AKTU Data Analytics (BIT601) Question Paper 2025-26

Note: Attempt all Sections. In case of any missing data; choose suitably.

Section A

Q1. Attempt all questions in brief · 2 × 7 = 14 marks

1. Explain structured vs semi-structured data.
2. List the phases of the Data Analytics Lifecycle.
3. Define multivariate analysis.
4. Define Bayesian inference.
5. What is stream data? Give one example.
6. What is the Apriori algorithm?
7. What are NoSQL databases?

Section B

Q2. Attempt any three of the following · 7 × 3 = 21 marks

1. Explain the Data Analytics Lifecycle in detail with a neat diagram.
2. Explain the linear regression model along with its assumptions. Using the least squares method, fit a regression line for the following data and obtain the equation of the line ( 𝑦 ൌ 𝑚𝑥 ൅ 𝑐ሻ. 𝐷𝑎𝑡𝑎: ሺ1,2ሻ, ሺ2,4ሻ, ሺ3,5ሻ, ሺ4,4ሻ, ሺ5,5ሻ
3. Discuss the concept of filtering data stream processing. Explain Bloom filtering in detail.
4. Provide an in-depth comparison between the CLIQUE and ProCLUS clustering algorithms. How do these methods handle challenges such as noise, outliers, and varying cluster shapes?
5. Discuss the components of Hadoop ecosystem: Apache Hive, Apache Pig, Apache HBase.

Section C

Q3

Attempt any one part of the following · 7 × 1 = 7 marks

1. What happens in the ‘model planning’ and ‘model building’ stages? How are these two different?
2. Discuss the evolution of Big Data and need for data analytics in modern organizations.

Q4

Attempt any one part of the following · 7 × 1 = 7 marks

1. Explain neural networks including learning, generalization, and competitive learning.
2. Discuss Fuzzy membership function. Apply Union, Intersection,
   Complement, Bold Union and Bold Intersection operations on fuzzy sets
   A and B.
   Given:
   A = {(10,0.2), (20,0.4), (25,0.7), (30,0.9), (40,1.0), (50,0.4)}
B = {(10,0.4), (20,0.1), (25,0.9), (30,0.2), (40,0.6), (50,0.6)}

Q5

Attempt any one part of the following · 7 × 1 = 7 marks

1. Discuss Real-Time Analytics Platforms (RTAP) and explain applications like sentiment analysis and stock prediction.
2. Apply the Flajolet–Martin algorithm on the following data stream to estimate the number of unique elements: 𝑆 ൌ ሼ1,3,2,1,2,3,4,3,1,2,3,1ሽ Given the hash function: ℎሺ𝑥ሻ ൌ ሺ3𝑥 ൅ 1ሻ 𝑚𝑜𝑑 8 Compute the hash values for each element i. Represent the hash values in 3-bit binary form ii. Determine the maximum number of trailing zeros (R) iii. Estimate the number of distinct elements using 2

Q6

Attempt any one part of the following · 7 × 1 = 7 marks

1. Discuss market basket analysis and association rule mining.
2. Cluster the following eight points (with (x, y) representing locations) into three clusters:
   A1(2, 10), A2(2, 5), A3(8, 4), A4(5, 8), A5(7, 5), A6(6, 4), A7(1, 2),
A8(4, 9)
   • Initial cluster centers are:
   A1(2, 10), A4(5,8) and A7(1, 2).
   • The distance function between two points a = (x1, y1) and b = (x2, y2) is defined as 𝑑ሺ𝑎, 𝑏ሻ ൌ |𝑥2 – 𝑥1| ൅ |𝑦2 – 𝑦1|
   Use K-Means Algorithm to find the three cluster centers after implanting all eight points.

Q7

Attempt any one part of the following · 7 × 1 = 7 marks

1. Explain MapReduce model with architecture and working in Apache Hadoop.
2. Explain Exploratory Data Analysis (EDA) in R with steps and sample commands.

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