The Data Science – Specialist program is an advanced, career-focused training pathway designed for learners who want to move beyond foundational data science into high-performance, industry-level practice. Over 8 months (6 months of intensive training plus 2 months of internship), students gain mastery across advanced analytics, machine learning engineering, and deployment workflows.

This tier includes everything covered in the Professional level and expands into advanced modeling, scalable data systems, and production-ready AI solutions.

Module 1: Python for Data Science (Advanced Foundations)

Students strengthen their Python expertise with object-oriented programming (OOP), automation, API integration, virtual environments, and advanced error handling. Emphasis is placed on writing efficient, production-quality Python code suitable for scalable data systems.

Module 2: Advanced Data Cleaning & Feature Engineering

Learners tackle complex datasets using advanced missing value strategies, encoding techniques, scaling, normalization, and outlier detection. They build feature-rich datasets and implement introductory data pipelines, culminating in a comprehensive feature engineering project.

Module 3: Statistics & Probability for Machine Learning (Advanced)

This module deepens statistical thinking with Bayesian reasoning, maximum likelihood estimation, applied Central Limit Theorem concepts, regression diagnostics, multicollinearity analysis (VIF), and industry-focused A/B testing. Students complete hands-on statistical modeling projects to reinforce applied understanding.

Module 4: Advanced Machine Learning

Students implement and optimize high-performance models, including regularized regression (Lasso, Ridge, ElasticNet), advanced decision trees, and ensemble techniques such as Random Forest, Gradient Boosting, and XGBoost. The program also introduces hyperparameter tuning (GridSearchCV, RandomizedSearchCV), model interpretability tools (SHAP, LIME), and techniques for handling imbalanced datasets. A predictive modeling project reinforces end-to-end ML workflows.

Module 5: Advanced Unsupervised Learning

Learners explore clustering techniques (K-Means++, DBSCAN, Hierarchical), PCA and dimensionality reduction, anomaly detection, and introductory recommendation systems. The module concludes with a real-world unsupervised learning project.

Module 6: Introduction to Data Engineering for Data Scientists

Designed to prepare students for progression into AI Engineering, this module introduces ETL concepts, large dataset processing, cloud fundamentals (AWS, GCP, Azure), Python integration with cloud storage, and big data concepts including Spark. Students complete a real-world ETL mini-project to simulate modern data workflows.

Module 7: Model Deployment & MLOps Foundations

Students learn how to package and deploy machine learning models using Flask or FastAPI, understand Docker basics, monitor deployed systems, and explore CI/CD principles. This module bridges the gap between data science experimentation and production deployment.

Module 8: Advanced Capstone Project

The program culminates in a comprehensive end-to-end data science case study. Students perform problem framing, feature engineering, modeling, evaluation, and optional deployment. Each learner delivers a technical and business presentation, producing a portfolio-ready project aligned with industry standards.

Internship Experience (2 Months)

Students apply their skills in a supervised, real-world environment, gaining practical exposure to data workflows, stakeholder communication, and production systems.

Outcome

Graduates of the Data Science – Specialist program emerge with advanced technical expertise, deployment capability, data engineering awareness, and real-world project experience—positioning them for roles such as Data Scientist, Machine Learning Engineer (entry-level), Analytics Specialist, and AI-focused technical roles.