Project: Future-Proofing IT Careers ☁️📊
Overview
This 6-month, highly selective internship was designed to bridge the gap between academic theory and industry demands in the high-growth fields of Cloud Computing and Data Science.
Participants and Structure
- Cohort Size: 25 final-year IT/Engineering students.
- Format: Hybrid (4 months of intensive online training followed by 2 months of on-site project work).
Detailed Curriculum: Data Science Track
The Data Science track utilized a Project-Based Learning (PBL) approach, culminating in a real-world predictive modeling task for a local NGO.
# Sample Code Snippet from Module 3: Model Evaluation
from sklearn.metrics import accuracy_score, precision_score
# Assuming y_true and y_pred are defined
accuracy = accuracy_score(y_true, y_pred)
precision = precision_score(y_true, y_pred, average='weighted')
print(f"Model Accuracy: {accuracy*100:.2f}%")
Key Skills Acquired
- Programming Proficiency: Advanced Python and R.
- Cloud Platforms: AWS/Azure Fundamentals.
- Statistical Modeling: Regression, Classification, Clustering.
- Big Data Tools: Introduction to Spark and Hadoop concepts.
Project Outcomes and Success Metrics
We tracked the professional placement of the participants closely. 18 out of 25 interns secured full-time employment in their specialized field within three months of graduation. The remaining individuals opted to pursue further academic studies abroad.
Cloud Computing Deep Dive
The Cloud track focused on securing DevOps principles and practicing Infrastructure as Code (IaC) using Terraform, preparing students for highly demanded roles in software deployment and management. The final project involved deploying a containerized application to a scalable Kubernetes cluster.
