π Exam Info
- Format: 4 structured questions
- Coverage: Weeks 1, 2, 5, 7 + core analytics concepts
- Tip: Keep answers concise; use tables/keywords; calculator allowed
πΈ Visual Quick Reference (Henry Notes)
Images from Henryβs review pack.
Page 1
Page 2
Page 3
β
Predicted 4 Major Question Types (Week 1/2/5/7)
Q1: Concepts of Analytics (Chapter 1)
Q1(a) Four Types of Analytics
| Type |
Key Idea |
Question Answered |
Example |
| Descriptive |
Past facts |
What happened? |
Monthly sales report |
| Diagnostic |
Root cause |
Why did it happen? |
Churn analysis |
| Predictive |
Probabilities |
What will happen? |
Demand forecast |
| Prescriptive |
Optimization |
What should we do? |
Optimal pricing |
Answer tip: Use a table and keep it short.
Q1(b) BA vs DS Skill Focus
| Skill |
BA |
DS |
Key Difference |
| Business Knowledge |
Very High |
High |
BA is business-heavy |
| Data Visualization |
Very High |
High |
BA focuses on reporting |
| Statistics/Math |
Some |
Very High |
DS is math-heavy |
| Machine Learning |
None/Low |
Very High |
DS focuses on modeling |
| Data Wrangling |
Some |
Very High |
DS spends more time on prep |
Summary: BA = business communication; DS = modeling and statistics.
Visual: Analytics Types vs Roles

Q2: Analytics Lifecycle (Chapter 2)
Q2(a) Six Phases + Key Tasks
- Discovery β define business problem, assess resources
- Data Preparation β ETL, clean data, set up sandbox (most time-consuming)
- Model Planning β select techniques & features
- Model Building β train/test models
- Communicate Results β visualize and interpret
- Operationalize β deploy and monitor
Tip: Emphasize Phase 1 & 4 if asked.
Q2(b) Seven Key Roles (bonus)
- Business User β provides problem context
- Project Sponsor β budget & alignment
- Project Manager β timeline & coordination
- BI Analyst β reporting/visualization
- DBA β data governance
- Data Engineer β pipelines & prep
- Data Scientist β modeling & insights
Visual: Lifecycle & Roles

Q3: Regression Analysis (Chapter 5)
Regression Types by Shape
- Simple Linear Regression β straight line, continuous outcome
- Logistic Regression β S-curve, binary probability
- Polynomial/Non-linear β curve for non-linear relationships
Q3(a) Interpret a Regression Equation
Given $Sales = 100 + 5(Advertising)$:
- Intercept (100): predicted sales when advertising is 0
- Slope (5): each 1 unit increase in advertising raises sales by 5
Q3(b) Meaning of $R^2$
If $R^2 = 0.85$:
- the model explains 85% of variance in the dependent variable
- higher $R^2$ = better fit (but not necessarily causation)
Q4: Data Mining Algorithms (Chapter 7)
Common Algorithm Comparison
| Algorithm |
Best Use |
Strengths |
Weaknesses |
| Decision Tree |
Interpretability |
Easy to explain |
Overfitting risk |
| Random Forest |
Accuracy |
Robust, lower variance |
Less interpretable |
| KNN |
Small data |
Simple |
Slow, sensitive to noise |
| Naive Bayes |
Text/classification |
Fast, low data requirement |
Independence assumption |
| SVM |
High-dimensional |
Strong margins |
Computationally heavy |
Key rule: Match algorithm to data size, dimensionality, and explainability needs.
Final Tips
- Use tables and keywords in answers
- Keep definitions short, focus on comparison
- Use diagrams when possible
- Calculator is allowed for simple ratios/percentages