Data Science Internship
Data Science
Analyze data — Python, ML, Visualization, Real Projects
Learn Python, data analysis, visualization, machine learning, and deploy real-world data projects. Build a portfolio and get mentor support.
Duration 2 months
Eligibility Students & Graduates
Mode Remote + Onsite (Bangalore)
Small batch, weekly mentor reviews, data labs, and project cycles.
Certificate: Hallunix verified certificate on completion.
What you'll learn
End-to-end data science workflow — from data to deployment.
- Python for Data Analysis
- Data Cleaning & Visualization
- Machine Learning Basics
- Building Dashboards
- Model Evaluation & Tuning
- Deployment on Cloud (AWS/GCP)
- Version Control: Git & GitHub
Responsibilities
- Complete weekly data science tasks and submit for review
- Document experiments and results
- Participate in code reviews and data labs
- Present final project with deployment and documentation
Benefits
- 1:1 mentorship and code reviews
- Certificate & letter of recommendation
- Portfolio-ready data science project
- Interview prep & placement assistance for top performers
Timeline & Capstone
Onboarding
Week 0 — Setup & orientation
Core Modules
Weeks 1–6 — Data sprints
Capstone
Weeks 7–8 — Final project & presentation
Sample Certificate
Official Hallunix Internship Certificate
PYQs & Official FAQ
Q: Which data science library should I use?
A: Pandas for analysis, scikit-learn for ML, Matplotlib/Seaborn for visualization. All are covered in the program.
Q: Will I deploy a real dashboard?
A: Yes — the capstone includes deploying a dashboard or ML model on cloud.
Q: What is the certificate format?
A:
HTS/JUN25/INT-XX — Official Hallunix Internship Certificate with unique ID.Q: What are the official PYQs?
- Explain the difference between supervised and unsupervised learning with examples.
- How do you clean and preprocess data? List key steps.
- Describe steps to visualize data in Python using Matplotlib or Seaborn.
- How do you evaluate a machine learning model? List metrics and methods.
- How do you deploy a dashboard using Streamlit or Tableau?
- What is the role of Jupyter and Colab in data science?
- Explain the difference between regression and classification.
- How do you tune hyperparameters in ML models?
- What is cross-validation and why is it important?
- List steps for building a data science project from scratch.
Ready for data science?
Apply now — limited seats for hands-on mentorship.
Need help? Contact: hallunix.tech@gmail.com