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life cycle

Introduction Reading Time: 12 min

Table of Contents

Description

The Data Science Life Cycle is a structured sequence of steps guiding data scientists to solve problems using data systematically — from data collection to model deployment and generating insights.

Prerequisites

  • Basic data science concepts
  • Data collection and preprocessing
  • Machine learning basics

Examples

Here's a simple example of a data science task using Python:


# Load data
import pandas as pd
data = pd.read_csv('data.csv')

# Clean data
data.dropna(inplace=True)

# Summary statistics
print(data.describe())

# Model training
from sklearn.linear_model import LinearRegression
X = data[['feature1', 'feature2']]
y = data['target']
model = LinearRegression()
model.fit(X, y)

# Prediction
predictions = model.predict(X)
print(predictions[:5])

Real-World Applications

Predictive maintenance in manufacturing

Customer churn prediction in telecom

Credit scoring in banking

Where topic Is Applied

Finance

  • Risk assessment and management
  • Algorithmic trading

E-commerce

  • Recommendation systems
  • Customer behavior analysis

Manufacturing

  • Predictive maintenance
  • Quality control

Resources

Data Science topic PDF

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Harvard Data Science Course

Free online course from Harvard covering data science foundations

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Interview Questions

Data collection, data cleaning, exploratory data analysis (EDA), feature engineering, model building, model evaluation, deployment, and monitoring

I handle missing values, remove duplicates, fix inconsistencies, and normalize data because clean data ensures model accuracy and reliability.

Creating new features, encoding categorical variables, scaling, normalization, and dimensionality reduction.

Training data is used to teach the model, while testing data evaluates its performance on unseen data.

Using techniques like cross-validation, confusion matrix, precision, recall, F1-score, and ROC-AUC.