Practical Data Science using Python - Types of Data and Descriptive Statistics

The video tutorial covers the types of data: nominal, ordinal, interval, and ratio, explaining their characteristics and differences. It also discusses discrete and continuous variables, highlighting their unique properties. The tutorial delves into descriptive statistics, focusing on mean, median, and mode, and explains how to calculate standard deviation and variance. The importance of understanding these concepts in data science and machine learning is emphasized, as they are crucial for converting data into numeric variables for analysis.

Which type of data is characterized by labels without any inherent order?

• Ratio

• Ordinal

• Nominal

• Interval

What distinguishes ordinal data from nominal data?

• Ordinal data is continuous.

• Ordinal data has a perceivable degree of measurement.

• Ordinal data is numeric.

• Ordinal data has an absolute zero.

Which of the following is an example of a discrete variable?

• Height

• Weight

• Number of students

• Temperature

What is a key characteristic of continuous variables?

• They can only be whole numbers.

• They are always positive.

• They have a finite number of values.

• They can have an infinite number of values between any two points.

Which measure of central tendency is calculated as the middle value of a data set?

• Mean

• Mode

• Median

• Range

In a data set, which measure of central tendency is most affected by extreme values?

• Mean

• Mode

• Standard Deviation

• Median

What does standard deviation measure in a data set?

• The sum of all values

• The most frequent value

• The spread of data points

• The central value

How is variance related to standard deviation?

• Variance is the sum of standard deviation.

• Variance is unrelated to standard deviation.

• Variance is the square of standard deviation.

• Variance is the square root of standard deviation.

What is the main difference between population and sample standard deviation?

• Population standard deviation uses n-1 in the denominator.

• Sample standard deviation uses n-1 in the denominator.

• Sample standard deviation is always smaller.

• Population standard deviation is always larger.

Why might a sample be used instead of a population in statistical experiments?

• To increase accuracy

• To reduce computation time and resources

• To ensure all data is included

• To avoid bias