Data Science & Machine Learning
Practical Data Science program covering exploratory data analysis, statistical modeling, machine learning algorithms, and MLOps deployment in Python.
Course Overview
Master the complete data science workflow from data Wrangling and feature engineering to training predictive machine learning models and deploying them as REST APIs. Learn Scikit-Learn, Pandas, NumPy, and model evaluation techniques.
Quick Facts
Who Should Join
Target Audience
- Aspiring Data Scientists and Machine Learning Engineers
- Data Analysts advancing to predictive modeling and machine learning
- Software developers entering the Data Science domain
Expected Baseline Knowledge
- High school mathematics (basic algebra and statistics helpful)
- Basic computer literacy
- No prior coding experience required — Python covered from scratch
What You Will Learn
Curriculum Modules
3 ModulesEach module combines theoretical concepts, practical coding exercises, and milestone assessments.
Hands-on Student Projects
Clearly structured practice exercises and end-to-end simulated capstone projects.
Practice Project: House Price Prediction Model
Problem Addressed: Predict residential real estate prices based on property features and location metrics.
A regression model evaluating housing attributes with automated feature scaling.
- Jupyter notebook code
- Model evaluation metrics report
Simulated Business Project: E-Commerce Customer Churn & Segmentation Engine
Problem Addressed: An online retailer needs to predict which high-value customers are likely to churn next month and segment them for retention campaigns.
An end-to-end ML pipeline combining K-Means customer segmentation with an XGBoost churn prediction API service.
- Trained model package
- FastAPI server code
- Customer segmentation dashboard
Class Experience & Learning Environment
Live Sessions
Live interactive classes focusing on real-world datasets and algorithm coding.
Recordings & Access
HD recordings available after each class.
Assignments & Work
Weekly coding assignments with automated test checks.
Doubt & Support
Slack support channel with mentor assistance.
Lead Industry Trainer
Ananya Sharma
Lead Data Scientist
9+ Years in Predictive Analytics & Machine Learning Engineering
Schedule & Transparent Fees
Starts 2nd Saturday of Next Month
Weekend: Sat & Sun 10:00 AM - 1:00 PM
Standard fee with 3-installment payment plan
Full refund before 2nd live class.
360° Career Assistance Scope
Substantiated referral assistance and technical preparation without misleading promises.
Custom Data Science resume setup highlighting ML projects and GitHub portfolio.
2 Technical mock interviews covering Python, statistics, and ML algorithms.
Data Science interview question bank and scenario exercises.
Referral opportunities with hiring partner network.
Support Duration: 6 Months career assistance post-course.
- 80%+ class attendance
- Complete all projects
- Pass mock interview