📊 Introduction to Data Science

1Problem Definition

Understanding the problem you want to solve.

Key Activities:

• Collaborate with stakeholders
• Define success metrics
• Set project goals and deliverables

2Data Collection

Gathering relevant data from multiple sources.

Sources may include databases, APIs, web scraping, or third-party datasets.

Key Activities:

• Identify data sources (structured vs. unstructured)
• Collect data using SQL, Python, or automated pipelines
• Ensure data relevance and completeness

3Data Cleaning (Data Preprocessing)

Preparing raw data for analysis. This step addresses missing values, duplicates, and inconsistencies.

Key Activities:

• Handle missing or incorrect data
• Standardize formats and remove duplicates
• Manage outliers and inconsistencies

4Data Exploration (EDA – Exploratory Data Analysis)

Analyzing data patterns and relationships. Understanding data distributions and detecting anomalies using visualizations.

Key Activities:

• Summarize data using statistics (mean, median, etc.)
• Visualize patterns (using Matplotlib, Seaborn, etc.)
• Identify correlations and outliers

5Model Building

Creating and training machine learning models. Use algorithms to predict outcomes or classify data.

Key Activities:

• Choose appropriate models (e.g., regression, decision trees, neural networks)
• Split data into training and testing sets
• Train and fine-tune models

Common Tools: Scikit-learn TensorFlow PyTorch

6Model Evaluation

Measuring model performance and accuracy. Evaluate models using metrics to ensure reliability.

Key Activities:

• Use performance metrics (e.g., accuracy, RMSE, ROC curve) to answer: "How often is my model correct?"
• Perform cross-validation for robustness
• Compare multiple models for best outcomes

Key Metrics:

• Classification: Accuracy, Precision, Recall, F1-Score
• Regression: RMSE, R-squared

7Deployment

Integrating the model into production systems. Deliver actionable results through APIs or dashboards.

Key Activities:

• Package the model for deployment (usually done using web frameworks like Flask, and FastAPI)
• Automate pipelines for continuous learning (MLOps)
• Monitor performance post-deployment

8Communication & Reporting

Sharing insights with stakeholders. At the end of the day, the ML model solves a problem and proper reporting to the concerned department is very important.

Key Activities:

• Create dashboards
• Present findings clearly and concisely
• Document the process and results

9Maintenance & Iteration

Keeping the model accurate and up-to-date.

Key Activities:

• Monitor model performance
• Update models with new data
• Refine features and parameters