Data Science Course Syllabus | Topics, Skills and Curriculum
Data Science Course Syllabus
The 2026 Industry-Standard Curriculum: From Foundations to Agentic AI.
Module 1: Mathematical Foundations
The BedrockUnderstanding the "Why" behind the algorithms. You cannot build what you don't understand.
Linear Algebra
Calculus (Optimization)
Descriptive Statistics
Inferential Statistics
Probability Distributions
Hypothesis Testing
Module 2: Programming & Data Wrangling
The ToolkitMastering the languages of data. 80% of a data scientist's time is spent here.
Python (Advanced)
SQL & NoSQL
Pandas & NumPy
Data Cleaning
Feature Engineering
Exploratory Data Analysis (EDA)
Module 3: Machine Learning & Modeling
The BrainBuilding predictive engines using supervised and unsupervised techniques.
Linear & Logistic Regression
Decision Trees & Random Forest
XGBoost & Gradient Boosting
K-Means Clustering
Hyperparameter Tuning
Model Evaluation (ROC/AUC)
Module 4: Advanced AI & Deep Learning
The FutureDeep diving into neural networks and unstructured data processing.
Neural Networks (CNN/RNN)
PyTorch & TensorFlow
NLP & Transformers
Computer Vision
Generative AI (LLMs)
Prompt Engineering
Module 5: MLOps & Deployment
The Industry-StandardHow to take a model from your laptop to a global production environment.
Docker & Kubernetes
MLflow (Tracking)
AWS/Azure/GCP Deployment
CI/CD Pipelines
Model Monitoring
Responsible AI & Ethics
Hands-on Capstone Projects
Project A: Predictive Healthcare
Build a model to predict cardiovascular diseases using EHR data with 95% recall.
Project B: Financial Bot with RAG
Implement a Retrieval-Augmented Generation (RAG) system for a real estate firm.
4Achievers Training Advantage
Our syllabus is updated weekly to reflect the 2026 job market in Roorkee, Haridwar, and Noida centers.
