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 how they process and learn from data.

1. Define the Problem

• Goal: Understand the business or research objective.

• Activities:

  • Identify stakeholders

  • Frame a clear problem statement

  • Define success metrics

Example: "Predict customer churn for a telecom company within the next 30 days."

 2. Data Collection

• Goal: Gather all relevant data.

• Sources:

  • Internal databases

  • APIs

  • Web scraping

  • Public datasets

  • Sensors/IoT devices

 3. Data Cleaning & Preprocessing

• Goal: Prepare raw data for analysis.

• Tasks:

  • Handle missing values

  • Remove duplicates

  • Correct inconsistencies

  • Normalize or scale data

  • Encode categorical variables

This step often takes the most time—sometimes up to 80% of the project!

 4. Exploratory Data Analysis (EDA)

• Goal: Understand the patterns, relationships, and anomalies in the data.

• Tools:

  • Visualization (e.g., histograms, scatter plots, box plots)

  • Summary statistics

  • Correlation analysis

 5. Modeling / Machine Learning

• Goal: Build a predictive or descriptive model.

• Steps:

  • Select appropriate algorithms

  • Split data into training/testing (and sometimes validation)

  • Train the model

  • Tune hyperparameters (e.g., using Grid Search or Random Search)

6. Model Evaluation

• Goal: Assess how well the model performs.

• Metrics depend on the problem type:

  • Classification: Accuracy, Precision, Recall, F1-score, ROC-AUC

  • Regression: MAE, MSE, RMSE, R²

  • Use cross-validation to validate results

 7. Deployment

• Goal: Make the model accessible to users or systems.

• Options:

  • APIs (e.g., via Flask, FastAPI)

  • Integration into apps/websites

  • Cloud deployment (e.g., AWS, Azure, GCP)

 8. Monitoring & Maintenance

• Goal: Ensure the model remains accurate and useful over time.

• Tasks:

  • Monitor performance (drift detection)

  • Update data and retrain as needed

  • Handle scalability and latency issues

 Optional but Important:

• Documentation

• Reporting to stakeholders

• Version control 

• Collaboration tools

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

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