INTRODUCTION

Data science is the art and science of extracting valuable knowledge and actionable insights from vast and complex datasets. It empowers individuals and organizations to make data-driven decisions, solve intricate problems, and unlock the potential of data that surrounds us. As a data scientist, you step into a role that not only demands technical expertise but also offers boundless opportunities to contribute meaningfully to society and businesses alike.

ROAD MAP:

1 Learn a programing language

2 Understand statistics

3:Linear algebra

4:Calculus

5:Probability

6:Learn how to use data visualization tools like excel

7:Understand how machine learning algorithms work

8:Databases 

1.PROGRAMMING LANGUAGE

One can learn a programming language of choice since its needed for manipulation of data.The commonly used languages are python and R.He/she can start by learning the basic of the language and understand concepts like variables , logic flow ,different data types(strings , lists, dictionaries etc).Learning the different libraries would come in handy ,an example in python you can start with numpy or pandas.This skills are essential as they aid in data manipulation.Later one can learn object oriented programming and create their own data types

2:STATISTICS

Statistics equips you with the know how in mean , mode ,standard deviation , median ect.One should not only know the formulas but have a deeper understanding of what they represent.

3:LINEAR ALGEBRA

Under this one should have the knowledge in Vectors , matrices,linear transformations.

Vectors and Vector Operations:

Vectors are like arrows with both length and direction. Vector operations include adding, subtracting, and scaling these arrows to represent things like speed, force, or direction change.

Matrices and Matrix Operations:

Matrices are like grids of numbers. Matrix operations involve adding, subtracting, multiplying, and transforming these grids to process data efficiently, like in spreadsheets.

Linear Transformations:

changing a shape, like a square,stretching, shrinking, rotating, or shearing is called transformation . These transformations are linear if they maintain the basic shape. Linear transformations are essential for things like resizing images or transforming data in various ways.

### 4:Calculus
One should be have knowledge in concepts like derivatives, 1st and 2nd derivatives.This helps in understanding what is happening to the data.

5:Probability

This enables one to understand outputs of some algorithms in machine learning whose end results are the odds of something being meeting a certain conditions.One should have be able to understand things like mean,median,random experiments,distributions etc.

6:Data visualization tools

For datasets one can learn to use tools like excel ,Tableau,Microsoft Power BI,Google Data Studio.This enables one to quickly derive some basic information from the data sets which are easily noticeable before diving into to the code.

7:MACHINE LEARNING.

One can start by familiarizing with the different types of machine learning :
1:supervised machine learning
2:Unsupervised machine learning
3:Reinforcement Learning

Understanding the math behind machine learning is of essence and this is aided by having the knowledge in math.
This can be followed by understanding the different libraries used in ML like tensorflow, pytorch, keras.
The next step would be understanding deep learning which entails concepts like Artificial Neural Network (ANN),Recurrent Neural Network (RNN),Convolutional Neural Network (CNN):

8:DATABASES

Here one learns how to use the different data frames like mysql which enables one to store large datasets .This is essential as most of the data will be stored in data frames.