Data Visualization and Analysis Techniques

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What is data collection in the context of data science?

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Gathering and acquiring data from various sources.

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What are the steps involved in data analysis?

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1. Defining the problem and objectives 2. Collecting and cleaning the data 3. Exploring and analyzing the data 4. Drawing conclusions and making predictions 5. Communicating the results

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Why is data cleaning important in data science?

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Data cleaning is important in data science because it ensures the accuracy, consistency, and reliability of the data.

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What is the purpose of data visualization in data science?

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To present data in a visual format and help understand patterns, trends, and relationships within the data.

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What is the purpose of box and whisker plots in data science?

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To visualize the distribution of a dataset and identify outliers and spread of the data.

Tags

CCSS.6.SP.B.4

CCSS.HSS.ID.A.1

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Define data analysis.

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The process of inspecting, cleaning, transforming, and modeling data to discover useful information, inform conclusions, and support decision-making.

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What is the role of exploratory data analysis (EDA)?

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To analyze data sets to summarize their main characteristics, often using visual methods.

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CCSS.6.SP.B.5B

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What is a histogram used for in data visualization?

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To represent the distribution of numerical data by showing the number of data points that fall within a specified range of values.

Tags

CCSS.6.SP.B.4

CCSS.HSS.ID.A.1

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Explain the concept of outliers in data analysis.

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Outliers are data points that differ significantly from other observations in a dataset, which can indicate variability in measurement or may suggest experimental errors.

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What is the significance of the mean in data analysis?

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The mean is the average of a set of numbers, providing a central value that summarizes the data.

Tags

CCSS.6.SP.A.3