DataScienceAulas

This repository contains all the exercises made in class during the Alura's course "Data Science: Data analysis and visualization".

Data Analysis with Pandas:

1. Importing pandas: Import the pandas library for data analysis.

2. Reading CSV data: Read data from a CSV file.

3. Renaming columns: Rename columns in a DataFrame.

4. Counting data and improving information visualization: Count data and visualize information using descriptive statistics and data visualization techniques.

5. Working with queries: Perform data manipulation using queries.

6. Identifying bins: Identify bins for data discretization.

7. Filtering a single column: Filter data based on values in a specific column.

8. Importing a CSV: Import data from a CSV file.

9. Identifying variable types: Determine the data type of a variable based on its content.

10. Categorical vs. ordinal variables: Differentiate between categorical and ordinal variables.

11. Interpreting quantitative variables: Understand the meaning of quantitative variables.

12. Comparing categories: Compare categories in a dataset.

13. Understanding Series: Understand the concept of a Pandas Series.

14. Converting a Series to a DataFrame: Convert a Series to a DataFrame using the to_frame() function.

15. Removing the index: Remove the index from a DataFrame to create a two-column format using the reset_index() function.

16. Using Categorical from Seaborn: Utilize the Categorical function from the Seaborn library for data visualization.

17. Rescaling a plot: Rescale the axes of a plot.

18. Sorting a plot: Sort the data in a plot.

19. Changing plot colors: Customize plot colors.

20. Comparing movie ratings: Compare the average ratings of movies.

21. Joining arrays: Combine arrays using the np.append() function.

22. Understanding data dispersion: Understand the concept of data dispersion.

23. Understanding standard deviation: Understand the concept of standard deviation.