Track 03

Theme: Descriptive statistics is a branch of statistics that involves organizing, summarizing, and presenting data in a meaningful way. Here are several things that can be created or derived using descriptive statistics: 

• Frequency distributions: Descriptive statistics can help in creating frequency tables or histograms, which display the frequency or count of each value or range of values in a dataset. This provides a visual representation of the data distribution.

• Percentiles and quartiles: Descriptive statistics allow you to calculate percentiles and quartiles, which divide a dataset into equal portions. These measures indicate the relative position of a specific value within the dataset.

• Summary statistics: Descriptive statistics can provide a summary of the dataset, including minimum and maximum values, the range, mean, median, mode, standard deviation, and quartiles. These statistics give a comprehensive overview of the dataset.

• Data visualization: Descriptive statistics can be utilized to create visual representations of data, such as line charts, bar graphs, scatter plots, and box plots. These visualizations make it easier to understand patterns, trends, and relationships within the data.

• Outlier detection: Descriptive statistics can identify outliers, which are extreme values that deviate significantly from the rest of the dataset. Outliers can provide valuable insights or indicate data errors.

This learning path is designed to develop three skills: 

1.  Learn how to read data from an Excel file on a web page.

2.  Extract and treat the data making it read for Exploratory Data Analysis (EDA).

3.  Create a bar graph using the data.

4.  Extract summary statistics from the data.

5. Create a box plot and know how to find outliers.

6. Know how to extract summary statistics without outliers.

The next links will help in this learning journey. Have a good learning journey.