Course Overview
This course explores the integration of Artificial Intelligence (AI) techniques within the field of data science and analytics. Participants will gain a foundational understanding of AI concepts, machine learning algorithms, and their application to extracting insights from data. The course covers key tools and methodologies to leverage AI for predictive analytics, pattern recognition, and decision-making processes.
Course Objectives
By the end of this course, participants will be able to:
- Understand fundamental AI concepts and their role in data science.
- Apply machine learning algorithms for data analysis and predictive modeling.
- Utilize AI tools and frameworks commonly used in data analytics.
- Perform data preprocessing and feature engineering for AI models.
- Interpret and evaluate model results to drive informed business decisions.
- Recognize ethical considerations and challenges in AI-driven analytics.
Who Should Attend
This course is ideal for:
- Data scientists and analysts
- Business intelligence professionals
- IT professionals working with big data
- Managers and decision-makers interested in AI applications
- Students and researchers in data science and AI fields
Course Outline
Introduction to AI and Data Science
- Overview of AI, machine learning, and data science
- AI’s role in modern analytics and business intelligence
- Types of machine learning: supervised, unsupervised, reinforcement
Data Preparation for AI Models
- Data collection and cleaning
- Handling missing values and outliers
- Feature selection and feature engineering
Machine Learning Algorithms
- Regression analysis and classification
- Clustering and dimensionality reduction
- Neural networks and deep learning basics
AI Tools and Frameworks
- Overview of popular tools (Python, TensorFlow, Scikit-learn)
- Using AI platforms for model development
- Automation and AI pipelines
Model Training, Evaluation, and Optimization
- Training and testing datasets
- Performance metrics (accuracy, precision, recall, F1 score)
- Hyperparameter tuning and model optimization
Applications of AI in Data Analytics
- Predictive analytics and forecasting
- Pattern recognition and anomaly detection
- Natural language processing (NLP) basics
Ethical and Practical Considerations
- Data privacy and bias in AI models
- Transparency and explainability
- Future trends and challenges in AI analytics