How to Learn Data Science A Beginners Free Guide

Data science

How to Learn Data Science A Beginners Guide

At present technology is more powerful in the world. Data Science is one of the most powerful technology at present. Information is wealth and information is need this technology.

Data science
Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from noisy, structured and unstructured data, and apply knowledge and actionable insights from data across a broad range of application domains.


History of Data Science 

Actually data science is very old and very good process. It is the modern technology at present. I think this technology is very fast and very good for the world.

Data science
Lead a team as a data science and analytics expert. Make decisions & create new policies. Be indispensable in a developing industry – Receive a Master in Data Science & Analytics. Internships Available. Change Your Career. Study at Georgetown.

Here the picture show about the data science history and its details.


How to Learn Data Science

At present we can use modern technology for this type learning. It is very good and smart process for learning.


A Beginner’s Guide to Getting Started

Data science is a vast field with many sub-disciplines. It’s also one of the fastest-growing industries in the world.


Data scientists are hired for everything from finance to healthcare and the demand is only expected to grow.

To be successful in this competitive industry, you need to know how to learn data science.

Luckily, there are many resources available that can help. Here are some tips on how you can become a data scientist.


What is data science?

In 2014, this word was a new and strange one!!!

If I told you that, in addition to statistics, you’d need data visualization skills, a good understanding of machine learning, and a strong understanding of statistics, what would you think?

Many people get confused by exactly “data science” since there are so many different subcategories.


Without getting too technical, data science is the practice of using math to improve a business, product, or service. Let me try to explain.

First of all, there’s applied statistics and linear algebra.

These topics cover numbers that are used by everyday people, like decimal points.

On the other hand, Linear algebra focuses on calculations involving math, like algebraic formulas or vectors.


Why learn data science?

Since I was a kid, I’ve always been interested in numbers. In high school, it was online forums, and sometimes, stuff on the radio.


Once I got to college, I kept listening to books and asking questions. I realized that I wanted to use numbers as a part of my everyday life.

Many of you might be thinking that data science might seem like an unattainable goal, that there’s no way for me to pick up enough data science to get work in the field.


I’m not saying that there’s a way to be good at everything you want to be good at. But a gap exists, and this gap is where you should aim for.

For me, there are two very tangible skills that I want to be better at:

An understanding of the statistical modeling and statistical physics involved in numerical computation.


How to learn data science

I find that most students come to a machine learning class and think, “I need to memorize big numbers and formulas”.

But this is only the first part of the learning process. It’s more important to learn how to code and think about data than memorizing formulas.


When I first started to learn to code, I kept trying to code stuff as I studied.

It was slow, frustrating, and frustratingly difficult. This wasn’t the best learning method for me.

One of the best things about this course is that it teaches how to understand your data and create things with it.

You learn what a classification problem is, use a neural network, and design your algorithms.

These skills are essential to data science and become more and more important the deeper you go into your journey.


Try a MOOC

Probably the best tool I ever had for learning data science was Khan Academy. There are countless courses on data science on the platform.

Data science
What is MOOC test ?
MOOC stands for Massive Open Online Course. They are distance-learning courses run online by many universities worldwide. Usually, they are open to anyone who registers. One single course may admit even thousands of students.


I recommend the Introduction to Statistics course, but if you want a general introduction to the field, start with Introduction to Mathematics: Linear Algebra and Its Applications.

I went through this one for a few weeks and loved it.

This course starts with a brief overview of statistics, linear algebra, and matrices.


The course focuses on two basic mathematical concepts: Linear algebra and mathematical programming.

Linear algebra is the study of how these two concepts are related. Mathematical programming is the design and implementation of algorithms.


The introduction contains a lot of code in the form of JavaScript.


Try a boot camp

Boot camps are fantastic for learning fast because they give you a short period to learn a skill. You learn by doing.

There’s a problem and a time to solve it.


Once you solve it, you move on to the next one. The idea is that you can figure out all the challenges without any guidance or difficulty.

These boot camps are available in many areas, but one of my favorites is the data science boot camp.

You do a mini-course with some friends, and then take an exam to see how far you’ve come in your data science learning.

You get feedback and support from the teacher, but you still have to take it on yourself to keep learning.

Post Code = N202103


Try a self-learning approach.

The first thing I did to learn data science took a minimal approach.

The approach I chose was to try something new and see what happened.


This is where a self-learning approach makes the most sense.

In my case, I decided to learn Python (a Python-based programming language) as a data science language.

I’m more of an R user, so that was out.


I knew I wanted to work with Python, and I didn’t want to waste my time learning a new language (I just picked up Python three weeks ago, and I already feel like I could write a line of Python).


As I learned, I shared the project with my wife and asked for her input.

I had limited my data to only my undergraduate degree, so I only had a few years of data to learn on.



There’s so much hype around AI right now. I don’t think its right to assume that everyone should learn data science to have a job in the future.

The goal is to have a job in the present, so learn things that matter now. After all, it’s not like we can all get jobs in Amazon with this skill anyway.

So I propose an alternative approach to learning data science: be a part of the network that is changing the world.

You won’t make any money in the future from applying AI, but you’ll be able to help make the world a better place.

First Published = 22th November 2021

2nd Published = 29th April 2022

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