About this previous year paper
BCIT601 – DATA SCIENCE IN IoT
Time: 3 Hours | Max Marks: 70
SECTION A – Short Answer Questions
(2 × 7 = 14 marks | Attempt ALL)
Write clear definition + 1–2 key points
1. IoT Analytics
IoT analytics is the process of analyzing data generated by IoT devices to extract useful insights for decision-making.
It includes real-time, batch, and predictive analytics.
2. Exploratory Data Analytics (EDA)
EDA is the initial analysis of data to understand patterns, trends, outliers, and relationships using statistical and visual methods.
3. Streaming Analytics vs Prescriptive Analytics
| Streaming Analytics | Prescriptive Analytics |
|---|---|
| Real-time data analysis | Suggests actions/decisions |
| Continuous data flow | Uses optimization & models |
| Example: live sensor alerts | Example: what action to take |
4. Mean vs Deviation Mean: Average value of dataset
Deviation: Spread of data from the mean Deviation shows data variability.
5. Purpose of Data Pre-Engineering in IoT Cleaning noisy sensor data
Handling missing values Data normalization for better analytics accuracy
6. Skewness & Its Significance Skewness measures asymmetry of data distribution.
Positive skew: long right tail Negative skew: long left tail
Helps understand data behavior.
7. Residual Plot
A residual plot shows errors between observed and predicted values.
It helps identify non-linearity and model inadequacy.
SECTION B – Medium Answer Questions
(7 × 3 = 21 marks | Attempt ANY THREE)
Write concept → explanation → example
1. Role of IoT Analytics in Creating Business Value IoT analytics helps organizations:
Improve operational efficiency Enable predictive maintenance
Reduce costs and downtime Enhance customer experience
Example: Smart manufacturing systems.
2. Data Collection Strategies in IoT Sensor-based data collection
Edge data acquisition Gateway-based aggregation
Cloud-based data ingestion Each strategy balances latency, cost, and reliability.
3. Visualization Techniques for Model Evaluation
Line graphs – trends over time Scatter plots – correlation analysis
Box plots – outliers and spread Heat maps – intensity and patterns
Used for decision-making and performance analysis.
4. Linked Analytical Datasets in IoT
Linked datasets integrate data from multiple IoT sources.
Benefits: Better contextual insights
Improved accuracy Unified analytics view
Example: Smart city data integration.
5. Analytics in IoT-Driven Agriculture Benefits include:
Crop yield prediction Soil moisture monitoring
Smart irrigation Reduced resource wastage
Improves productivity and sustainability.
SECTION C – Attempt ANY ONE
(7 marks)
1. Architecture of an IoT Analytics System Main components:
IoT devices & sensors Data ingestion layer
Data storage (cloud/databases) Analytics & ML engine
Visualization & decision layer Explain with neat block diagram.
2. Streaming Analytics vs Time-Series Analytics
| Streaming Analytics | Time-Series Analytics |
|---|---|
| Real-time processing | Historical data analysis |
| Low latency | Trend & forecasting |
| Example: traffic alerts | Example: weather prediction |