Data Analytics Insight

What is Big Data Analytics?

• Big Data Analytics is a type of music genre

• Big Data Analytics involves analyzing large data sets to extract valuable insights.

• Big Data Analytics involves predicting future events based on historical data

• Big Data Analytics refers to analyzing small data sets

Name one key benefit of utilizing Big Data Analytics.

• Improved decision-making

• Limited scalability

• Decreased data security

• Faster internet speed

Explain the difference between structured and unstructured data.

• Structured data is always text-based.

• Structured data is organized in a predefined format, while unstructured data lacks a specific format.

• Structured data is disorganized and messy.

• Unstructured data is always stored in a database.

How does semi-structured data differ from structured data?

• Structured data is more flexible than semi-structured data.

• Semi-structured data does not fit into a rigid tabular structure like structured data.

• Semi-structured data is always perfectly organized.

• Semi-structured data is always stored in a database, unlike structured data.

What are the main challenges faced in analyzing Big Data?

• Data visualization, data security, data privacy, data accuracy

• Data cleaning, data modeling, data visualization, data interpretation

• Data storage, data processing, data analysis, data visualization

• Data collection, storage, processing, analysis, interpretation

Describe the process of data mining in the context of Big Data Analytics.

• Data mining is only applicable to small datasets.

• Data mining is a process of creating new data from scratch.

• Data mining involves physical extraction of minerals from the ground.

• Data mining in Big Data Analytics is the process of extracting valuable patterns and insights from large datasets through steps like data collection, preprocessing, transformation, model building, evaluation, and deployment.

What role does machine learning play in Big Data Analytics?

• Machine learning enables algorithms to learn from data, identify patterns, make predictions, and optimize processes in Big Data Analytics.

• Machine learning is not relevant to Big Data Analytics

• Machine learning cannot handle large datasets in Big Data Analytics

• Machine learning only focuses on data storage in Big Data Analytics

How can businesses leverage Big Data Analytics to improve decision-making?

• By collecting and analyzing large volumes of data to identify trends, patterns, and insights.

• By randomly selecting data points without any analysis

• By ignoring data analysis and relying solely on intuition

• By outsourcing data analysis to unqualified individuals

What are some popular tools used for Big Data Analytics?

• MongoDB

• Apache Hadoop, Apache Spark, Apache Flink, Apache Kafka

• Apache Hive

• TensorFlow

Discuss the importance of data visualization in Big Data Analytics.

• Data visualization is important in Big Data Analytics for presenting complex data visually, identifying patterns, trends, and outliers, aiding in decision-making, communication of insights, and enhancing data understanding.

• Data visualization is irrelevant in Big Data Analytics

• Data visualization does not aid in decision-making

• Data visualization only confuses the data analysis process