📄 Internship Offer Letter
⚠️ Please generate your Offer Letter before starting Week 1.

WEEK 1 – Python & Statistics Foundation
This week builds a strong foundation in Python programming and essential statistical concepts required for Data Science and Machine Learning. Students develop core coding skills while understanding how mathematical principles support data analysis and model building. This foundation prepares learners for advanced data science and machine learning concepts in the upcoming weeks.

WEEK 2 – Data Handling & Visualisation
This week covers the complete data handling and visualisation toolkit used by every professional data scientist. Students master NumPy for numerical computing, Pandas for data manipulation, Matplotlib and Seaborn for visualisation, and the full 5-step EDA pipeline. By the end of Week 2 students can take any raw dataset and transform it into clean, analysed, and visualised insights ready for machine learning.

WEEK 3 – Machine Learning
This week introduces machine learning from first principles and builds up to training, tuning, and evaluating production-ready models. Students implement regression and classification algorithms on real datasets, understand model evaluation at a professional level, and package their full workflow into reproducible sklearn Pipelines. By the end of Week 3 students can solve any standard supervised learning problem end to end.

WEEK 4 – Capstone Project & Internship Conclusion
Students apply all 10 lessons by building a complete end-to-end Data Science project from scratch. This week demonstrates full mastery of the data science pipeline — from raw data to a trained, tuned, and evaluated model with professional documentation. Successful completion unlocks all internship certificates.

🎓 Request Your Internship Completion Certificate & Letter of Recommendation

🎓 Training Completion Certificate Request

Lab — Statistics & Probability

Apply statistical concepts through 6 coding tasks covering Mean, Median & Mode, Variance & Standard Deviation, Probability Basics, Normal Distribution, Hypothesis Testing, and Correlation Analysis. Tasks progress from foundational calculations to full statistical analysis of real datasets. Completing all tasks confirms mastery of the mathematical foundation for ML.

🚀 Practice Lab
➡️ For best experience, open this lab in Full Screen

👉 Open Full Screen

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