Course Modules

Module 1: Data Science Foundations for Telecommunications

• Data science lifecycle
• Telecom data ecosystems
• Data-driven decision-making
• Industry challenges and opportunities
• AI applications in telecommunications
• Data ethics and governance
• Case Study & Practical Application: Assessing a telecom organization's data science maturity and identifying priority analytics initiatives.

Module 2: Python Programming for Telecom Analytics

• Python fundamentals
• Data structures
• Functions and modules
• File handling
• Object-oriented programming
• Automation using Python
• Case Study & Practical Application: Developing Python scripts to automate telecom KPI extraction and reporting.

Module 3: Data Collection, Cleaning and Preparation

• Data acquisition
• Data wrangling
• Missing value treatment
• Data transformation
• Feature engineering
• Preparing telecom datasets
• Case Study & Practical Application: Cleaning and preparing subscriber and network datasets for machine learning.

Module 4: Statistical Analysis and Data Visualization

• Descriptive statistics
• Inferential statistics
• Correlation analysis
• Hypothesis testing
• Dashboard development
• Interactive visualization
• Case Study & Practical Application: Creating an executive dashboard to visualize network performance and customer trends.

Module 5: Machine Learning for Telecommunications

• Regression algorithms
• Classification models
• Clustering techniques
• Model evaluation
• Telecom machine learning applications
• Feature selection
• Case Study & Practical Application: Developing a machine learning model to predict customer churn and recommend retention actions.

Module 6: Deep Learning and Artificial Intelligence Applications

• Neural networks
• Deep learning fundamentals
• Computer vision overview
• Natural Language Processing
• Generative AI fundamentals
• Telecom AI applications
• Case Study & Practical Application: Developing a deep learning solution for intelligent network anomaly detection.

Module 7: Predictive Analytics and Forecasting

• Time series forecasting
• Capacity planning
• Demand forecasting
• Revenue prediction
• Predictive maintenance
• Operational forecasting
• Case Study & Practical Application: Building predictive models to forecast network traffic and optimize infrastructure investments.

Module 8: APIs, Data Integration and AI Solution Development

• REST APIs
• Data integration
• Cloud services
• Automation workflows
• AI application development
• Telecom systems integration
• Case Study & Practical Application: Integrating network performance APIs into a Python-based analytics platform.

Module 9: Model Deployment, MLOps and Performance Monitoring

• Model deployment strategies
• MLOps fundamentals
• Continuous integration and deployment
• Model monitoring
• Performance optimization
• Explainable AI
• Case Study & Practical Application: Deploying and monitoring a machine learning model that predicts network equipment failures.

Module 10: Future AI Technologies and Enterprise AI Implementation

• Agentic AI
• Edge AI
• Federated learning
• AI for 6G networks
• AI governance
• Enterprise AI strategy
• Emerging trends in telecom analytics
• Case Study & Practical Application: Developing an end-to-end AI implementation roadmap for a telecommunications organization, integrating Python, machine learning, predictive analytics, automation, and emerging AI technologies to improve operational performance, customer experience, and digital service innovation.