The data science life cycle typically consists of the following stages
Problem Definition: Define the problem or question you want to answer using data science techniques.
Understand the business objectives and goals.
Data Collection: Gather relevant data from various sources, which may include databases, APIs, files, or
other means.
Data Preprocessing: Clean and prepare the data for analysis. This involves tasks like
handling missing values, data transformation, and feature engineering.
Exploratory Data Analysis (EDA): Explore the data to gain insights and identify patterns. Visualizations
and statistical analyses are often used in this stage.
Feature Selection: Choose the most relevant features or variables to use in your models. This helps
improve model performance and reduce complexity.
Model Building: Create machine learning or statistical models to address the problem. This involves selecting algorithms, training models, and fine-tuning parameters.
Model Evaluation: Assess the performance of your models using appropriate metrics. This helps you
understand how well your models are doing and whether they need improvement.
Model Deployment: Deploy the models in a real-world environment, often as part of a larger software
system or application.
Monitoring and Maintenance: Continuously monitor the deployed models, retrain them, if necessary,
and ensure they perform well over time.
Communication of Results: Share your findings and insights with stakeholders through reports,
dashboards, or presentations
Feedback and Iteration: Use feedback from stakeholders to make improvements and iterate on the data
science process.

The data science life cycle is often iterative, with stages revisited as needed to improve results or
address changing requirements