Data Science Prerequisites - Numpy, Matplotlib, and Pandas in Python - Histogram

The video tutorial explains how to plot histograms to visualize data distribution. It begins by generating random numbers from a standard normal distribution and demonstrates plotting a histogram using PLT.hist. The tutorial then discusses improving histogram detail by adjusting the number of bins and verifying the data's normal distribution. Finally, it explores whether the random function samples from a uniform distribution by generating data and plotting another histogram.

What is the primary purpose of using histograms in data analysis?

• To plot the distribution of data

• To find the median of data

• To determine the mode of data

• To calculate the mean of data

How can you improve the representation of a histogram with many data points?

• By changing the data type

• By using a different color scheme

• By increasing the number of bins

• By reducing the number of data points

What does a bell curve in a histogram indicate about the data?

• The data is bimodal

• The data is uniformly distributed

• The data is skewed

• The data follows a normal distribution

What is the expected shape of a histogram when data is sampled from a uniform distribution?

• Bell-shaped

• Skewed to the left

• Flat

• Skewed to the right

Which function is used to generate random numbers from a uniform distribution in the given context?

• NP.random.normal

• NP.random.randint

• NP.random.uniform

• NP.random.random