Introduction to Data Science

Introduction to Data Science
Key concepts, workflow, tools, and career pathways

1. What is Data Science?

2. The Data Science Process (CRISP‑DM Inspired)

1. Business / Problem Understanding

   • Define objectives, success metrics, and constraints.
   • Identify stakeholders and required deliverables.

2. Data Acquisition

   • Sources: databases, APIs, web scraping, sensors, public datasets.
   • Consider data licensing, privacy, and security.

3. Data Exploration & Cleaning

   • Inspect data quality (missing values, outliers, inconsistencies).
   • Perform EDA (Exploratory Data Analysis) with visualizations and summary statistics.
   • Apply transformations: imputation, encoding, scaling, feature engineering.

4. Modeling / Analysis

   • Choose appropriate algorithms (regression, classification, clustering, time‑series, deep learning).
   • Split data (train/validation/test) and tune hyper‑parameters.
   • Evaluate using metrics aligned with business goals (e.g., accuracy, RMSE, AUC, F1‑score).

5. Interpretation & Communication

   • Translate model outputs into insights.
   • Use visual storytelling (charts, dashboards, reports).
   • Explain model behavior (feature importance, SHAP values, LIME).

6. Deployment & Monitoring

   • Package models as APIs, batch jobs, or embedded services.
   • Set up monitoring for data drift, performance decay, and resource usage.
   • Plan for maintenance, versioning, and reproducibility.

7. Feedback Loop

   • Collect new data, user feedback, and business results.
   • Iterate on earlier steps to improve the solution.

3. Fundamental Techniques

4. Core Tools & Libraries