📄 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.

Theory — Data Science Capstone Project
Lab — Data Science Capstone Project

🎓 Request Your Internship Completion Certificate & Letter of Recommendation

🎓 Training Completion Certificate Request

Final Presentation & Review lesson (updated text)

🎤 Capstone Evaluation & Certification

You have completed all four weeks of the Synkoc Data Science Internship. Now it is time for your instructor evaluation.

What to Submit:

  • Your completed Jupyter Notebooks for both Capstone Projects (choose any 2 from the 10 available datasets)
  • A PDF project report following the provided documentation template (Problem Statement → EDA → Feature Engineering → Model → Results → Business Insights)
  • A 10–15 slide presentation summarising your dataset, approach, key EDA findings, model performance, and actionable insights

Instructor Evaluation: After you submit, your Synkoc instructor will review your projects and schedule a 1-on-1 evaluation session to discuss your work and ask questions about your data science methodology, modelling decisions, and results interpretation.

Evaluation Criteria:

  • Technical correctness of the Data Science pipeline — EDA, feature engineering, model training, and tuning (40%)
  • Quality and depth of Exploratory Data Analysis and feature engineering decisions (25%)
  • Clarity of documentation, presentation, and business insight communication (20%)
  • Code quality, reproducibility, and correct use of sklearn Pipelines (15%)

After successful evaluation — within 24 hours:

  • ✅ Internship Completion Certificate
  • ✅ Training Certificate
  • ✅ Capstone Project Completion Certificate
  • ✅ Letter of Recommendation (performance-based)
  • ✅ Offer Letter from Synkoc IT Services Private Limited

📧 Submit your project to: support@synkoc.com

Your instructor will contact you within 2 working days to schedule the evaluation session.

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