Table of Contents
1 Descriptive vs Inferential Statistics
3 Data Exploration
(Linear Regression, Polynomial Regression)
(Logistic Regression, KNN, Decision Trees, Random Forest, etc.
2.3 Dimensionality reduction
1 Understand Objective
2 Data Collection
3 Data Preparation
(Data Cleaning, EDA, Feature Engineering, Feature Selection, Train/Validation/Test Split)
(Confusion Matrix, AUC-ROC, Lift Charts, Gain Chart, KS Statistic, F1 Score, etc.)
(R-squared (R2), RMSE, RSE, MAE, RAE)
6 Model Deployment
1.1 Classification: Titanic Survival Prediction
1.2 Regression: Boston Housing Prediction
1.1 Click Through Rate Prediction
1.2 Fraud Detection
6 Components of Data Science Syllabus
Statistics foundation is an import part of data science field. Cover all the important statistics topics required for data science.
6 steps approach of Data Science Project Lifecycle is followed in any end-to-end Data Science project.
Curated hands-on case studies with all the required explanation. Educational case studies are best for beginners and explore data science field. Business case studies contains capstone projects used in actual companies, ranging from retail, finance, healthcare domains,…
The last part of the puzzle is Interview Preparation, to be able to present and explain everything one has learned so far. This section will act as a guide and assist you in interview preparation.