📌 Overview¶
Data Science is an iterative process that transforms raw data into actionable insights and deployable models. Here’s a typical end-to-end workflow.
🧩 1. Problem Definition¶
Goal: Understand the domain problem clearly.
Tasks:
- Define the business/research question
- Set objectives and success metrics
- Understand constraints (data, time, compute)
Tools/Notes:
- Stakeholder interviews
- Domain knowledge
- Jupyter Notebooks for ideation
🗃️ 2. Data Collection¶
Goal: Gather relevant data from various sources.
Sources:
- CSV, Excel, Databases (SQL/NoSQL)
- APIs, Web scraping
- Sensor data, logs
Python Tools:
import pandas as pd
import requests
import sqlite3🧹 3. Data Cleaning¶
Goal: Prepare the data for analysis by handling missing or inconsistent data.
Tasks:
- Handle missing values
- Remove duplicates
- Outlier detection
- Data type conversion
Python Tools:
df.dropna(), df.fillna(), df.duplicated(), df.astype()📊 4. Exploratory Data Analysis (EDA)¶
Goal: Understand patterns, trends, and relationships in data.
Tasks:
- Summary statistics
- Data visualization
- Feature relationships
Python Tools:
import seaborn as sns
import matplotlib.pyplot as plt
sns.pairplot(df)
df.describe()
df.corr()🔧 5. Feature Engineering¶
Goal: Transform raw data into meaningful features.
Tasks:
- Normalization, scaling
- Encoding (One-Hot, Label)
- Binning, polynomial features
- Feature selection
Python Tools:
from sklearn.preprocessing import StandardScaler, OneHotEncoder🤖 6. Model Building¶
Goal: Train and evaluate predictive models.
Tasks:
- Split data: Train/Test/Validation
- Train model
- Cross-validation
Python Tools:
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.linear_model import LinearRegression🧪 7. Model Evaluation¶
Goal: Quantify model performance.
Metrics:
- Classification: Accuracy, Precision, Recall, F1, ROC-AUC
- Regression: RMSE, MAE, R²
Python Tools:
from sklearn.metrics import classification_report, confusion_matrix🔄 8. Model Tuning¶
Goal: Improve model with hyperparameter optimization.
Methods:
- Grid search, Random search
- Bayesian optimization
Python Tools:
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV🚀 9. Deployment¶
Goal: Make the model accessible to users or systems.
Methods:
- REST APIs with Flask/FastAPI
- Docker containers
- Streamlit/Dash apps
Tools:
Flask, FastAPI, Docker, Streamlit, GitHub Actions📈 10. Monitoring and Maintenance¶
Goal: Track model performance in production.
Tasks:
- Drift detection
- Retraining pipelines
- Logging and alerting
Tools:
- MLflow
- Prometheus + Grafana
- Airflow for pipelines
🔁 11. Iteration¶
Goal: Continuous improvement as new data and feedback arrive.
Approach:
- Close feedback loops
- Re-evaluate metrics
- Re-engineer features and models