Curriculum

Data Science Lifecycle

Introduction

Data Science is all about extracting meaningful insights and knowledge from raw data using scientific methods, algorithms, and tools.
But to get from raw data to useful decisions, data scientists follow a structured process known as the Data Science Lifecycle.

This lifecycle guides every project — from understanding a problem to deploying a final model in production.

🧭 What is the Data Science Lifecycle?

The Data Science Lifecycle is a step-by-step process that defines how data science projects are executed.
It involves several stages — data collection, preparation, analysis, modeling, and deployment — ensuring that business problems are solved using data-driven decisions.

Each stage is connected and iterative, meaning data scientists often go back and refine previous steps as they learn more.

⚙️ Key Stages of the Data Science Lifecycle

Let’s go through each stage in detail 👇

1️⃣ Problem Definition

Before working with data, the first step is to understand the business problem you’re trying to solve.

Example:

A retail company wants to predict customer churn.
A hospital wants to forecast disease risks.

In this stage, data scientists work with business teams to clearly define:
✅ What problem are we solving?
✅ What is the goal or metric (sales, accuracy, customer retention)?
✅ What data is needed?

Outcome: A clear problem statement and project objective.

2️⃣ Data Collection

Once the problem is defined, the next step is to gather relevant data from different sources.

Data Sources Include:

Company databases (CRM, ERP, etc.)
APIs and web scraping
Surveys or sensors (IoT devices)
Public datasets (Kaggle, UCI, Government portals)

Tools Used: SQL, Python (Pandas, Requests), APIs, BeautifulSoup, Power BI connectors.

Goal: Collect raw, relevant, and sufficient data for analysis.

3️⃣ Data Cleaning & Preparation

Raw data is often incomplete, inconsistent, or contains errors.
This stage focuses on cleaning, transforming, and preparing the data for analysis.

Common Tasks:

Handling missing values
Removing duplicates
Dealing with outliers
Normalizing and scaling data
Feature engineering (creating new features)

Tools Used: Python (Pandas, NumPy), Excel, Power Query.

💡 This step takes almost 60–70% of a data scientist’s time because clean data = better results.

4️⃣ Data Exploration & Analysis (EDA)

Now comes the Exploratory Data Analysis (EDA) stage.
Here, data scientists study data patterns, trends, and relationships to gain insights.

Tasks Include:

Understanding data distribution
Checking correlations between variables
Creating visualizations (bar charts, scatter plots, heatmaps)
Detecting hidden patterns

Tools Used: Python (Matplotlib, Seaborn), Power BI, Tableau.

Goal: Understand what the data tells us before building models.

5️⃣ Data Modeling

This is the core stage of Data Science — where machine learning algorithms are used to make predictions or classifications.

Steps:

Split data into training and testing sets.
Choose the right model/algorithm (Linear Regression, Decision Trees, Random Forest, etc.)
Train the model on historical data.
Evaluate its performance using metrics like accuracy, precision, recall, F1-score, or RMSE.

Tools & Libraries:

Python (Scikit-learn, TensorFlow, PyTorch)
R, Jupyter Notebook

💡 Goal: Build a model that can predict or classify future outcomes accurately.

6️⃣ Model Evaluation

After training the model, it must be tested and validated to ensure reliability.

Evaluation Metrics:

Classification: Accuracy, Precision, Recall, F1-score
Regression: Mean Squared Error (MSE), R² score
Clustering: Silhouette score

If performance is poor, data scientists tune hyperparameters, change algorithms, or re-clean data.

Outcome: Best-performing model ready for deployment.

7️⃣ Deployment

Once the model performs well, it’s deployed into production so real users or systems can use it to make predictions.

Example:

E-commerce sites using recommendation systems.
Banks using fraud detection models.
Healthcare apps predicting disease risk.

Deployment Platforms: AWS, Azure, Google Cloud, Flask, FastAPI, Streamlit.

💡 Goal: Integrate the model into real-world applications for continuous use.

8️⃣ Monitoring & Maintenance

After deployment, the model’s performance must be monitored regularly.
As new data comes in, models can degrade (known as data drift), so they must be updated and retrained.

Tasks:

Track model accuracy over time
Collect feedback from users
Update data pipelines and retrain models

Goal: Keep the model accurate, relevant, and useful long-term.

🧠 Summary of the Data Science Lifecycle

Stage	Description	Tools Used
Problem Definition	Define business goals	Meetings, Docs, KPIs
Data Collection	Gather raw data	SQL, APIs, Python
Data Cleaning	Prepare and preprocess	Pandas, Excel
Data Exploration	Analyze patterns	Power BI, Tableau
Data Modeling	Apply ML algorithms	Scikit-learn, TensorFlow
Model Evaluation	Test and validate	Confusion Matrix, RMSE
Deployment	Make model live	Flask, AWS, Streamlit
Monitoring	Maintain performance	Cloud dashboards

Conclusion

The Data Science Lifecycle ensures that every data project follows a logical, efficient, and repeatable process.
From identifying a problem to deploying and maintaining a solution, each step is crucial for creating accurate, reliable, and business-driven results.

💡 “Data science is not just about models — it’s about solving real-world problems using data.”