big data analysis

Which of the following is a primary source of big data?

• Data extracted from textbooks
• Data collected from scientific journals
• Data obtained from social media platforms
• Data generated directly from users, devices, and sensors

What is the purpose of data preprocessing in big data analysis?

• To increase the complexity of data for analysis
• To randomly sample data for analysis
• To clean, transform, and organize raw data into a usable format for analysis.
• To skip data cleaning and directly analyze raw data

Which of the following is NOT a data visualization technique used in big data analysis?

• Bar graph
• Scatter plot
• Histogram
• Pie chart

What is the goal of exploratory data analysis (EDA) in big data analysis?

• To summarize main characteristics, gain insights, detect patterns, test assumptions, and develop hypotheses.
• To skip data preprocessing steps
• To create complex machine learning models
• To predict future outcomes based on historical data

Which of the following is a tool used for big data analysis?

• TensorFlow
• Apache Hadoop
• MongoDB
• MySQL

What is the role of data scientists in big data analysis?

• Data scientists are responsible for marketing strategies in big data analysis
• Data scientists only collect data but do not analyze or interpret it
• Data scientists collect, process, analyze, and interpret large volumes of data to extract valuable insights and make data-driven decisions.
• Data scientists focus on qualitative data analysis only

Which of the following is an example of predictive analytics in big data analysis?

• Using social media sentiment analysis to predict weather patterns
• Applying statistical models to predict customer churn based on historical data
• Utilizing data visualization tools to analyze real-time market trends
• Using machine learning algorithms to forecast sales trends based on past sales data

What is the benefit of real-time analytics in big data analysis?

• Delayed insights and slower decision-making
• Limited data processing capabilities
• Increased data security risks
• Immediate insights and faster decision-making

Which of the following is a challenge in implementing big data analysis?

• Lack of available data
• Limited storage capacity
• Inadequate processing power
• Complexity of managing and processing large volumes of data

How does big data analysis contribute to innovation?

• Big data analysis contributes to innovation by limiting access to information.
• Big data analysis contributes to innovation by revealing insights and opportunities hidden within large datasets.
• Big data analysis contributes to innovation by ignoring data trends and patterns.
• Big data analysis contributes to innovation by creating more confusion and complexity.