Podcast
Questions and Answers
What component of an image stores information about color and intensity?
What component of an image stores information about color and intensity?
How would you classify 'The reviews for a property on Airbnb' in terms of data types?
How would you classify 'The reviews for a property on Airbnb' in terms of data types?
Which data type includes information about roads, buildings, and vegetation?
Which data type includes information about roads, buildings, and vegetation?
What is a common application of network data?
What is a common application of network data?
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Which of the following describes quantitative data?
Which of the following describes quantitative data?
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In data science, which data type would be classified as image data?
In data science, which data type would be classified as image data?
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What are pixels primarily used for in digital images?
What are pixels primarily used for in digital images?
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Which of the following is an example of qualitative data?
Which of the following is an example of qualitative data?
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What is a primary benefit of using open data sources?
What is a primary benefit of using open data sources?
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Which type of data is collected when individuals interact with a website?
Which type of data is collected when individuals interact with a website?
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What methods might be used to collect survey data?
What methods might be used to collect survey data?
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Which of the following is NOT a source of company data?
Which of the following is NOT a source of company data?
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What type of information is typically captured in web data tracking?
What type of information is typically captured in web data tracking?
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Which aspect is essential for companies when collecting data from their services?
Which aspect is essential for companies when collecting data from their services?
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What determines the effectiveness of a data pipeline?
What determines the effectiveness of a data pipeline?
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In terms of data generation, which of the following activities contributes to vast amounts of data creation?
In terms of data generation, which of the following activities contributes to vast amounts of data creation?
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What is the primary use of cloud storage providers such as Microsoft Azure, AWS, and Google Cloud?
What is the primary use of cloud storage providers such as Microsoft Azure, AWS, and Google Cloud?
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Which type of data is best indicated to be stored in a Document Database?
Which type of data is best indicated to be stored in a Document Database?
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What kind of database primarily uses SQL for querying data?
What kind of database primarily uses SQL for querying data?
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Which option correctly describes what NoSQL stands for?
Which option correctly describes what NoSQL stands for?
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What analogy is used to explain the decision-making process for data storage locations?
What analogy is used to explain the decision-making process for data storage locations?
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If data requires a tabular format, which type of database is appropriate?
If data requires a tabular format, which type of database is appropriate?
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Which scenario necessitates the use of both Document Databases and Relational Databases?
Which scenario necessitates the use of both Document Databases and Relational Databases?
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When querying data, which type of analysis is NOT typically mentioned?
When querying data, which type of analysis is NOT typically mentioned?
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What is the primary distinction between quantitative and qualitative data?
What is the primary distinction between quantitative and qualitative data?
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Which of the following statements best describes qualitative data?
Which of the following statements best describes qualitative data?
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Which type of data is typically represented in numbers, such as height, quantity, or price?
Which type of data is typically represented in numbers, such as height, quantity, or price?
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Why is it important to understand the types of data you are collecting?
Why is it important to understand the types of data you are collecting?
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Which of the following is NOT typically an example of qualitative data?
Which of the following is NOT typically an example of qualitative data?
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Which of the following data types is mentioned as being a special mix of quantitative and qualitative data?
Which of the following data types is mentioned as being a special mix of quantitative and qualitative data?
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In the context of data science, what is image data considered to be?
In the context of data science, what is image data considered to be?
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What is a potential consequence of not recognizing the type of data being collected?
What is a potential consequence of not recognizing the type of data being collected?
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What is the primary purpose of the transform phase in the ETL process?
What is the primary purpose of the transform phase in the ETL process?
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During which stage of the data pipeline are irrelevant data removed?
During which stage of the data pipeline are irrelevant data removed?
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Which statement best describes the role of automation in data pipelines?
Which statement best describes the role of automation in data pipelines?
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Which of the following tools is popular for automating data pipelines?
Which of the following tools is popular for automating data pipelines?
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What happens in the load phase of the ETL process?
What happens in the load phase of the ETL process?
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Which of the following statements regarding data pipelines is false?
Which of the following statements regarding data pipelines is false?
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How does the practice of data preparation and exploration relate to the data pipeline?
How does the practice of data preparation and exploration relate to the data pipeline?
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What type of data tasks can be classified as part of the transform phase?
What type of data tasks can be classified as part of the transform phase?
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What type of data does the Net Promoter Score (NPS) represent?
What type of data does the Net Promoter Score (NPS) represent?
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What type of data will Jane be extracting from the activity tracker's API to create a heatmap of her running routes?
What type of data will Jane be extracting from the activity tracker's API to create a heatmap of her running routes?
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Which factor is NOT mentioned as important when storing data?
Which factor is NOT mentioned as important when storing data?
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What is the primary reason for using parallel storage solutions in data science?
What is the primary reason for using parallel storage solutions in data science?
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Which of the following best describes the role of a server in data storage?
Which of the following best describes the role of a server in data storage?
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Which step is considered part of the data science workflow related to data?
Which step is considered part of the data science workflow related to data?
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What is the main purpose of collecting Net Promoter Score data?
What is the main purpose of collecting Net Promoter Score data?
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In which scenario would it be necessary to use a data cluster for storage?
In which scenario would it be necessary to use a data cluster for storage?
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Study Notes
Data Science Fundamentals
- This presentation covers data collection and management, data storage and retrieval, and data pipelines.
- The data science workflow encompasses data collection and storage, data preparation, exploration and visualization, and experimentation and prediction.
- Data collection is crucial for data science, as it underpins all analysis.
- Various data sources exist, including company data (collected internally to inform decisions) and open data (freely shared and usable by anyone).
- Common company data sources: web events, survey data, customer data, logistics data, and financial transactions.
- Web data includes event names (e.g., URLs, click identifiers), timestamps, and user identifiers.
- Survey data is collected through various methods like face-to-face interviews, online questionnaires, or focus groups.
- Net Promoter Score (NPS) is a common survey metric gauging customer likelihood to recommend.
- Public data APIs (Application Programming Interfaces) allow access to data from third parties via the internet, including Twitter, Wikipedia, Yahoo! Finance and Google Maps.
- Public records are another open data source, often from international organizations (e.g., World Bank, UN, WTO), national statistical offices, and government agencies (e.g., weather, population data).
- Data types include quantitative (countable and measurable, using numbers) and qualitative (descriptive and conceptual, observed but not measured).
- Examples of quantitative data:
- The price of a cup of coffee in Parisian cafés
- The daily average temperature in NYC during 2019
- The individual weight of dogs in a shelter
- Examples of qualitative data:
- The eye colour of study participants
- Images of cats
- Product reviews
- Stock prices
- Other data types: Image, text, geospatial, and network data
- Data storage solutions: Document databases (used for unstructured data) and Relational databases (used for structured data).
Data Storage and Retrieval
- Data storage needs vary depending on the volume of data.
- Companies can store data on-premises (e.g., in clusters) or cloud storage.
- Common cloud providers are Microsoft Azure, Amazon Web Services, and Google Cloud.
- Multiple types of databases are used for storage. They include document databases (for unstructured data) and relational databases (for tabular data).
- Tools for efficient retrieval: query languages (e.g., NoSQL for document databases and SQL for relational databases) are needed to access data effectively.
Data Pipelines
- Data pipelines automate the movement of data through various stages (extract, transform, load - ETL).
- Automation allows for handling large volumes of incoming data, as well as real-time updates, including handling data such as tweets, allowing for continuous collection.
- The concept of data pipelines is needed when working with considerable quantities of data from different sources and handling various data types.
- Data pipelines are frequently used when different data types need to be incorporated into one dataset.
- Data pipelines (especially in the transform phase) convert incoming data's structure to fit existing database schemas.
- Data pipelines also automate many data analysis tasks.
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Description
This quiz explores the essential elements of data science, including data collection, management, storage, and data pipelines. Understand the importance of various data sources, such as internal company data and open data, as well as the data science workflow from collection to prediction. Test your knowledge on how data informs decision-making in organizations.
