Top Key Statistical Concepts for Data Science
Learn Key Statistics in Data Science with Practical Python Examples
Statistics is like the starting point for data science. It’s the tool that helps us understand data better. Whether you’re trying to figure out what’s happening in the market, make predictions about the future, or make sense of a small chunk of data, knowing about statistics is super important. In this article, we will delve into key statistical concepts and applications in data science. We’ll also use Python to show practical examples to make it easier to understand.
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Descriptive Statistics
Descriptive statistics are tools that help us understand data better. They include things like the average, middle value, most common value, and how spread out the data is. With Python libraries like NumPy and Pandas, you can find and show these statistics.
Python Code Syntax:
import numpy as np
data = np.array([12, 15, 18, 21, 24, 27, 30])
mean = np.mean(data)
median = np.median(data)
mode = np.mode(data)Use Case: Calculate and display the central tendencies of a dataset to understand the distribution of student ages in a class.
Inferential Statistics
Inferential statistics involves drawing conclusions about a population from a sample. Methods like hypothesis testing and confidence intervals are used for Inferential statistics. Python’s SciPy library has tools that help us do inferential statistics.
Python Code Syntax:
from scipy import stats
sample_data = [5, 6, 7, 8, 9]
population_mean = 7.5
t_statistic, p_value = stats.ttest_1samp(sample_data, population_mean)Use Case: Determine if a sample of product reviews is representative of the entire customer population’s satisfaction level.
Probability
Probability theory is like the building block of statistics. It’s all about dealing with things we’re not completely sure about and making guesses. In Python, the NumPy library has handy tools for working with probabilities.
Python Code Syntax:
from random import randint
probability_of_heads = sum(randint(0, 1) for _ in range(1000)) / 1000Use Case: Calculate the probability of getting heads in a series of coin flips.
Sampling Techniques
Sampling is when we pick out a smaller group from a big bunch of data. We use different methods like random, stratified, or systematic sampling to make sure our small group is a good representation of the big group.
Python Code Syntax:
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