Big Data Analytics — Winter 2023

Define following. 1. Data warehouse 2. Cluster centroid 3. Stemming 4. Term Frequency

Discuss the importance of analytic sandbox.

Explain Data preparation, Model planning and Model building phases of data analytic lifecycle.

Explain types and drivers of big data.

Discuss collaborative filtering vs association rules.

Discuss different metrics to measure the distance between two records.

Explain k-means clustering algorithm.

Write short note on item based collaborative filtering.

Discuss the concept of support and confidence for generating association rules in apriori algorithm.

Explain hierarchical clustering in brief.

Explain simple exponential smoothing in detail.

Write short note on: Amodel with Linear Trend.

Define autocorrelation. Discuss ARIMA models in brief.

Explain edge list and adjacency matrix with proper example.

Explain following: 1. Node level centrality metrics 2. Egocentric Network

Explain advantages and disadvantages of social media analytics.

Explain tokenization in brief.

Explain bag-of-words approach with suitable example.