An Introduction to Statistics For Data Science: Basic Terminologies Explained
This article was published as a part of the Data Science Blogathon.
Are you an aspiring data scientist who wants to learn statistics for data science purposes? Did you find statistical concepts hard in your school and you are looking for easier ways to learn the statistical concepts to improve your capabilities of understanding the data now? If your answer is yes to both then you have come to the right place. Today, we introduce you to the concept of statistics that is widely accepted in the data science domain. Before learning the concepts it’s important to know what you can expect to learn.
Introduction to Statistics And Machine Learning:
What is Statistics? What are the types of statistical concepts you should know?
Statistics is one of the popularly known disciplines that is mainly focused on data collection, data organization, data analysis, data interpretation, and data visualization. Earlier, statistics was practiced by statisticians, economists, business owners to calculate and represent relevant data in their field. Nowadays, statistics has taken a pivotal role in various fields like data science, machine learning, data analyst role, business intelligence analyst role, computer science role, and much more.
While we are introduced to certain statistical concepts like central tendency and standard deviation much earlier. There are many more important statistical concepts that we need to learn and implement for data science and machine learning. Let’s learn about the basic terminologies of statistics and their categories.
Basic Terminologies in Statistics:
To become a master in the statistical program we should be familiar with certain terminologies. They are:
- Population: A population is the set of resources from where we can collect data
- Sample: A Sample is nothing but a subset of the Population which is used for sampling of data and in inferential statistics to predict the outcome.
- Variable: A Variable can be a number, a characteristic, or a quantity that can be counted. It can be also called a data point.
- Probability Distribution: A probability distribution is a mathematical concept that primarily gives the probabilities of occurrence of different possible outcomes generally for an experiment conducted by statisticians.
- Statistical Parameter: Statistical or population parameter is basically a quantity that helps in indexing a family of probability distributions like the mean, median, or mode of a population.
Types of Statistics Concepts:
- Descriptive Statistics -Descriptive statistics is a concept that allows us to analyze and summarize data and organize the same in the form of numbers graph, bar plots, histogram, pie chart, etc. Descriptive statistics is simply a process to describe our existing data. It transforms the raw observations into some meaningful data that can be further interpreted and used. Concepts like standard deviation, central tendency are widely used around the world when it comes to learning descriptive statistics.
- Inferential Statistics – Inferential statistics on the other hand is an important concept that deals with drawing conclusions based on small samples collected from the entire population. For example, during an election poll, people will often want to predict the exit poll results so they will conduct a survey in various parts of state or country and record their opinion. Based on the information they have collected they tend to draw conclusions and make inferences to predict results for the entire population.
Now, that we know the types of statistics, it is quite important to admit the pivotal role of statistics concepts in data science and machine learning, and how both are two closely related areas of study. Data Science Statistics actually helps us in selecting, evaluating, and interpreting predictive models for data science use cases.
Statistics and Data Science
The core concept of machine learning & data science is entirely built around statistics. Hence, it is important to learn the fundamentals of statistics thoroughly to solve real-world problems.
If you weren’t comfortable with statistics before, then we will explain certain concepts you need to master in order to ace your data science journey. You need to comfortable while learning mathematical equations and statistical formulas and theories to know what to apply where. It is hard no doubt but it is worth learning the subject.
Starting from exploratory data analysis until designing hypothesis testing, statistics play a crucial role in solving many problems across various industries and sectors, especially for data scientists.
Why should you master statistics concepts?
Nowadays, almost all companies have become data-driven and are using various concepts to interpret their existing data. That’s where fundamental statistical concepts come into play & their implementations help us in describing the data that we have in hand.
To solve the ongoing problems in the company and predict a better strategy to improve the profit margin of the company we need to learn concepts that help us understand the data and categorize it according to their features. Thankfully, statistics has a set of tools that help us organize and visualize the data and provide actionable insights.
Hence, it has become crucial to master statistical concepts at this point in time. There are plenty of online courses and books that are available to help us better our knowledge and become better data scientists.
How to Make Sense of the Current Data
Data is essentially nothing but a collection of observations that are present in our company system. With help of descriptive statistics, we can collect, organize, categorize, sample, visualize the data to make informed decisions for the company.
We can also use inferential statistics to predict outcomes. Generally, this concept is used when we are conduct surveys or doing market research, we tend to collect samples of data, and based on that we predict the findings for the entire population of that particular location.
Here are certain concepts that you need to master to become a better data science practitioner:
✔You need to calculate and apply measures of central tendency to grouped and ungrouped data.
✔You need to be comfortable in summarizing, presenting, and visualizing data, in a way that the reports obtained are clear and provide practical insights to the stakeholders and business owners of your company.
✔You also need to perform hypothesis tests that are required to use for common data sets.
✔Conduct rigorous correlation tests, and regression analysis to make send of the data.
✔Implement statistical concepts using R & Python and demonstrate your proficiency in this program.
✔ Become proficient in tools like Excel, Tableau, Power Bi to represent the data in a proper format
What is the Significance of Statistics in Our Daily Life?
Luckily for us, statistics can help us answer important questions about data like:
- Among the available data points which features are important for us to develop the model.
- What is the best way to conduct the experiment?
- How to design a strategy based on the outcomes of the experiment?
- What performance metrics should you focus on?
- How to interpret the outcome?
- How to differentiate between valid and noise data?
All these are common and important questions that hold statistical significance, and the data teams have to answer them to perform their tasks better.
These were some of the pointers you ought to know to get started with the statistical program. There are plenty of courses out there which help you improve your knowledge and become a better professional.
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