Data Science

Data Collection is the foundational step in the data science lifecycle. It involves gathering raw data from a wide variety of sources to be used for analysis, modeling, and decision-making.

Databases

• • Structured data stored in relational databases like MySQL, PostgreSQL, or Oracle.

  • Common in enterprise systems, financial records, and customer databases.

• APIs (Application Programming Interfaces)

  • Interfaces provided by platforms (like Twitter, Google Maps, or weather services) to access live or historical data.

  • Data is typically in JSON or XML format.

• Sensors and IoT Devices

  • Used in smart devices, manufacturing, healthcare, and environmental monitoring.

  • Provide real-time, continuous data (e.g., temperature, pressure, motion).

• Spreadsheets & Flat Files

  • Data from CSV, Excel, or plain text files.

• Surveys & Forms

  • Manually collected data via Google Forms, Typeform, or similar tools.

This is the process of transforming raw data into a clean dataset that can be used for analysis and modeling. Real-world data is often incomplete, inconsistent, and noisy — cleaning ensures reliability.

Key Steps:

• Handling Missing Values: Impute (mean, median, mode) or remove rows/columns with too many missing values.

• Removing Duplicates: Use scripts to drop duplicate records.

• Data Type Conversion: Ensure proper formats for dates, numbers, text, etc.

• Standardization: Convert units, text formats, or date formats to a consistent style.

• Outlier Detection & Treatment: Identify outliers using IQR or z-scores and decide whether to keep, remove, or transform them.

EDA involves visualizing and summarizing the dataset to understand key patterns, spot anomalies, and generate hypotheses. It provides crucial guidance before modeling.

Key Techniques:

• Descriptive Statistics: Mean, median, standard deviation, skewness

• Visualization Tools: Histograms, boxplots, scatter plots, correlation heatmaps

• Univariate Analysis: Look at the distribution of individual features

This stage involves training machine learning models to make predictions or classifications based on the data.

Types of Models:

• Supervised Learning:
  • Regression: Linear Regression, Ridge, Lasso
  • Classification: Logistic Regression, Decision Trees, Random Forest, SVM, XGBoost, Neural Networks
• Unsupervised Learning:
  • Clustering: K-Means, DBSCAN
  • Dimensionality Reduction: PCA, t-SNE

Steps:

1. Choose a model based on problem type
2. Train it on the data
3. Tune hyperparameters
4. Validate using test sets or cross-validation

This phase evaluates the model’s performance using statistical metrics, helping to choose the best model or fine-tune it further.

For Regression:

• RMSE (Root Mean Squared Error)
• MAE (Mean Absolute Error)
• R² Score

For Classification:

• Accuracy
• Precision, Recall
• F1-Score
• Confusion Matrix
• ROC Curve & AUC Score

Model Deployment:

After selecting the best-performing model, the next step is deployment to make it accessible for end-users or systems.

After selecting the best model, deployment involves integrating it into a usable product that provides value to users or stakeholders.

Ways to Deploy:

• APIs: Use Flask or FastAPI to serve the model as a REST API
• Web Applications: Build dashboards or interfaces using Streamlit or Dash
• Mobile/Embedded Systems: Deploy lightweight models for edge computing
• Cloud Services: Use AWS, Azure, or GCP to scale and manage deployment

Monitoring Tools:

• Track model performance over time
• Re-train with new data when performance drops (model drift)