Data Science Project Life Cycyle

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1. Understanding Business Problems
2. Data Collection
3. Data Wrangling and Preparation
   • Missing values
   • Duplicates
   • Inconsistent formatting
   • Improper formatting
4. Exploratory Data Analysis (EDA)
   • Univariate
   • Bivariate
   • Outliers
   • Variable transformation
   • Feature engineering
   • Correlation analysis
5. Model Selection, Building and Evaluation
6. Model Performance Communication
7. Model Deployment and Maintenance

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References

1. Data Science 101 : Life Cycle of a Data Science Project
2. Introduction to Life Cycle of Data Science projects