History of DS

Expect tighter integration with real-time pipelines, generative AI copilots for analytics, and stronger governance to support reliable, scalable decision-making.

DS Workflow

• Discovery: Goals, KPIs, data audit.
• Prepare: Ingest, clean, join, feature build.
• Analyze: EDA, statistical tests, modeling.
• Communicate: Dashboards, narratives, decision docs.

Data Wrangling

Handle missingness, outliers, units, and schema drift; normalize and encode categorical data; enforce data quality with tests and contracts.

Statistics & Experimentation

• Inference: Confidence intervals, hypothesis testing.
• Causal: A/B tests, CUPED, diff-in-diff, matching.
• Uncertainty: Power analysis, MDE, sequential tests.

Visualization & Storytelling

Use clear charts, annotate key takeaways, and tailor the narrative to the audience. Prefer simple encodings; document assumptions and limitations.

ML in Data Science

From baselines (linear/logistic) to ensembles and deep models, choose the simplest model that meets requirements; prioritize interpretability and operational constraints.

Deployment & MLOps

Productionize with versioned datasets, reproducible builds, monitoring (SLIs/SLOs), and feedback loops. Collaborate with data engineering and platform teams.

Data Governance

Define ownership, access controls, lineage, documentation, and retention policies; align with privacy and compliance requirements.

Ethics in DS

• Fairness & bias: Checks across segments
• Privacy: By-design and minimization
• Transparency: Data docs, model cards
• Accountability: Risk management

Future of DS

Expect tighter integration with real-time pipelines, generative AI copilots for analytics, and stronger governance to support reliable, scalable decision-making.