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Questions and Answers
What is a primary characteristic of Big Data that differentiates it from traditional data?
Which of the following is an example of unstructured data?
What does the term 'Data Deluge' refer to?
How does having a larger volume of data enhance analytical accuracy?
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Which of the following does not represent a type of structured data?
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What is a primary concern regarding data storage in the context of big data?
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What reflects the role of 'data analytical talent' in the new big data ecosystem?
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Which of the following is NOT a challenge of big data?
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What role do 'data savvy professionals' play in the big data ecosystem?
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In the context of big data, what is a significant issue regarding security?
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What is a common strategy for managing the infrastructure needed for big data?
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What aspect of data consistency is a question that arises in big data environments?
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Which of the following best describes the concept of the 'Sensornet' in the big data ecosystem?
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Which statement accurately describes the primary difference between traditional BI and Big Data?
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What kind of approach is commonly associated with Business Intelligence?
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What kind of data is typically analyzed using Data Science techniques?
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Which technique is NOT generally associated with Business Intelligence?
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In the context of BI and Data Science, which question aligns with typical BI inquiries?
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When integrating Big Data into decision making, what infrastructure is primarily used?
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What characterizes the analytical approach of Data Science compared to Business Intelligence?
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What is a limitation of traditional Business Intelligence compared to Data Science?
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What is a primary challenge associated with big data?
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Which skill is emphasized as essential for a data scientist?
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What is required to develop, manage, and run applications that generate insights from big data?
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Which approach enables organizations to gain deeper insights into their businesses?
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What aspect of data needs to be addressed when working with big data?
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What is one of the components of the big data technologies mentioned?
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What behavioral characteristic is associated with a successful data scientist?
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What does big data typically exceed regarding traditional database software?
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Which analytic technique is commonly used in the Consumer Packaged Goods sector?
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What is an example of a tool that provides in-database analytics for predictive modeling?
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In model building, what is the primary focus when creating a model from data?
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Which of the following sectors uses logistic regression as a primary analytic technique?
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Which data partitioning method allocates 20%-30% of data for testing?
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Which analytic method is NOT associated with Wireless Telecom?
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What is the role of hyperparameter tuning in the model training process?
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Which of the following tools allows for advanced analytics without programming?
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Study Notes
Data Structure
- Unstructured Data: Includes images, videos, PDFs, memos, white papers, and email bodies.
- Semi-structured Data: Examples are HTML, XML, JSON, and email metadata.
- Structured Data: Common formats are Excel files, SQL databases, and point-of-sale data.
Data Deluge
- Excess data generation exceeds the capacity for management.
- Reasons include widespread online activity and rapid data production outpacing infrastructure.
Introduction to Big Data
- Big Data requires advanced technical architectures and analytics for insights that enhance business value.
- Characterized by three key dimensions: large volume, wide variety, and high velocity.
Importance of Big Data
- Increased data leads to improved analytical accuracy and confidence in decision-making.
- Enhancements can include operational efficiencies, cost reduction, new product development, and service optimization.
Business Intelligence vs. Data Science
- Traditional BI: Data is centralized, analyzed offline, focused on structured data.
- Data Science: Utilizes real-time streaming and large diverse datasets; employs predictive analytics and mining techniques.
Drivers of Big Data Ecosystem
- Growth of data devices, data collectors, aggregators, and users.
- Key roles include data analytical talent and technology enablers providing support for analytical projects.
Challenges of Big Data
- Management of scale, security, schema flexibility, and continuous availability.
- Data volume is rapidly increasing, requiring critical assessment of its utility for analysis.
- Need for skilled professionals in data science is essential for effective management of big data.
Technologies for Big Data
- Availability of cheap storage, faster processors, and open-source platforms like Hadoop.
- Enables parallel processing and flexible resource allocation through cloud computing.
Activities and Profile of Data Scientists
- Key skills include quantitative analysis, technical aptitude, curiosity, skepticism, and communication.
- Important to reframe business challenges into analytical challenges and develop actionable insights from statistical models.
Big Data Analytics Lifecycle
- Involves determining model requirements based on market sector.
- Various analytic techniques are used based on industry needs, e.g., regression models in consumer goods or decision trees in retail business.
Common Tools for Model Planning
- R: For building models and executing statistical analyses.
- SAS: A programming environment suited for data manipulation and analysis.
- SQL: Performs in-database analytics and predictive modeling.
- RapidMiner: Offers easy access to advanced analytics without coding.
- Tableau Public: Connects to various data sources for real-time analysis.
Importance of Model Building
- Critical for extracting insights and guiding business strategies.
- Emphasizes the use of training and testing data for model accuracy, including hyperparameter tuning.
- Focuses on identifying patterns in data rather than simple memorization.
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Description
This quiz covers the fundamental concepts of Big Data, including the types of data structures such as unstructured, semi-structured, and structured data. Explore the significance of big data in modern business intelligence and its impact on decision-making and operational efficiencies.
