Professional Guide

A practical introduction connecting concepts, methods, and real-world applications

What is Data Science? How does it differ from Data Analysis?

Data Science is an umbrella discipline encompassing data management, statistical analysis, machine learning, and software engineering to develop data-driven solutions. Data Analysis focuses on extracting patterns and insights from data to answer specific questions.

Why it matters

  • Improves decision quality and reduces risk
  • Increases operational efficiency
  • Enables product and experience innovation

When to use it

  • When historical or streaming data is available
  • When a clear business question exists
  • When technical capacity for data processing is present

Systematic Workflow

1) Define the problem & success metrics

Formulate a clear question: What do we want to know? and define KPIs and timelines.

Example: Reduce cart abandonment by 10% within a quarter.

2) Data collection & cleaning

Unify sources, handle missing values, detect outliers, and document transformations (Data Lineage).

NULL handling • Outliers • Standardization

3) Exploratory Data Analysis (EDA)

Test hypotheses, use descriptive statistics, and visualize patterns and relationships.

“Good exploration saves 50% of modeling time.”

4) Modeling & Evaluation

Select an appropriate model (regression, classification, clustering) and evaluate using proper metrics and cross-validation.

Accuracy ROC-AUC RMSE

5) Visualization & Recommendations

Turn results into a story with visuals and dashboards, backed by actionable insights and follow-up plans.

  • Keep the message simple and persuasive
  • State assumptions and limitations
  • Provide “what-if” scenarios

Core Skills & Tools

Category Examples When to use
Querying SQL (SELECT, JOIN) To extract structured data from relational databases
Programming Analysis Python (pandas, NumPy) For cleaning, merging, transforming, and advanced analysis
Visualization Matplotlib, Plotly To tell the story visually and build dashboards
Modeling scikit-learn For classification/regression/clustering and performance evaluation
Engineering ETL/ELT, Airflow To automate data pipelines and ensure scalability
Note: Tool choice depends on context, data size, and budget.

Soft Skills

  • Clearly defining business problems
  • Effective stakeholder communication
  • Technical writing and documentation
  • Ethics and privacy awareness

Data Quality

Completeness 85%
Governance

Case Study: Product Pricing Optimization

  1. Goal: Increase revenue by optimizing pricing for a seasonal product.
  2. Data: Weekly sales, discounts, marketing campaigns, and weather conditions.
  3. Analysis: Linear regression with interaction terms to test price × season effect.
  4. Outcome: Demand elasticity −1.4 during peak season; recommendation: reduce discount by 5% and reallocate budget to digital ads.
Success Metrics
Revenue growth +3–5% Margin improvement +1.2 pts Reduced churn

Best Practices

  • Start with questions, not tools
  • Make data cleaning reproducible (scripts/notebooks)
  • Split data into training/validation/test
  • Assess sensitivity and scenarios
  • Provide actionable recommendations with timelines

Common Mistakes

  • Overfitting to noise
  • Ignoring data biases
  • Misusing metrics (e.g., Precision/Recall in wrong context)
  • Not documenting assumptions and transformations

Common Terms & Quick FAQ

Structured data is stored in tables (SQL), while unstructured data (text, images, audio) requires special preprocessing before analysis.

Descriptive tells you what happened, predictive forecasts what might happen, and prescriptive suggests what to do to achieve an objective.

Documentation of the data journey from its origin to final reports—ensuring transparency and auditability.

Data analysis provides a practical framework for turning data into impactful decisions. Start with a clear problem, build a trustworthy data pipeline, and deliver a compelling, evidence-based story.