Data Science Course > Measures of Central Tendency and Variability

Quartiles in Data Science

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A quartile is the 25th, 50th, and 75th percentile of a data set. This means that if you were looking at the median for example, then the 25th percentile would be the median point in your data set (50% below and 50% above), while the 75th percentile would be where half your data falls below and half falls above this point.

The first quartile is the median, which is found by arranging the values in ascending order and then finding the value that divides them into two equal parts. The second quartile is found by arranging the values in descending order and then finding the value that divides them into two equal parts. The third quartile is found by arranging the values in ascending order and then finding the value that divides them into three equal parts.

The most common quartile is the first quartile. It is most likely that the first quartile will be a good fit for your needs. This is because this quartile has the least amount of information in it. The second and third quartiles are more likely to be too detailed for your needs, but they may also be a good fit depending on your specific goals.

The importance of data science is not limited to the number of data points that a model can handle. It is also important to know how those data points are distributed, which is why quartiles are important in data science.

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  3. Levels of Data Measurement in Data Science
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  1. Charts and Graphs in Data Science
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  2. Frequency Distribution in Data Science
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  3. Bar Graphs and Pie Charts in Data Science
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  4. Summarizing Quantitative Data in Data Science
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  5. Cummulative Distribution in Data science
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  6. Histogram and Scatter Plot in Data Science
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  7. Cross Tabulation in Data Science
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  1. Measures of Central Tendency and Variability in Data Science
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  2. Mean and Percentiles in Data Science
    5m 32s
  3. Quartiles in Data Science
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  4. Measures of Variability in Data Science
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  5. Variance and Standard Deviation in Data Science
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  6. Application of Standard Deviation in Data Science
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  7. Z-score and Co-efficient of Variation in Data Science
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  8. Measures of Central Tendency for Grouped Data in Data Science
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  9. Standard Deviation and Variance for Grouped Data in Data Science
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  1. Skewness in Data Science
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  2. Kurtosis in Data Science
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  2. Statistical Hypothesis in Data Science
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  3. Errors in Statistical Hypothesis in Data Science
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  4. Statistical Inference for Single Population in Data Science
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  5. Testing Example in Data Science
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  6. Statistical Inference with Two Population in Data Science
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  5. Example of K Means Clustering in Data Science
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  1. Theoretical Concepts of Dashboard
    5m 41s
  2. Creating Pivots and Preparing Charts
    5m 36s
  3. Slicers in Dashboard
    3m 27s
  4. Preparing Final Dashboard
    8m 20s
  5. Presenting Dashboard
    3m 2s
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