Data Visualization and Analysis Techniques

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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 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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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 are some common techniques used for data visualization?

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Bar charts, line charts, scatter plots, pie charts, and heat maps.

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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 is 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 difference between qualitative and quantitative data?

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Qualitative data describes characteristics or qualities, while quantitative data represents numerical values that can be measured.

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What is a histogram?

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A histogram is a graphical representation of the distribution of numerical data, showing the frequency of data points within specified ranges.

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What is a scatter plot?

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A scatter plot is a graph that uses dots to represent the values obtained for two different variables, showing the relationship between them.

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What is a pie chart?

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A pie chart is a circular statistical graphic divided into slices to illustrate numerical proportions.