraries (Pandas / NumPy / SciKitLearn) Using popular libraries for data analysis. 🔹Module 15: Data Visualization Presenting data visually to facilitate understanding. 🔹Module 16: Practical Applications in Real-World Projects Implementing data analysis projects using artificial intelligence. 🗓️ Day Five: Advanced Analysis and Decision Making 🔹Module 17: Forecasting Future Trends Using models to predict future outcomes. 🔹Module 18: Supporting Decision Making with Data Translating analysis into strategic decisions. 🔹Module 19: Deploying Models Applying models in the workplace. 🔹Module 20: The Future of Data Analytics and Artificial Intelligence Exploring future developments in this field. |
Course Outcomes
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By the end of the course, participants will be able to: · Understanding advanced data analytics techniques · Effectively using machine learning algorithms · Building accurate analytical and predictive models · Applying deep learning to data analysis · Using Python tools and libraries · Analyzing data and extracting strategic insights · Supporting data-driven decision-making · Implementing advanced data analytics projects |