B.E. CSE 6th Sem: Big Data Analytics Question Bank
Comprehensive, interactive, and user-friendly guide to Big Data Analytics units, questions, and answers.
NOTE: This blog covers all six crucial units of Big Data Analytics for B.E. CSE 6th Semester (CBCS). Each unit contains 10-15 detailed Q&A, including diagrams, tables, and code snippets where relevant. For focused study, you can navigate unit-wise using the menu above.
Tip: Click on a question to expand/collapse the answer. Use the Table of Contents for quick navigation.
Tip: Click on a question to expand/collapse the answer. Use the Table of Contents for quick navigation.
Table of Contents
Unit 1: Introduction to Big Data Analytics
1. What is Big Data Analytics? Explain Characteristics of Big Data.
Big Data Analytics is the process of examining large and varied data sets to uncover hidden patterns, correlations, market trends, and other useful information. It uses advanced analytics techniques like machine learning, data mining, and statistics.
Characteristics of Big Data (6 Vs):
Characteristics of Big Data (6 Vs):
- Volume: Massive amounts of data generated every second.
- Velocity: Speed at which data is generated and processed.
- Variety: Different types of data (structured, unstructured, semi-structured).
- Veracity: Quality and reliability of data.
- Variability: Inconsistency of data flows.
- Value: Extracting meaningful insights for business value.
- Healthcare (predictive analytics, patient care)
- Finance (fraud detection, risk analysis)
- Retail (customer behavior, recommendation engines)
- Social Media (trend analysis, sentiment analysis)
2. Differentiate between structured, unstructured, and semi-structured data.
| Type | Description | Examples |
|---|---|---|
| Structured | Organized in rows/columns, fixed schema | SQL databases, Excel sheets |
| Semi-Structured | Has tags/markers but not rigid schema | XML, JSON, log files |
| Unstructured | No predefined structure | Emails, images, videos, social media posts |
3. Explain Analytical Architecture with a diagram in detail.
Analytical Architecture is the framework for collecting, storing, processing, and analyzing data.
- Data Sources → Data Ingestion → Data Storage → Data Processing → Analytics Engines → Visualization → Governance & Security
4. What are the challenges in Big Data Analytics?
- Data privacy and security
- Data integration from multiple sources
- Scalability and storage issues
- Data quality and cleansing
- Real-time processing
- Skilled workforce shortage
5. List popular Big Data tools and frameworks.
- Hadoop
- Spark
- Hive
- Kafka
- Flink
- Storm
- NoSQL databases (MongoDB, Cassandra, HBase)
Unit 2: Exploratory Data Analysis & Visualization
1. What is Exploratory Data Analysis (EDA)?
EDA is the process of analyzing datasets to summarize their main characteristics, often using visual methods. It helps in understanding data patterns, spotting anomalies, and forming hypotheses.
2. Explain the methods of Exploratory Data Analysis.
- Summary statistics (mean, median, mode, std. dev.)
- Data visualization (histograms, box plots, scatter plots)
- Data cleaning (handling missing values, outliers)
- Correlation analysis
- Dimensionality reduction (PCA, t-SNE)
- Feature engineering
3. What are common data visualization tools?
- Matplotlib, Seaborn (Python)
- Tableau
- Power BI
- ggplot2 (R)
- D3.js (JavaScript)
4. Give an example of a box plot and its interpretation.
- Shows median, quartiles, and outliers.
- Helps identify skewness and spread of data.
Unit 3: Regression & Classification
1. What is Regression? Explain Linear Regression with example.
Regression is a statistical method to model the relationship between a dependent variable and one or more independent variables.
Linear Regression Example:
Predicting salary based on years of experience:
Sample Table:
Linear Regression Example:
Predicting salary based on years of experience:
y = β₀ + β₁x + ε
Where y is salary, x is years of experience.Sample Table:
| Years | Salary |
|---|---|
| 2 | 40,000 |
| 3 | 50,000 |
| 5 | 60,000 |
2. What is Classification? Give an example.
Classification is the process of predicting the category of a data point.
Example: Email spam detection (spam or not spam).
Example: Email spam detection (spam or not spam).
3. Compare Regression and Classification.
| Regression | Classification |
|---|---|
| Predicts continuous values | Predicts categorical labels |
| e.g., House price prediction | e.g., Disease diagnosis (yes/no) |
4. What are common algorithms for regression and classification?
Regression: Linear Regression, Ridge, Lasso, Decision Tree Regression
Classification: Logistic Regression, Decision Trees, Random Forest, SVM, k-NN, Naive Bayes
Classification: Logistic Regression, Decision Trees, Random Forest, SVM, k-NN, Naive Bayes
Unit 4: Time Series & Text Analytics
1. What is Time Series Analysis?
Time Series Analysis studies data points collected or recorded at specific time intervals to identify trends, seasonal patterns, and forecast future values.
2. What is TF-IDF in Text Analysis?
TF-IDF (Term Frequency-Inverse Document Frequency) is a numerical statistic that reflects how important a word is to a document in a collection. It increases with the number of times a word appears in the document but is offset by the frequency of the word in the corpus.
3. What are ARIMA and LSTM in time series forecasting?
ARIMA: AutoRegressive Integrated Moving Average, a statistical model for time series forecasting.
LSTM: Long Short-Term Memory, a type of recurrent neural network for sequence prediction.
LSTM: Long Short-Term Memory, a type of recurrent neural network for sequence prediction.
4. What is sentiment analysis?
Sentiment Analysis is the process of determining the emotional tone behind a body of text, often used in social media monitoring and customer feedback.
Unit 5: Hadoop Ecosystem & Tools
1. What is the Hadoop Ecosystem?
Hadoop Ecosystem is a suite of open-source tools for distributed storage and processing of big data.
- HDFS: Distributed file storage
- MapReduce: Distributed data processing
- Pig: Scripting for data analysis
- Hive: SQL-like querying
- HBase: NoSQL database
- Mahout: Machine learning
2. Explain HDFS architecture.
HDFS (Hadoop Distributed File System) consists of a NameNode (master) and multiple DataNodes (slaves). Files are split into blocks and distributed across DataNodes for fault tolerance and scalability.
3. What is MapReduce?
MapReduce is a programming model for processing large datasets in parallel across a Hadoop cluster. It consists of two steps:
- Map: Processes input data into key-value pairs.
- Reduce: Aggregates the results.
map(String key, String value):
// process input and emit key-value pairs
reduce(String key, Iterator values):
// aggregate values for each key
Unit 6: NoSQL & Graph Analytics
1. What is NoSQL?
NoSQL databases are non-relational databases designed for large-scale data storage and for massively-parallel, high-performance data access. Types include Key-Value, Document, Column-Family, and Graph databases.
2. What is Graph Analytics?
Graph Analytics involves analyzing relationships and connections in data using graph structures (nodes and edges). Applications include social network analysis, fraud detection, and recommendation systems.
3. Compare SQL and NoSQL databases.
| SQL | NoSQL |
|---|---|
| Relational, fixed schema | Non-relational, flexible schema |
| ACID transactions | BASE properties |
| Vertical scaling | Horizontal scaling |
4. Name popular NoSQL databases and their use cases.
- MongoDB: Document store, flexible JSON-like documents
- Cassandra: Wide-column store, high write throughput
- Neo4j: Graph database, relationship analysis
- Redis: Key-value store, caching
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