Data Science 🐍 Graphical Analysis

The video tutorial covers data visualization techniques using Python libraries like Matplotlib and Plotly. It explains how to generate and import data, create basic plots, and customize them. The tutorial also explores advanced plot types such as scatter, bar, pair, box, violin, and joint plots. Additionally, it includes a practical TC Lab activity to apply these visualization techniques.

Which of the following libraries is NOT mentioned as a tool for data visualization?

• Seaborn

• Matplotlib

• NumPy

• Plotly

What is the purpose of using 'PLT dot show' in Matplotlib?

• To save the plot as an image

• To display the plot in the window

• To change the plot's color

• To add labels to the plot

How can you change the color of a line plot in Matplotlib?

• By using the 'shape' parameter

• By using the 'color' parameter

• By using the 'size' parameter

• By using the 'style' parameter

What is a key feature of scatter plots compared to regular line plots?

• They show data points connected by lines

• They display individual data points

• They are used for time series data

• They are only available in Plotly

In a bar chart, what does the 'alpha' parameter control?

• The width of the bars

• The color of the bars

• The height of the bars

• The transparency of the bars

What does a pair plot primarily show?

• Distribution of a single variable

• Time series data

• Correlations between variables

• Quartiles of a dataset

What is the main use of a box plot?

• To show quartiles and outliers

• To display individual data points

• To visualize categorical data

• To show time series data

How does a violin plot differ from a box plot?

• It shows only the median

• It includes distribution information

• It is used for time series data

• It does not show outliers

What does a joint plot display?

• Two variables with their joint distribution

• Only one variable

• A time series of data

• Categorical data

What is the purpose of using a TC lab in data visualization?

• To visualize categorical data

• To generate random numbers

• To collect real-time data for analysis

• To create synthetic data