(SEM V) THEORY EXAMINATION 2023-24 INTRODUCTION TO DATA ANALYTICS AND VISUALIZATION

Course: B.Tech (Semester V)

Subject Code: KDS501

Subject: Introduction to Data Analytics and Visualization

Maximum Marks: 100

Time: 3 Hours

Pattern:

Section A: 10 × 2 marks = 20

Section B: Attempt any 3 × 10 marks = 30

Section C (Q3–Q7): Each has 2 parts, attempt one from each = 50

SECTION A – Short Answer Questions (2 × 10 = 20 Marks)

a. What are the features of Big Data?
b. What is a Decision Tree?
c. Explain the need for Data Analytics.
d. What do you mean by Learning Rate?
e. What is Real-Time Analysis?
f. Define Data Model.
g. Discuss Sentiment Analysis.
h. List various methods of clustering.
i. Differentiate between Pig and SQL.
j. Write any two visualization tools.

SECTION B – Descriptive / Analytical Questions (Any 3 × 10 = 30 Marks)

a. Discuss Classification of Data – explain categories in detail with examples.
b. Illustrate with examples Regression and Bayesian Modelling.
c. Discuss RTAP (Real-Time Analytical Processing) applications in detail.
d. Illustrate with examples CLIQUE and ProCLUS clustering methods.
e. Discuss various visualization techniques with examples.

SECTION C – Long / Case-Based Questions (Each 10 Marks)

Q3. Data Analytics Fundamentals

a. Explain the phases of Data Analytics Life Cycle in detail.
b. Write short notes on:
i. Modern Data Analytics Tools
ii. Applications of Data Analytics

Q4. Algorithms and Search Methods

a. Demonstrate any algorithm for counting oneness in a window.
b. Explain stochastic search methods in detail with examples.

Q5. Prediction and Clustering

a. Explain Prediction Error in classification and regression with an example.
b. Discuss K-Means Clustering – explain working and write the K-Means algorithm for partitioning.

Q6. Sentiment and Market Analysis

a. Case study: Real-Time Sentiment Analysis.
b. Explain the following:
i. Apriori Algorithm
ii. Market-Based Modelling

Q7. Big Data and Visualization

a. Draw and explain the architecture of HDFS (Hadoop Distributed File System).
b. What are the approaches to integrate humans in data exploration for visual data mining?