Introduction to Statistics for Data Science

Statistics is the science of collecting, analyzing, and interpreting data. It is a fundamental tool in data science, helping us make sense of large datasets and derive meaningful insights. Understanding statistics is essential for anyone working in data science or AIML (Artificial Intelligence and Machine Learning).

Key Concepts:

1. Data Types: Quantitative (numerical) and Qualitative (categorical)
2. Descriptive Statistics: Summarize and describe the main features of a dataset.
3. Inferential Statistics: Make predictions or inferences about a population based on a sample of data.

Working:

• Data Types:
  • Quantitative: Data that represents counts or measurements (e.g., height, weight, age).
  • Qualitative: Data that represents categories or groups (e.g., colors, brands, yes/no answers).
• Descriptive Statistics:
  • Measures of Central Tendency: Mean, median, mode
  • Measures of Dispersion: Range, variance, standard deviation
• Inferential Statistics:
  • Hypothesis Testing: Used to make decisions or predictions about a population.
  • Confidence Intervals: Range of values used to estimate the true value of a population parameter.

Mathematical Formulas and Examples:

Variance=[(12−15.4)²+(15−15.4)²+(14−15.4)²+(10−15.4)²+(18−15.4)²+(20−15.4)²+(15−15.4)²+(14−15.4)²+(17−15.4)²+(19−15.4)²] / 10​=9.04

Real-Time Use

Understanding these concepts helps in everyday situations like calculating averages, finding the most common value, and understanding the spread of data in practical scenarios.

Python Implementation:

Let’s look at some basic statistical operations using Python.

Calculating Mean, Median, and Mode:

!pip install scipy       # if not installed

import numpy as np
from scipy import stats

data = [12, 15, 14, 10, 18, 20, 15, 14, 17, 19]

mean = np.mean(data)
median = np.median(data)
mode = stats.mode(data)

print(f"Mean: {mean}")
print(f"Median: {median}")
print(f"Mode: {mode.mode[0]}")

Calculating Range, Variance, and Standard Deviation:

range_ = np.ptp(data)
variance = np.var(data)
std_dev = np.std(data)

print(f"Range: {range_}")
print(f"Variance: {variance}")
print(f"Standard Deviation: {std_dev}")

Conclusion:

Statistics is a powerful tool that helps us understand and interpret data. By learning the basics of statistics, you can begin to analyze data and make informed decisions. Statistics forms the foundation for data analysis, providing tools to summarize and interpret data effectively

Practice Set:

1. Calculate the mean, median, and mode for the following dataset: [22, 25, 27, 22, 30, 22, 35, 28].
2. Find the range, variance, and standard deviation for the dataset: [45, 47, 50, 46, 53, 48, 49].

Future Work:

Future articles will delve deeper into variability, probability, distributions, and more advanced statistical concepts.

This article lays the groundwork by introducing fundamental statistical measures and their applications, preparing students for deeper exploration in subsequent articles.