Do you know everything about data science? Take this data science analysis quiz and see how well you know about this topic. Basically, data analytics is about focusing on viewing the historical data in context. If you believe you know about data science analytics, you can test your knowledge as well as enhance it with some new points. Proceed with See morethe quiz, and see how much you score. All the best! Do share the quiz with others who are interested in practicing data science analysis.
The R-squared may be biased upwards by the extreme-valued outcomes. Remove them and refit to get a better idea of the model’s quality over typical data.
The R-squared is good. The model should perform well.
The extreme-valued outliers may negatively affect the model’s performance. Remove them to see if the Rsquared improves over typical data.
The observations seem to come from two different populations, but this model fits them both equally well.
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Clustering
Association Rules
Classification
Regression
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K Means Clustering
K Means Clustering
Logistic Regression
Association Rules
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Ensure that a DataNode is running
Ensure that the JobTracker is running
Ensure that the NameNode is running
Ensure that the TaskTracker is running
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MADlib
Mahout
RStudio
HBase
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Communication skill
Scientific background
Domain expertise
Well Organized
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Document B
Document A
Document C
Document D
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Classification Y = 0,Probability = 4/54
Classification Y = 1,Probability = 4/54
Classification Y = 0,Probability = 1/54
Classification Y = 1,Probability = 1/54
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0.83
0
0.498
0.6
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Operates on queries and potentially increases the number of rows
Operates on queries and potentially decreases the number of rows
Operates on tables and potentially decreases the number of columns
Operates on both tables and queries and potentially increases both the number of rows and columns
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When you cannot make an assumption about the distribution of the populations
When the data can easily be sorted
When the populations represent the sums of other values
When the data cannot easily be sorted
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It aggregates the results of the Map function and generates processed output.
It distributes the input to multiple nodes for processing.
It writes the output of the Map function to storage.
It breaks the input into smaller components and distributes it to other nodes in the cluster.
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It is too processed.
It is not structured.
It is not normalized.
It is too centralized.
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Rules B and D
Rules A and F
Rules C and E
Rules D and E
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