(SEM V) THEORY EXAMINATION 2020-21 DATA ANALYTICS

SECTION A (Any 2 Questions Explained Briefly)
 

What are the different types of data?
Data can be classified into structured, semi-structured, and unstructured data. Structured data is organized in tables and databases, semi-structured data includes formats like XML or JSON, and unstructured data includes text, images, audio, and video. Understanding data types is important for selecting the correct analysis technique.
 

Explain decision tree.
A decision tree is a supervised learning technique used for classification and prediction. It represents decisions in the form of a tree structure where each internal node denotes a condition, each branch represents a decision rule, and each leaf node shows the outcome. Decision trees are easy to understand and interpret.
 

SECTION B (Any 2 Questions Explained)
 

Explain K-Means algorithm and its use.
K-Means is an unsupervised clustering algorithm used to group data into K clusters based on similarity. It works by selecting initial cluster centers, assigning data points to the nearest center, and updating the centers iteratively. K-Means is mainly used when the number of clusters is known in advance and is widely applied in market segmentation and pattern recognition.
 

Explain Bayesian data analysis.
Bayesian data analysis is a statistical method that uses probability to represent uncertainty in data. It updates prior knowledge with new evidence using Bayes’ theorem to obtain posterior probabilities. This approach is useful in situations where data is limited or uncertainty needs to be handled effectively.
 

SECTION C (Any 2 Questions Explained)
 

Describe the architecture of HIVE.
Hive is a data warehousing tool built on Hadoop that allows users to query large datasets using SQL-like language called HiveQL. Its architecture includes user interface, compiler, execution engine, and Hadoop distributed file system. Hive simplifies data analysis by hiding the complexity of MapReduce programming.
 

Brief about the main components of MapReduce.
MapReduce consists of two main components: Mapper and Reducer. The Mapper processes input data and generates key-value pairs, while the Reducer aggregates and processes these pairs to produce final results. MapReduce enables parallel processing of large datasets efficiently.
 

Most Questions in This PDF Are Related To
 

Most questions in the Data Analytics (KCS-051) paper are related to data types, data analytics life cycle, clustering and classification algorithms, Hadoop ecosystem (HDFS, Hive, MapReduce), data streams, sampling techniques, multivariate analysis, and machine learning concepts such as supervised and unsupervised learning. The paper mainly focuses on analytical techniques and big data processing frameworks.