1. Business Understanding
Identify the problem and define project objectives. Example: Predict student performance.
Data Science is an interdisciplinary field that combines Statistics, Mathematics, Programming, Machine Learning, and Domain Knowledge to extract meaningful insights from data. Organizations use Data Science to make informed decisions, predict future trends, improve efficiency, and solve real-world problems.
Data-driven insights help organizations make accurate and evidence-based decisions.
Machine learning automates repetitive tasks and improves productivity.
Predict future trends such as sales forecasting, disease prediction, and customer behavior.
Used in healthcare, finance, agriculture, education, and smart cities.
Identify the problem and define project objectives. Example: Predict student performance.
Gather data from databases, surveys, sensors, websites, and APIs.
Remove missing values, duplicates, noise, and inconsistencies.
Understand patterns, trends, correlations, and distributions using statistics and visualizations.
Create useful variables and transform data to improve model performance.
Apply Machine Learning algorithms such as Linear Regression, Decision Trees, and Random Forest.
Measure model accuracy using metrics like Precision, Recall, Accuracy, and F1-score.
Deploy the model into production for real-world use.
Continuously monitor model performance and update it when required.
Learn the Data Science Life Cycle step-by-step.