4-Month Data Science & AI Career Program
Duration: 4 Months Mode: Live Online Practical Learning + Real ProjectsLearn the skills that matter for Data Science roles: Python, SQL, Statistics, Pandas, EDA, Machine Learning, Deep Learning, and Deployment. Build 3–5 strong projects and become job-ready with resume and interview preparation.
Our structured, industry-focused program emphasizes hands-on learning with assignments, case studies, and real-world projects—supported by trainers and career guidance.
Practical Learning Methodology
Our hands-on approach ensures you master skills by building:
Learn
Interactive live concept sessionsPractice
Guided hands-on coding exercisesBuild
3–5 portfolio-ready projectsReview
Mentor code review & feedbackImprove
Model optimization & MLOps deploymentWho Should Join?
Students & Freshers
Build job-ready technical skills alongside your degree.
Working Professionals
Upskill into Data Science, Machine Learning, and AI.
Career Switchers
Structured, beginner-friendly path to transition into tech.
Career Support Pillars
Click on any module below to expand the detailed syllabus and learning outcomes:
- Python Core: Fundamentals, Data Structures, Functions, OOP principles
- Data Analysis: NumPy array operations, Pandas DataFrames, Data Cleaning & Transformations
- SQL Mastery: Joins, Subqueries, CTEs, Window Functions, Complex Business Queries
- Statistics: Probability distributions, Central Limit Theorem, Hypothesis Testing (t-test, Chi-square, ANOVA)
- EDA & Visualization: Univariate, Bivariate & Multivariate Analysis using Matplotlib & Seaborn
- ML Lifecycle: Preprocessing, One-Hot/Label Encoding, Feature Scaling, Cross-Validation
- Regression Models: Linear, Ridge, Lasso, ElasticNet & Performance Metrics
- Classification Models: Logistic Regression, KNN, Naive Bayes, Decision Trees, ROC-AUC
- Ensemble Techniques: Random Forest, Gradient Boosting (GBM), AdaBoost, XGBoost
- Unsupervised Learning: K-Means Clustering, Hierarchical, DBSCAN, PCA
- Model Optimization & Explainability: Hyperparameter Tuning (Grid/Random Search), SMOTE, SHAP
- Deep Learning Core: Neural Networks, Activations, Loss Functions, Optimizers, Regularization
- Computer Vision (CNN): Convolution, Pooling, Transfer Learning for Image Classification
- Sequential Data (RNN/LSTM): Time-series modeling and sequential prediction
- Production REST API: API Development with FastAPI, Pydantic data validation & Swagger docs
- Containerization: Dockerizing ML applications, environment variables & Git workflows
- MLOps Fundamentals: MLflow experiment tracking, model registry & CI/CD basics
Build a full end-to-end industry solution from problem statement to cloud deployment:
- Key Deliverables: Cleaned Codebase, Model Registry Run, Containerized Dockerfile, Swagger API, Portfolio Presentation
Industry Portfolio Projects
Hands-on projects designed to showcase your real-world capabilities to recruiters:
1. E-Commerce Business Analytics
Perform Exploratory Data Analysis, cohort retention analysis, and complex SQL database queries on sales transactions.
2. Customer Churn Prediction
Train binary classification models to identify churn-risk users with model explainability using SHAP values.
3. Customer Segmentation
Unsupervised clustering and Principal Component Analysis (PCA) to group users into actionable marketing segments.
4. CNN Image Classification
Deep learning computer vision application using Convolutional Neural Networks and Transfer Learning.
5. LSTM Time-Series Forecasting
Build sequential Recurrent Neural Networks (LSTM/GRU) for predicting trend & demand over time horizons.
6. Production End-to-End Capstone
Deploy an ML model as a production REST API microservice containerized with Docker and tracked with MLflow.
Tools & Tech Stack Mastered
Hover over tools to explore the technologies you will use in live projects:
Programming & Environment
Data Science & Analytics
Machine Learning & AI Frameworks
MLOps, Deployment & Cloud
Program features
- Duration 4 Months
- Mode Live Online
- Learning Hands-on + Projects
- Placement Assistance
