Data Science Workflow & Process
When students start learning Data Science, the biggest confusion is:
What is the actual process followed in real companies?
How do Data Scientists solve problems step-by-step?
This article explains the Data Science workflow like a teacher explaining in class — clear, simple, and with real examples.
What is a Data Science Workflow?
A Data Science Workflow is the step-by-step process used to solve a data problem — from understanding the business need to deploying the model.
It helps ensure:
Clear communication
Accurate analysis
Faster project execution
Repeatable and reliable results
Most companies follow a structured process similar to CRISP-DM, but simplified.
The 8-Step Data Science Workflow
Here are the eight essential steps used by Data Scientists in real-world projects:
Problem Understanding
Data Collection
Data Cleaning & Preparation
Exploratory Data Analysis (EDA)
Feature Engineering
Model Building
Model Evaluation
Deployment & Monitoring
Let’s explain each step clearly.
Problem Understanding (Define the Goal)
This is the most important step.
You answer questions like:
What problem are we solving?
What is the business objective?
What is the expected outcome?
Example:
A bank wants to predict loan default.
Objective: Identify customers who will likely not repay.
Data Collection
Data is collected from multiple sources:
Databases (SQL)
APIs
Websites
CSV/Excel files
IoT sensors
Cloud platforms
Third-party datasets
Example:
The bank collects data on customer income, past loans, credit history, etc.
Data Cleaning & Preparation (The MOST time-consuming step)
This step takes 60–70% of project time.
Cleaning includes:
Handling missing values
Removing duplicates
Fixing data types
Dealing with outliers
Standardizing formats
Example:
If income is missing, fill it with median salary or remove those rows.
Exploratory Data Analysis (EDA)
In EDA, we visualize and explore data to understand patterns.
Tasks:
Summary statistics
Correlation analysis
Histograms
Boxplots
Scatter plots
Example:
Check which features strongly impact the default rate.
Tools:
Python (Pandas, Matplotlib, Seaborn)
Power BI
Tableau
Feature Engineering
Feature Engineering means creating new useful variables that increase model accuracy.
Methods:
Encoding categorical data
Creating new ratios (e.g., income-to-loan ratio)
Normalization/Scaling
Binning
Feature selection
Example:
Create a new feature: “Debt-to-Income Ratio”.
Model Building (Where Machine Learning Happens)
Choose ML algorithms based on the problem type:
For classification (Yes/No):
Logistic Regression
Decision Trees
Random Forest
XGBoost
For regression (predict numbers):
Linear Regression
Gradient Boosting
Neural Networks
The model learns patterns from training data.
Model Evaluation
Evaluate performance using metrics:
Classification Metrics:
Accuracy
Precision
Recall
F1-score
AUC-ROC
Regression Metrics:
MAE
RMSE
R²
Example:
If recall is low, the model is missing many defaulters — adjust.
Deployment & Monitoring
After evaluation, the model is deployed in:
Web applications
Mobile apps
Cloud platforms (AWS, GCP, Azure)
Internal company dashboards
After Deployment:
Monitor performance
Retrain model with new data
Fix data drifts
Example:
The bank deploys the model to score new loan applicants in real time.
Real-Life Example: Netflix Recommendation System
Netflix uses a full Data Science workflow:
Problem → Recommend movies
Data → Watching history, search data, ratings
Cleaning → Remove incomplete logs
EDA → Find genre preferences
Features → “Time watched”, “Genre score”
Model → Collaborative filtering ML model
Evaluation → Measure recommendation accuracy
Deployment → Show suggestions on your homepage