What is Mean? Median? Mode? why they are used?

Mean:


Its nothing but Average. 

Well, what is Average?

When you see some scoring Not Higher, Not Lower, but medium marks like 50, 60 etc.. we call them average student.


Example:

Now, I have 5 Students Marks: 10,40,51,68,98

What is the Average mark of this 5 Students?

53.4 is the Average score of 5 students.

How this is calculated?

(10+40+51+68+98) / Number of values.

How many values I have? 10,40,51,68,98 - I have 5 Values.

So, (10+40+51+68+98) / 5

267 / 5

= 53. 4




Mode:


It is the number that is repeating more times in the group of numbers.


Example:

 I have money in my Pocket.

1 - 500 rupees Note, 3 - 100 Rupees Note, 7 - 20 Rupees Note.

Now, tell me which Note I have the Most?

20 Rupees Right? because I have 7 20 rupees Notes. 

Exactly, that is how we check Mode.

If you are given data with numbers (1,2,3,4,5,5,5,5,5,5,6,7,8,9)

You know which is Mode. It is 5, because it is the most available number in this group.


Median:


It is the central Point of data. 

You are traveling from Chennai to Ramanathapuram District, which is 500 Kms. 

You are stopping at exactly 250.5 Kms on the way for Coffee!

Now, you are standing at the Median.


Example:

when you have number (1,2,3,4,5,6,7,8,9,10)

What is the center number here : Both 5 and 6.

But, we cannot keep 2 numbers as Median. So let us find that is the center number for 5 and 6. 

It is 5.5.

The median is 5.5, that is the exact center number.

When you have number (1,2,3,4,5,6,7,8,9,10,11)

What is the Center Number: Its 6.

It is a single Number, so we can keep it as Median.



Mean / Average, Mode, Median are important basic Calculations in data science, but without it, we cannot do machine learning models.

In Fact, without learning these basics, you cannot write codes with bigger formulas.

They will be used in almost all of your data projects.

Comments

Post a Comment

Popular posts from this blog

What is Artificial Intelligence? What is Machine Learning? What is Data Science? how they are related to each other?

Image
Artificial Intelligence means making Computers think like Human Brain, choosing correct & realistic decisions, answers based on the Current circumstance and importance. Personal Example: Data Science = What is Actually Happening in your Life. Machine Learning = What you are learning and experiencing in your Life. Artificial Intelligence = Taking Correct Decisions based on your Life Experience Another Example: You are AI Engineer & you created a Personal AI Friend using Python. Now, You give all information about your Financial Status, Economic Status, Emotional Status, Social Status, Current Situation in your Life, Current Situation in your Family, Current Situation in your Country etc etc... and your Current problems in an Excel file or SQL Database. Now you are giving this data to your AI tool which you created. 1. It will analyze the information, choose which is important and which is useless based on your current situation. 2. Now, it sends that important Information to Mac...
Read more

Linear Algebra - What is it?

Linear Algebra is a branch of mathematics that deals with vectors , matrices , and linear transformations . It's one of the foundations of data science, machine learning, and computer graphics. In Simple Words : Linear algebra helps us: Solve equations with multiple variables ( variables - a value, number or anything that changes anytime eg, Mark, Temperature, Price ) Represent and transform data using matrices ( Matrix - numbers arranged in rows and columns) Understand how systems behave (like how multiple inputs affect an output) Key Concepts : Scalars – Scalar means Magnitude(Speed). So it is just a single number (e.g., 8 or 6 or 10 etc..) Vectors – Vector means Both Magnitude (Speed) and Direction. So it is a ordered list of numbers (e.g., [2, 3] or [4, 5] or [10, 50] etc..) Matrices – A grid of numbers arranged in rows and Columns  (e.g., a 2x2 matrix or 4x4 matrix) Matrix Multiplication – Combining 2 or more matrices to transform data. Linear Transformation - Multiply...
Read more

What is a Python Library?

When we use Python for data analysis, machine learning, or any programming, we do not write each and every code. Why Should we? Some experts already wrote code for us and packed it as a Library. This reusable collection of code is called a Python Library . A library in Python is like a ready-made toolbox. Instead of building every tool yourself, you take the tools from the toolbox and start using it in your code. Why do we need Python Libraries? Save time – We do not write the same code again and again. Easy to use – Just install, import, and start using. Powerful features – Some libraries can do complex tasks in one line of code. Community support – Big communities maintain them, so they keep improving. Examples of Python Data Science Libraries: Pandas – Works with data tables like Excel, filtering and summarizing data NumPy – Works with numbers, arrays, mathematical calculations Matplotlib – Creates graphs and charts Scikit-learn – For machine learning models like regres...
Read more