Objective: Introduce participants to Google Colab and Jupyter Notebooks as platforms for AI development, and teach Python basics for telecom data analysis.

Detailed Explanation:

  • Google Colab: A free, cloud-based Python environment hosted by Google, ideal for running AI and machine learning tasks without local setup.
    • Features: Access to GPUs, pre-installed libraries (e.g., pandas, scikit-learn), and easy sharing of notebooks.
    • Telecom Example: Analyzing MTN Ghana’s customer data (e.g., call logs) to predict churn using Colab’s Python environment.
    • MTN-Specific Example: MTN Ghana analysts can use Colab to process anonymized Mobile Money transaction data, identifying patterns for fraud detection.
  • Python Basics:
    • Variables: Store data like customer IDs or call durations (e.g., call_duration = 120).
    • Loops: Iterate over datasets (e.g., looping through customer records to calculate average data usage).
    • Functions: Reusable code blocks (e.g., a function to compute churn probability).
    • Example Code: python
      # Calculate average call duration
      call_durations = [120, 300, 180] # Seconds
      avg_duration = sum(call_durations) / len(call_durations)
      print(f”Average call duration: {avg_duration} seconds”)
    • MTN Context: Python scripts can analyze MTN’s network traffic data to predict peak usage times.
  • Jupyter Notebooks: Interactive coding environments for data analysis, combining code, visualizations, and text.
    • Features: Run code in cells, visualize data (e.g., matplotlib plots), and document processes.
    • Telecom Example: Creating a Jupyter Notebook to visualize customer churn trends based on MTN’s subscription data.
    • MTN-Specific Example: MTN Ghana can use Jupyter Notebooks to explore 5G signal strength data, plotting coverage maps for Accra.
  • Benefits:
    • Google Colab eliminates hardware barriers, enabling MTN staff to experiment with AI.
    • Python and Jupyter Notebooks support rapid prototyping for telecom analytics.
PreviousNext