Mathematics for Data Science

Mathematics is a fundamental component of data science. It provides the necessary tools and techniques to analyze, interpret, and make predictions from data. In this section, we will review some important mathematical concepts used in data science.

Descriptive Statistics

Descriptive statistics is the branch of statistics that focuses on summarizing and describing the properties of a dataset. It helps us understand the central tendency, variability, and shape of the data. Common measures of descriptive statistics include:

• Mean: The average value of a dataset.
• Median: The middle value of a dataset.
• Standard Deviation: A measure of the dispersion of values around the mean.

Let's take a look at an example of how to calculate these measures using Python and the NumPy library:

1import numpy as np
2
3# Create an array
4arr = np.array([1, 2, 3, 4, 5])
5
6# Perform mathematical operations
7mean = np.mean(arr)
8median = np.median(arr)
9std_dev = np.std(arr)
10
11print(f'Mean: {mean}')
12print(f'Median: {median}')
13print(f'Standard Deviation: {std_dev}')

This code snippet creates an array and calculates the mean, median, and standard deviation of the values. These measures provide insights into the central tendency and variability of the dataset.

xxxxxxxxxx

import numpy as np

​

# Create an array

arr = np.array([1, 2, 3, 4, 5])

​

# Perform mathematical operations

mean = np.mean(arr)

median = np.median(arr)

std_dev = np.std(arr)

​

print(f'Mean: {mean}')

print(f'Median: {median}')

print(f'Standard Deviation: {std_dev}')