AKTU MCA Data Analytics (KCA034) Question Paper 2021-22

Note: Attempt all Sections. If you require any missing data, then choose suitably.

Section A

Q1. Attempt all questions in brief · 2 × 10 = 20 marks

1. Explain various sources of data analytics.
2. What is Principal Component Analysis (PCM).
3. Describe types of data.
4. Explain the need of data analytics.
5. Describe data streaming.
6. Explain stochastic search method.
7. What is Competitive learning?
8. Explain ProCLUS.
9. Briefly explain filtering stream.
10. What are various analytical tools?

Section B

Q2. Attempt any three of the following · 10 × 3 = 30 marks

1. Describe Hadoop and its architecture.
2. Explain all phases of data analytics lifecycle.
3. Describe the concept of extracting fuzzy models from data.
4. Explain K-means clustering with example.
5. Discuss stock market prediction case study.

Section C

Q3

Attempt any one part of the following · 10 × 1 = 10 marks

1. Apply Apriori algorithm for the following given transaction dataset, to generate rules. Consider the values as Support=50%, and
   Confidence=75%.
Transaction ID Items Purchased
1 Bread, Cheese, Egg, Juice
2 Bread, Cheese, Juice
3 Bread, Milk, Yogurt
4 Bread, Juice, Milk
5 Cheese, Juice, Milk
2. What are NoSQL databases and its types?

Q4

Attempt any one part of the following · 10 × 1 = 10 marks

1. Describe market based modeling with suitable example.
2. Apply Linear Regression algorithm to predict the salary with 10 years of experience using the method of least square. To solve the problem, use the following data: Experience (x) in years Salary (y) in dollars

Q5

Attempt any one part of the following · 10 × 1 = 10 marks

1. What is Support Vector machine (SVM)? Explain it with suitable example.
2. Apply Bayes Theorem. Suppose you have to draw a single card from a standard deck of 52 cards. Now the probability that the card is a Queen is P(Queen) = 4/52. If you are given evidence that the card that you have picked is a face card, then calculate the posterior probability using Bayes theorem.

Q6

Attempt any one part of the following · 10 × 1 = 10 marks

1. What is Real time analytics platform? Explain it with “Real time sentiment analysis” case study.
2. Explain hierarchical clustering with suitable example.

Q7

Attempt any one part of the following · 10 × 1 = 10 marks

1. Explain MapReduce, Hive, Pig, and HBase in Data Analytics.
2. Apply PCY algorithm on following transactions on following transaction to find the candidate sets. Assume threshold value = 3, and
   hash function h(x) = (i*j) mod 10.
T1 = {1, 2, 3}
T2= {2, 3, 4}
T3 = {3, 4, 5}
T4 = {4, 5, 6}
T5 = {1, 3, 5}
T6 = {2, 4, 6}
T7 = {1, 3, 4}
T8 = {2, 4, 5}
T9 = {3, 4, 6}
T10 = {1, 2, 4}
T11 = {2, 3, 5}
T12= {3, 4, 6}

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