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What is Data Science & How to become a Data Scientist - A complete Guide for Beginners

What is Data Science & How to become a Data Scientist - A complete Guide for Beginners

June 21, 2021, 3:08 p.m.




Complete Guide on Data Science


Whenever I see people questioning on Internet about data science field, Most probably they are going to ask

1) What is data science actually
2) How to learn data science
3) What is included in field of data science
4) What is future of data science
5) What are salaries of Data scientists
7) Are remote jobs widely available

So lets Discuss all questions one by one so that you completely understand this field and decide that should you step in data science or NOT?



What is data science


Data science is a field in which scientist perform different operations on data including

  1. finding trends and patterns in data
  2. Transform that data into something that is understandable for a normal/non technical person
  3. Apply different artificial intelligent algorithms that are able to learn through this data and predict future e.g stock market prices, next frames in video etc.


Sounds complex,right? Don't worry,go through guide, believe me its simple and basic. So mainly there are 6 steps of doing any project in data science


Step 1 : Understand The problem


Data science is not like simple programming in which you can just write a peice of code. In datascience there are thousand of ways to solve single problem so you have to find the best solution among all. So the best way is that map your problem and think about best way to solve it.


Step 2 : Grab the data


Grabbing quality data is bit challenging task, either you have to create one or use any other dataset available on sites like kaggle etc. There is possibility that you want to grab a portion of data so you can use SQL etc to get needed data.Now you have data lets move forward to next step that is cleaning of data.


Step 3 : Data cleaning


Data cleaning is process in which you clean values in data, Assume that you want to delete certain columns that are not useful so you can wipe them out. or if there are some values that should not be in dataset which you want so you can replace them or remove them.In short you remove irrelevent data and keep what you need.


Step 4 : Data wrangling


While exploration of the data, when scientists found problems in the dataset, which need to be solved before the data is ready for a EDA (data visualization). This exercise is typically referred as “Data Munging”. In this process, characteristics of dataset is extracted and null values are either dropped or filled by some mean.In short this process includes cleaning of data,structuring that data into some better format for better decision making in a lot less time.


Step 5 : Data visualization


Also known as Exploratory data analysis in which data is been visualized in different types of graphs. Here dataset is been summarized and main characteristics are visualized.


Step 6 : Deploy Machine Learning Model - If needed


Its not necessary that you deploy machine learning algorithm in every data science project but often needed most of the time. AI is more specific field which involved tone of programming but data science is more broad term which includes different techniques and strategies related to statistical analysis and visualizations. So deploying ML model enables our project to learn from data and predicts the future outcomes.


Step 7 : Repeat Repeat Repeat


And lastly repeat above processes in  order to improve the results you are getting.


How to learn data science


I am going to provide simple answer to that question. Lets see step by step

First learn SQL. Its easy and most basic query language, also its logical so you will get idea of dealing with data.

The programming languages which are preffered for data scientists are python and R. 

R is much more specific for data science but python is general-purpose programming language. That means python can be used for desktop apps, android apps(no recommended), web apps, data science projects, and we can create different bots in python.

So if you decided to start with python here are some tips and frameworks which you should learn in following order.

  1. Learn strong basics of python
  2. Learn advance python concepts including OOP
  3. Now more specifically for data science
    1. NLP (Natural language processing)
    2. Pandas
    3. sklearn
    4. Opencv (for Image processing)
    5. Tensorflow / pytorch / keras
  4. More ever learning django and flask + PyQt or wx python for making desktop and webapps will be plus point.

    Above mentioned libraries/frameworks are used for ML Engineering that also comes under umbrella of Data science

Other tools for data scientists are following

  1. SAS. It is one of those data science tools which are specifically designed for statistical operations
  2. Apache Spark.
  3. BigML
  4. D3
  6. Excel
  7. ggplot2
  8. Tableau
  9. Power BI
  10. Google dashboards



What is included in Data Science



data science scope


From above image( its clear that data science is more broad term that contains sub fields in it.

  • Data Mining and Statistical Analysis
  • Business Intelligence & Strategy-Making
  • Data Engineering and Data Warehousing
  • Database Management and Data Architecture
  • Operations-Related Data Analytics
  • Data visualization
  • Machine Learning and Cognitive Specialist
  • Market Data Analytics


What is future of Data science


In short words, this field is expanding every minute . Obviously data is generating every second my million of devices connected online. Big companies like google, facebook are not giving every service like search engines & storages for free, They are taking our personal data and that data is used to learn customer behavior

for better marketing campaigns. Assume data as a currency, you are giving data and in return you are getting services.

So we have seen that data is generating every second so people are required that deal with that data and here comes data scientists. You can learn data science and you are gonna get handsome job if u have command on that skill.

Also as compared to desktop mobile and web developement, data science is still less saturated.


What are salaries of Data scientists


The average salary of data scientist is $116,054 in US (by glassdoors) however mostly people are also doing freelancing along with their jobs. In this way they generate high monthly revenue.

(Also salary depends on different factors like number of hours location remote job or physical so it may vary).

Among all subfields of data science, ML Engineers have the most highly paid jobs.


Are Remote jobs widely available


Yes, you can do either physical or remote jobs. Companies are regularly posting remote jobs on linkedin glassdoors and indeed etc. Also build a strong linkedIn profile and it will definetely help you to grab a data science job.



This guide is based on Experience of self learning, I am not relating this guide with any bachelors or master degree in field of data science. Remember one thing that only you can learn this field if you want to. Universities out there are only going to provide overview of data science but in actual its life time learning process that ain't gonna stop. So its better to start learning



Happy Coding