What are the key steps in a data science project lifecycle? Get Best Data Analyst Certification Course by SLA Consultants India
Mar 7th, 2025 at 12:43 Learning Delhi 14 views Reference: 13Location: Delhi
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Key Steps in a Data Science Project Lifecycle
A data science project follows a structured lifecycle to ensure efficient problem-solving and data-driven decision-making. Below are the key steps involved:
1. Problem Definition
Before diving into data, clearly define the problem you aim to solve. Identify business objectives, stakeholders, and key performance indicators (KPIs). Understanding the problem helps in selecting the right approach and tools.
2. Data Collection
Gather relevant data from multiple sources such as databases, APIs, web scraping, or publicly available datasets. Ensure data quality, relevance, and completeness for better analysis.
3. Data Cleaning & Preprocessing
Raw data is often messy and requires cleaning. Handle missing values, remove duplicates, correct inconsistencies, and perform data transformation (e.g., encoding categorical variables, normalization, or scaling).
4. Exploratory Data Analysis (EDA)
EDA helps in understanding patterns, trends, and relationships within the data. Use statistical summaries, visualizations (histograms, scatter plots, box plots), and correlation matrices to gain insights and refine hypotheses.
5. Feature Engineering
Select, create, or modify features to improve model performance. Feature selection techniques like PCA, Lasso regression, and domain knowledge help in enhancing model efficiency.
6. Model Selection & Training
Choose appropriate machine learning models (e.g., linear regression, decision trees, neural networks) based on the problem type (classification, regression, clustering). Split data into training and testing sets, train models, and optimize hyperparameters for better accuracy.
7. Model Evaluation
Assess model performance using evaluation metrics like accuracy, precision, recall, F1-score, RMSE, and ROC-AUC. Compare multiple models and fine-tune parameters to enhance performance.
8. Deployment
Once a model performs well, deploy it into a production environment. Use tools like Flask, FastAPI, or cloud platforms (AWS, Azure, GCP) to integrate it into business applications.
9. Monitoring & Maintenance
Monitor the deployed model’s performance over time to detect data drift and degradation. Implement periodic retraining and updates to ensure accuracy and efficiency.
10. Communication & Reporting
Present findings to stakeholders through dashboards, reports, or visual storytelling. Tools like Power BI, Tableau, or Python libraries (Matplotlib, Seaborn) help in effective data visualization.
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