The document outlines the key steps of the data science workflow, including problem definition, data collection and cleaning, exploratory data analysis, feature engineering, model selection, and model deployment. Each step emphasizes the importance of aligning with business objectives and using appropriate techniques for data handling and analysis. The document serves as a guide for effectively navigating the data science process.

1. INTRODUCTION
TO THE DATA
SCIENCE
WORKFLOW

2. Key Steps:
Problem Definition
Data Collection & Cleaning
Exploratory Data Analysis (EDA)
Feature Engineering
Model Selection & Evaluation
Model Deployment
The Data Science
Workflow:
A Step-by-Step Guide
01
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3. Problem Definition:
Align your work with business objectives.
Understand the problem and define clear
goals.
Step 1: Problem
Definition & Step
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4. Data Collection:
Gather data from various sources (databases,
APIs, spreadsheets).
Preprocess data to remove duplicates and
handle missing values.
Step 2: Data Collection
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5. Exploratory Data Analysis (EDA):
Visualize and analyze the data to identify
trends and patterns.
Use graphs (scatter plots, histograms, etc.) to
understand data distribution.
Step 3: Exploratory Data
Analysis (EDA)
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6. Feature Engineering:
Create meaningful features and transform data
(scaling, encoding).
Handle missing values and improve data for
modeling.
Step 4: Feature
Engineering
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7. Model Selection:
Choose the right model (classification,
regression, etc.).
Train and evaluate using appropriate metrics
(accuracy, precision, recall).
Step 5: Model Selection
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8. Model Deployment:
Deploy the model into production.
Monitor its performance and ensure its
scalability.
Step 6: Model
Deploymen
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9. KNOW MORE ABOUT
DATA SCIENCE ON
OUR WEBSITE
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https://bostoninstituteofanalytics.org/