What is the Data Science Workflow | IABAC

The Data Science Workflow is a structured process for extracting insights from data, involving problem definition, data collection, cleaning, analysis, modeling, evaluation, and deployment. It ensures systematic, accurate, and actionable results for informed business decisions.

1. What is the Data
Science Workflow? iabac.org‌

2. Introduction to Data Science Workflow
Data Science Workflow is a structured sequence of steps used
to extract insights from data.‌
Enables systematic analysis and decision-making.‌
Helps teams handle complex datasets efficiently.‌
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3. Key Steps in Data Science Workflow
Problem Definition – Identify business objectives and questions.‌
Data Collection – Gather data from relevant sources.‌
Data Cleaning & Preparation – Handle missing values, inconsistencies, and
transform data.‌
Exploratory Data Analysis (EDA) – Understand patterns, trends, and
correlations.‌
Modeling & Algorithm Selection – Apply statistical or ML models.‌
Evaluation & Validation – Test model accuracy and reliability.‌
Deployment & Monitoring – Implement solutions and track performance.‌
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4. Importance of Data Science Workflow
Ensures systematic, repeatable processes.‌
Improves decision-making with accurate insights.‌
Reduces errors and inconsistencies.‌
Facilitates collaboration across teams.‌
Enhances efficiency and scalability of data projects.‌
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5. Conclusion
Data Science Workflow structures analysis from problem to
solution.‌
Following the workflow increases reliability and impact of
insights.‌
Essential for businesses aiming to leverage data strategically.‌
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6. Thank you
Visit: www.iabac.org‌
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