About the role

⚠️ **SAMPLE LISTING — this is an illustrative placeholder internship, not a verified real opening.** Included for content/testing purposes only; do not present the employer, stipend, or apply link as confirmed until replaced with a real, verified posting. ## About This Internship Work with the analytics team on real business datasets — cleaning data, building models, and presenting findings that feed into actual product/business decisions. ## What You'll Learn - Data cleaning and preprocessing with pandas - Exploratory data analysis and visualization - Building and evaluating basic ML models (scikit-learn) - Communicating technical findings to non-technical stakeholders - Version control for data science projects (Git, notebooks best practices) ## Prerequisites - Basic Python programming knowledge - Familiarity with statistics fundamentals (mean, median, distributions) ## How to Prepare ### Technical Skills to Learn Before Applying Python basics, pandas and numpy fundamentals, data visualization (matplotlib/seaborn), basic SQL, an introduction to scikit-learn for simple models (regression, classification), free resources: Kaggle Learn micro-courses, freeCodeCamp Data Analysis with Python. ### How to Build a Strong Application - **Resume tips for freshers:** Highlight any data project (even a Kaggle competition or coursework analysis), mention specific libraries/tools used - **GitHub profile tips:** Include Jupyter notebooks with clear markdown explanations, not just code - **LinkedIn optimization:** Mention "Data Science" or "Machine Learning" in your headline if targeting these roles; share project summaries as posts - **Cover letter template:** Mention a specific dataset or problem you've analyzed and what you found interesting about it ## Interview Preparation for Interns - Common questions: Explain overfitting, what is a p-value, difference between classification and regression, how do you handle missing data, what is cross-validation - Coding test preparation: practice on Kaggle Learn exercises and basic pandas manipulation problems on StrataScratch (free tier) - How to talk about academic projects: explain your data cleaning steps, why you chose a particular model, and what the results meant practically ## Stipend & Benefits ₹18,000/month stipend, hybrid work mode, mentorship, certificate of completion, potential Pre-Placement Offer (PPO) based on performance (illustrative — confirm with actual employer). ## How to Apply Submit your resume along with a link to any data project (GitHub/Kaggle) via the listed application channel.