Data science is one of the most fascinating technologies today, and statistics is the backbone of this technology, powering data-driven decision-making, data analysis, and problem solving across industries.
A graduate student looking to start a career in data science or an experienced data science professional looking to grow in their existing data science job role must be familiar with all the basic to advanced concepts of statistics.
On a lighter note, anyone looking to get started or advance in this field can enroll in top data science certifications to master these basic statistical concepts.
So, in this article, we will introduce beginners to the foundational concepts of statistics and simplify important terms and principles to help you understand the subject with ease.
What is Statistics?
Statistics is the branch of mathematics dealing with the collection, analysis, interpretation, presentation, and organization of data.
Statistics help data science professionals understand trends, test hypotheses, and make informed decisions based on data instead of just making assumptions.
There are two main branches of statistics:
1. Descriptive Statistics – Summarizes and presents data in a meaningful way
2. Inferential Statistics – Uses a sample to make predictions or inferences about a larger population.
In the following sections, we will discuss these in detail, along with other essential concepts. So, let’s get started.
Types of Data
The first thing is having a strong understanding of various types of data, so that you can apply statistical techniques effectively.
Data are basically classified into two types:
· Qualitative Data
o Non-numerical data like gender, colors, brands, etc.
· Quantitative Data
o Data that can be measured and expressed in numbers.
▪ Discrete data – countable values like the number of students
▪ Continuous data – measurable quantities like height or temperature
Descriptive Statistics
Descriptive statistics, as the name suggests, provides a way to describe the basic features of data. It includes:
1. Measures of Central Tendency
This describes the center point or typical value in a dataset.
· Mean – it is the sum of all values divided by the total number of values
· Median – it is the middle value when data is arranged in either ascending or descending order
· Mode – it is the value that appears most frequently in the dataset
2. Measure of Dispersion
This is used to indicate how spread out the data is. It includes:
· Range – difference between the highest and lowest values
· Variance – average squared difference from the mean
· Standard deviation – it is the square root of the variance and shows how much variation exists from the mean
These measures help us understand if data points are clustered or scattered.
Check out the Certified Lead Data Scientist (CLDS™) certification from USDSI®, which dives deeper into advanced statistical concepts to learn these concepts in depth.
Inferential Statistics
While descriptive statistics are used to summarize data, inferential statistics help data scientists draw conclusions beyond the data available.
It includes the following concepts.
1. Population vs. Sample
· Population – the entire group you want to study
· Sample – a portion of the population that is used to make inferences
Data scientists use sampling to make predictions or generalizations without studying the entire population.
2. Hypothesis Testing
The hypothesis testing method is used to test an assumption (hypothesis) about a population parameter. The common steps involved in hypothesis testing are:
· Formulating the null hypothesis (H₀) and alternative hypothesis (H₁)
· Selecting a significance level (commonly 5%)
· Calculating a test statistic (like Z, t, or chi-square)
· Making a decision to accept or reject the null hypothesis
3. Confidence Intervals
Confidence intervals give us a range of values that may contain the population parameter. For example, a 95% confidence interval means there is a 95% chance that the true value lies within that range.
Basics of Probability
Probability is the foundation of inferential statistics. It is used to measure the probability of an event occurring. It is expressed in numbers between 0 and 1.
Here are some key concepts of probability:
· Independent events – when the occurrence of one event does not affect the other
· Dependent events – it is when the outcome of one event affects the other
· Mutually exclusive events – here, two events cannot occur at the same time
A proper understanding of probability will help you make more accurate predictions and assess risks. This is why it is an important component in areas like finance, insurance, or data modeling.
Distributions in Statistics
Distribution shows how values are spread across a dataset. The most common types are normal distribution, which is also known as the bell curve, in which most of the data points cluster around the mean.
Other types of distributions are:
· The binomial distribution, which organizes the number of successes in a fixed number of trials
· Poisson distribution, which is used to count data, such as the number of emails received in an hour
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