Podcast
Questions and Answers
What are the three Vs that define Big Data according to Gartner's IT Glossary?
What are the three Vs that define Big Data according to Gartner's IT Glossary?
Why is the naive interpretation of Big Data considered incomplete?
Why is the naive interpretation of Big Data considered incomplete?
Which factor differentiates analyzing 1 Gigabyte of data per day from analyzing it per second?
Which factor differentiates analyzing 1 Gigabyte of data per day from analyzing it per second?
What aspect of Big Data refers to the variety of formats and sources of data?
What aspect of Big Data refers to the variety of formats and sources of data?
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Which of the following is NOT a characteristic of Big Data?
Which of the following is NOT a characteristic of Big Data?
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Which aspect focuses on techniques like predictive modeling and forecasting?
Which aspect focuses on techniques like predictive modeling and forecasting?
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What characterizes Business Intelligence compared to Data Science?
What characterizes Business Intelligence compared to Data Science?
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Which of the following is NOT an application of Data Science?
Which of the following is NOT an application of Data Science?
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What type of questions does Data Science often explore?
What type of questions does Data Science often explore?
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In what order do Data Science insights typically follow on a timeline?
In what order do Data Science insights typically follow on a timeline?
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Which of the following best describes optimization in the context of Data Science?
Which of the following best describes optimization in the context of Data Science?
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Which computing aspect involves the use of algorithms and data structures?
Which computing aspect involves the use of algorithms and data structures?
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Which option represents a form of large-scale data management?
Which option represents a form of large-scale data management?
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What does the term 'volume' refer to in the context of big data?
What does the term 'volume' refer to in the context of big data?
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Which V of big data pertains to the different types and sources of data?
Which V of big data pertains to the different types and sources of data?
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What represents data that has a structure and is easily analyzable?
What represents data that has a structure and is easily analyzable?
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What characterizes the 'velocity' aspect of big data?
What characterizes the 'velocity' aspect of big data?
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Which of the following best describes 'quasi-structured' data?
Which of the following best describes 'quasi-structured' data?
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What is the primary goal of data science?
What is the primary goal of data science?
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Which of the following describes unstructured data?
Which of the following describes unstructured data?
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What can be inferred about the definition of data science?
What can be inferred about the definition of data science?
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Which skill is essential for Data Scientists but not limited to mathematicians?
Which skill is essential for Data Scientists but not limited to mathematicians?
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What type of Data Scientist is primarily focused on analyzing data?
What type of Data Scientist is primarily focused on analyzing data?
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Which of the following is NOT mentioned as a type of Data Scientist?
Which of the following is NOT mentioned as a type of Data Scientist?
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What is a primary quality that Data Scientists are expected to have regarding hypotheses?
What is a primary quality that Data Scientists are expected to have regarding hypotheses?
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Which skill set is emphasized as a collaborative aspect of Data Scientists' roles?
Which skill set is emphasized as a collaborative aspect of Data Scientists' roles?
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Which term describes a Data Scientist that performs various functions, including data collection and analysis?
Which term describes a Data Scientist that performs various functions, including data collection and analysis?
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What is a key responsibility of a Data Preparer?
What is a key responsibility of a Data Preparer?
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Which skill is NOT typically associated with Data Scientists according to the provided information?
Which skill is NOT typically associated with Data Scientists according to the provided information?
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Study Notes
Big Data Definitions
- The term "Big Data" is often associated with the volume of data, but there are other important factors.
- Big data is defined as high-volume, high-velocity and/or high-variety information assets.
- Gartner defines big data as assets that "demand cost-effective, innovative forms of information processing."
- The three Vs of big data are Volume, Velocity, and Variety.
- The volume refers to the "bigness" of the data, requiring innovative processing approaches.
- Velocity is the speed at which data is created and must be analyzed, often close to real-time.
- Variety refers to the diversity in data types and sources, ranging from structured to unstructured data.
Structured, Semi-Structured and Unstructured Data
- Structured data is readily organized with defined types and structures, like comma-separated values.
- Semi-structured data has a parseable pattern, such as XML files with schemas.
- Quasi-structured data has erratic formats that can be formatted with effort, like clickstream data.
- Unstructured data has no inherent structure and multiple formats, such as websites and videos.
Data Science
- There is no clear definition of "data science".
- The goal of data science is extracting knowledge from data.
- It involves techniques from different disciplines, guided by scientific methodology.
- Data science combines computer science aspects like algorithms, databases, and machine learning.
- Statistical aspects include linear models, statistical tests, and inference.
Data Science Applications
- Data science is used in various fields, including intelligent systems, robotics, marketing, medicine, autonomous driving, and social networks.
Data Science and Business Intelligence
- Business Intelligence focuses on accessing and analyzing information to improve and optimize decisions and performance.
- Data science encompasses a wider range of techniques, including predictive modelling and forecasting.
- While business intelligence primarily uses structured data from data warehouses, data science can handle any kind of data, especially unstructured data.
- Business intelligence emphasizes answering "what happened?" while data science explores "what if?" and "what will be?" questions.
Skills of Data Scientists
- Data scientists require a diverse skill set encompassing quantitative, collaborative, technical, and skeptical approaches.
- Quantitative skills involve mathematics, algorithms, and statistics.
- Collaborative skills include teamwork and communication.
- Technical skills include programming, infrastructure knowledge, and understanding of data science platforms.
- Skeptical skills involve formulating hypotheses and critically evaluating them.
Different Types of Data Scientists (Microsoft Research)
- Polymath: "Do it all". They are involved in all aspects of Data Science.
- Data Evangelist: Analyze data and share insights to influence actions.
- Data Analyzer: Focuses on analyzing data.
- Platform Builder: Collects data and builds data infrastructure.
- Data Preparer: Queries data and prepares it for analysis.
- Moonlighters: Part-time data scientists, often 50% or 20% of their time.
- Insight Actors: Act based on insights derived from data analysis.
- Data Shapers: Analyze and prepare data for specific purposes.
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Description
Explore the fundamentals of Big Data, including its essential characteristics outlined by the three Vs: Volume, Velocity, and Variety. Additionally, gain insight into the distinctions between structured, semi-structured, and unstructured data types. This quiz will enhance your understanding of how Big Data is processed and categorized.
