What are the key steps in a data science project?

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Supervised and Unsupervised Learning are two primary types of machine learning, differing mainly in  The primary goal of a data science project is to extract actionable insights from data to support better decision-making, predictions, or automation—ultimately solving a specific business or real-world problem. 

A data science project usually follows a structured workflow to turn raw data into actionable insights or predictions. Here are the key steps 👇

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🔑 Key Steps in a Data Science Project

1. Define the Problem / Objective

   • Clearly understand what you are trying to solve.

   • Example: “Can we predict customer churn?” or “Which products should we recommend?”

2. Data Collection

   • Gather relevant data from multiple sources (databases, APIs, sensors, web scraping, logs, etc.).

   • Ensure data is sufficient, relevant, and reliable.

3. Data Cleaning & Preparation

   • Handle missing values, duplicates, and inconsistencies.

   • Convert raw data into a usable format.

   • Feature engineering (creating new useful variables).

4. Exploratory Data Analysis (EDA)

   • Explore datasets with statistics and visualizations.

   • Identify patterns, correlations, and outliers.

   • Form hypotheses about what factors affect the target outcome.

5. Model Building

   • Select appropriate algorithms (e.g., regression, classification, clustering).

   • Train machine learning models on the prepared dataset.

   • Tune parameters to improve performance.

6. Model Evaluation

   • Test the model on unseen data (validation/test set).

   • Use metrics like accuracy, precision, recall, F1-score, RMSE, etc., depending on the problem type.

7. Deployment

   • Integrate the model into a real-world system (web app, dashboard, API).

   • Ensure it can handle live data and scale as needed.

8. Monitoring & Maintenance

   • Track model performance over time.

   • Update or retrain models as new data becomes available.

   • Fix data drift or concept drift issues.

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📊 Summary

• What do we want to solve? → Problem definition

• What data do we need? → Data collection

• Is the data clean? → Preparation

• What does the data say? → EDA

• Can we build a predictive system? → Modeling

• Does it work well? → Evaluation

• Can it help in practice? → Deployment

• Will it keep working? → Monitoring

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👉 In short: Ask → Collect → Clean → Explore → Model → Evaluate → Deploy → Monitor.

Would you like me to also map these steps to a real-world case (like predicting loan defaults or recommending movies) so it feels more concrete?

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What is data science, and what does a data scientist do?

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