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Data Science Lifecycle - Coggle Diagram
Data Science Lifecycle
Problem Definition
Understanding the Business Problem
Translation Business Problem into Data Science Terms
Defining the Project Objectives
Data Collection
Identifying Data Sources
Data Scraping
Data Acquisition through APIs
Data Purchasing
Data Cleaning
Handle Missing Data
Noise Identification and Smoothing
Outlier Detection and Treatment
Data Exploration / Analysis
Descriptive Statistics
Data Visualization
Correlation Analysis
Exploratory Data Analysis (EDA)
Feature Engineering
Feature Extraction
Feature Encoding
Feature Selection
Feature Scaling
Predictive Modeling
Cross-Validation
Hyperparameter Tuning
Choosing the right model
Model Training
Model Evaluation
Confusion Matrix
Precision-Recall
ROC-AUC
Log-Loss
Model Deployment
A/B Testing
Monitoring Model Performance
Deployment Strategies
Updating Models
Model Interpretation
Feature Importance
Model Explainability Tools
Project Documentation
Code Documentation
Project Reporting