Data Science Course Syllabus & Modules
Data Science Course Syllabus
A high-impact roadmap designed to take you from data literacy to deploying production-grade Machine Learning and Generative AI models.
Learning Modules
Module 1: Mathematical Foundations
Mastering Linear Algebra, Calculus, and Probability & Statistics. These are the engines behind every algorithm.
Module 2: Python for Data Science
Deep dive into NumPy, Pandas for data manipulation, and Matplotlib/Seaborn for Exploratory Data Analysis (EDA).
Module 3: Machine Learning (Supervised & Unsupervised)
Regression, Classification, Clustering, and Ensemble methods (XGBoost, Random Forest). Includes feature engineering and hyperparameter tuning.
Module 4: Deep Learning & Neural Networks
Architecture of CNNs for vision, RNNs/LSTMs for sequences, and an introduction to Transformers using PyTorch/TensorFlow.
Module 5: Generative AI & LLMs (New for 2026)
Understanding Prompt Engineering, RAG (Retrieval-Augmented Generation), and fine-tuning Large Language Models for business use cases.
Module 6: Data Engineering & MLOps
SQL/NoSQL databases, Cloud deployment (AWS/Azure), and CI/CD pipelines for maintaining model performance in production.
Master Data Science in 6 Months
Join the 4Achievers immersive bootcamp. Experience 1:1 mentorship, real-world capstone projects, and 100% placement support.
Tech Stack Covered
- Python & SQL
- Scikit-Learn & PyTorch
- Tableau / Power BI
- Hugging Face & OpenAI API
- Docker & Kubernetes
Industry Insight
By 2026, companies aren't just looking for "model builders"—they want Data Strategists who understand the ROI of AI deployment. Our syllabus prioritizes business logic alongside code.
