Podcast
Questions and Answers
Which of the following is NOT typically considered a key characteristic of big data?
Which of the following is NOT typically considered a key characteristic of big data?
- Veracity: The uncertainty of data.
- Volume: The amount of data generated.
- Variety: Different forms of data.
- Validity: Accessibility and ease of interpreting data. (correct)
In the context of data science, what is the significance of a typical analytical architecture?
In the context of data science, what is the significance of a typical analytical architecture?
- It standardizes the format in which data is visualized.
- It ensures compliance with data privacy regulations.
- It provides a blueprint for how data is acquired, processed, stored, and analyzed. (correct)
- It dictates the programming languages to be used in data analysis.
Which skill is LEAST relevant for a data scientist in the new data ecosystem?
Which skill is LEAST relevant for a data scientist in the new data ecosystem?
- Proficiency in statistical analysis.
- Ability to communicate complex findings.
- Expertise in data visualization.
- Advanced knowledge of legacy systems. (correct)
What is the primary purpose of using R to 'look at data' in the early stages of a data analytics project?
What is the primary purpose of using R to 'look at data' in the early stages of a data analytics project?
When performing basic R operations on vectors, which operation would be most appropriate for normalizing data to a 0 to 1 scale?
When performing basic R operations on vectors, which operation would be most appropriate for normalizing data to a 0 to 1 scale?
In data analytics, what distinguishes data exploration from data presentation?
In data analytics, what distinguishes data exploration from data presentation?
In K-means clustering, what does the 'K' refer to?
In K-means clustering, what does the 'K' refer to?
Why is evaluating a model an important step in advanced analytics?
Why is evaluating a model an important step in advanced analytics?
What is Lift in the context of association rules?
What is Lift in the context of association rules?
In the context of 'putting it all together' in an analytics project, what is the significance of operationalizing an analytics project?
In the context of 'putting it all together' in an analytics project, what is the significance of operationalizing an analytics project?
Flashcards
Big Data Definition
Big Data Definition
Big data is defined by its characteristics (volume, velocity, variety, veracity) and the considerations needed for processing it.
Data Analytics Lifecycle
Data Analytics Lifecycle
A structured method to plan, execute, and manage data-driven projects.
What is R?
What is R?
R is a programming language and environment widely used for statistical computing, data analysis, and visualization.
K-Means Clustering
K-Means Clustering
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Association Rules
Association Rules
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Lift
Lift
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Linear Regression
Linear Regression
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Logistic Regression
Logistic Regression
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Hadoop
Hadoop
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Hive
Hive
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Study Notes
- Big data characteristics and considerations aid in defining the four main types of data structures
- Business drivers for analytics and a typical analytical architecture create new opportunities for analytics
- Skills are needed in the new data ecosystem by data scientists and industry analytics
- Data analytics lifecycle defines key roles for a successful analytics project
- R is used to look at data and remember five things about it
- The R Graphical User Interface helps to get data into R and get data out of R through external sources
- Basic R operations on vectors, descriptive statistics, generic functions help with data analysis and exploration
- Establish multiple pairwise relationships between variables by plotting high-volume data and analyzing a single variable over time to explore and present the data
- The data analytics lifecycle, clustering, and K-means clustering are applied in an online retailer
- Diagnostics model evaluation contains association rules, lift and leverage, and computing confidence and lift
- Diagnostics include regression, linear regression, and logistic regression for visualizing the model.
- Operationalizing an analytics project aids in Deliverables
- Data visualization techniques can be put together for The Endgame
- Features of Hadoop, Hadoop functions, & HDFS are introductions to data
- Hadoop can be defined as an eco-system
- Hive has a specific installation strategy
- Zookeeper also has a specific installation strategy
- The functions, strengths, and weaknesses of MapReduce are important concepts
- Pig architecture, properties, application flow, and data types need to be understood
- Pig has a specific installation process
- Running scripts are important to its functionality
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