4-6 Months exhaustive roadmap for covering Data Science syllabus.
Week 1: Induction
Week 2-5: Programming
Week 2: Excel
Week 3: SQL
Week 4-5: Python
Week 6-7: Educational Case Study
Week 6: Housing Price Prediction (Regression)
Week 7: Titanic Survival Prediction (Classification)
Week 8: Statistics
Week 9: Resume Preparation
Important tips on creating resume. How & where to apply for jobs .
Week 10: Machine Learning Algorithms
Week 10: Decision Tree
Week 11: Linear Regression
Week 11-12: Business Case Study
- Data Science Project Life Cycle is 6 steps approach followed in any end-to-end Data Science project.
- Customer Analytics – Customer Lifetime Value (CLTV) is a prediction of revenue or profit a customer will give the company over the period of the relationship. Using past purchase history of customers, build a model to predict the Customer Lifetime Value (CLTV) for new customers.
- Customer Analytics – Propensity To Buy: Use web clicks data of the links clicked by the user on a website. Create a real time model to predict user’s propensity to buy the product while browsing. Using propensity, decide whether to offer chat option to the customer with an agent.
Week 13: Kaggle Case Study
- Housing Prices – Kaggle Case Study
- Machine Learning Algorithms
- Ensemble Models: Bagging/ Boosting
- Random Forest Algorithm
Week 14: Resume Review
Resume and online job profile review (Naukri, LinkedIn, etc.)
Week 15: Advance Algorithms
- Logistic Regression (Classification)
- K Nearest Neighbors (Classification)
- K Means (Unsupervised)
Week 16: Model Deployment
- Fraud Detection Case Study
- Model Deployment using Flask
Week 17: Mock Interviews
- Individual mock interviews and feedback sessions.
- Data Science Questions/Answers Knowledge Bank
Week 18-26: Job Assistance
- Updates regarding relevant jobs opening
- Job Referrals
- Interview Assistance