What is Prescriptive Analytics, and how does it differ from Predictive Analytics?

• Prescriptive Analytics focuses on data visualization; Predictive Analytics emphasizes data collection.
• Prescriptive Analytics predicts future trends; Predictive Analytics analyzes historical data.
• Prescriptive Analytics is used for data cleaning; Predictive Analytics is for data storage.
• Prescriptive Analytics recommends actions; Predictive Analytics forecasts outcomes.

Discuss the importance of Optimization and Simulation in decision-making.

• Simulation is primarily used for entertainment purposes.
• Optimization and Simulation enable the evaluation of alternatives and the prediction of outcomes

• Optimization is only useful in financial contexts.
• Decision-making does not require any analytical tools.

Explain how Linear Programming is used in business decisions with an example.

• Linear Programming eliminates the need for any constraints in decision-making.
• Linear Programming is primarily focused on employee management and HR decisions.
• Linear Programming optimize resource allocation, such as maximizing profits or minimizing costs, while adhering to constraints.

• Linear Programming is only used for financial forecasting.

Provide an overview of Monte Carlo Simulation and its applications.

• Monte Carlo Simulation is only used in gaming for random number generation.
• Monte Carlo Simulation is a method for predicting weather patterns exclusively.
• Monte Carlo Simulation in project management for cost estimation, iand in scientific research for modeling complex systems.

• Monte Carlo Simulation is primarily focused on historical data analysis in marketing.

Discuss the role of Hadoop and Spark in handling Big Data.

• Hadoop is primarily used for real-time processing, while Spark is for storage.
• Hadoop and Spark are both used exclusively for real-time data processing.
• Hadoop is a programming language, and Spark is a database management system.
• Hadoop is for storage while Spark provides fast, in-memory processing for both batch and real-time data.

Compare and contrast structured, semi-structured, and unstructured data.

• Structured data is organized and easily searchable.

• Unstructured data is always organized in tables.
• Semi-structured data is completely random and chaotic.
• Structured data is always unchangeable and fixed.

Explain NoSQL databases and their advantages over traditional relational databases.

• NoSQL databases do not support unstructured data.
• Relational databases are more flexible than NoSQL databases.
• NoSQL databases offer the ability to handle unstructured data

• NoSQL databases are only suitable for small applications.

Discuss how Big Data Analytics is transforming industries like Retail, Healthcare, and Finance.

• Big Data Analytics is only useful for social media marketing.
• Big Data Analytics has no impact on customer satisfaction.
• Big Data Analytics enable data-driven decisions, improving customer experiences, and enhancing operational efficiencies.

• Big Data Analytics is primarily used for entertainment purposes.

Conduct a case study on a real-world Big Data project (e.g., Amazon, Netflix, or Google). Explain how they use Big Data to gain a competitive advantage.

• Amazon utilizes Big Data to personalize customer experiences

• Amazon avoids using Big Data in its supply chain management.
• Netflix uses Big Data solely for content streaming quality.
• Google relies on Big Data only for advertising revenue.

What are the ethical concerns in Big Data and Analytics?

• Increased job opportunities
• Enhanced data visualization techniques
• Improved customer satisfaction
• privacy issues, lack of transparency, and data security risks.