Data Science & Big Data Analytics | CS PPT +Worksheets +Activity + Mcq Quiz + Answers_Marking Scheme

Data Science & Big Data Analytics | CS PPT +Worksheets +Activity + Mcq Quiz + Answers_Marking Scheme

1. Lecture PPT (14 slides, dark navy/cyan theme)
   Title → Objectives → What Is Data Science? (Venn diagram + industry impact) → Big Data 5 Vs (Volume, Velocity, Variety, Veracity, Value with stats) → Data Analytics Pipeline (5-stage waterfall) → Machine Learning & AI (supervised/unsupervised/reinforcement + NLP/CV/recommenders) → Data-Driven Decision Making (6 industry sectors) → Big Data Tools Stack (Kafka→Hadoop→Spark→sklearn→Tableau) → Ethics, Privacy & Security (6 issue panels) → Worked Example: Netflix Recommendation Engine → Worked Example: COVID-19 AI Analytics → Data Regulation & GDPR → Practice Questions → Summary
2. Worksheet (72 marks)
   Section A: Data science definition, DDDM, company examples table · Section B: Big Data definition, full 5 Vs table · Section C: Analytics pipeline stage-by-stage + why cleaning takes so long · Section D: ML types comparison table, Netflix deep-dive, AI business benefits · Section E: 4 ethical issues with examples, 4 GDPR rights, AI CCTV ethical evaluation
3. Class Activity — “Data Detectives”
   4 real client briefs (NHS / Supermarket / Police / EdTech) · 5 Vs analysis → pipeline design → ML algorithm selection → ethics & risk assessment table → board pitch presentation · Class debate on predictive policing, surveillance and AI accountability
4. MCQ Quiz (25 questions, 30 mins)
   5 sections: Data Science Foundations · Big Data & 5 Vs · Analytics Pipeline & Tools · Machine Learning & AI · Ethics, Privacy & GDPR
5. Answers & Marking Scheme (Teacher Copy)
   Full model answers with mark-by-mark tick points, examiner notes, complete model tables, MCQ answer key, and grade boundaries for both assessments (combined 97 marks)