What is Data Science ? How to become a Data Scientist ?

What is Data Science?



Data Science is the field of study that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. It combines various disciplines such as statistics, machine learning, data analysis, and visualization to uncover hidden patterns, trends, and correlations in data. Data science plays a crucial role in decision-making, forecasting, and problem-solving across industries, driving innovation and enabling organizations to make data-driven decisions..

So briefly it can be said that Data Science involves:

  • Statistics, computer science, mathematics
  • Data cleaning and formatting
  • Data visualization

Why Data Science?


So before jumping into the complete Roadmap of Data Science, one should have a clear goal in their mind about why they want to learn Data Science. Is it for the phrase “The Sexiest Job of the 21st Century“? Is it for your college academic projects? or is it for your long-term career? or do you want to switch your career to the data scientist world? So first make a clear goal. 

Why do you want to learn Data Science? For example, if you want to learn Data Science for your college Academic projects then it’s enough to just learn the beginner things in Data Science. Similarly, if you want to build your long-term career then you should learn professional or advanced things also. You have to cover all the prerequisite things in detail. So it’s in your hand and it’s your decision why you want to learn Data Science.

What Does a Data Scientist Do?


A Data Scientist gathers and analyzes complex data to guide business decisions. They collect, clean, and explore data, develop machine learning models, and deploy them for real-world use. 

Data Scientists also monitor and maintain models, communicate findings to non-technical stakeholders, and collaborate across teams to align with organizational goals.

Why Become a Data Scientist


In the global landscape, data is the new oil, driving innovation and reshaping industries. Organizations crave skilled professionals to extract insights from this vast information ocean, and here’s where data scientists play a crucial role.

High Demand

  • US Bureau of Labor Statistics forecasts a 23% job growth for data scientists (2020-2030), surpassing the average.
  • Similar global trends indicate a surge in demand.

Lucrative Salaries

  • Handsome rewards for expertise; US data scientists earn over $120,000 annually.
  • In India, experienced professionals can fetch upwards of ₹15 lakhs (USD 18,750).

Impactful Work

  • Tangible societal impact: Develop algorithms for disease detection, optimize energy grids, or predict natural disasters.

Skills Required to Become a Data Scientist


Usually, data scientists come from various educational and work experience backgrounds, and most should be proficient in, or in an ideal case be masters in four key areas.


  1. Domain Knowledge
  2. Math Skills
  3. Computer Science
  4. Communication Skill

Domain Knowledge

Most people think that domain knowledge is not important in data science, but it is very important. Let’s take an example: If you want to be a data scientist in the banking sector, and you have much more information about the banking sector like stock trading, finance, etc. This is going to be very beneficial for you and the bank itself will give more preference to these types of applicants than a normal applicant. 

Math Skills

Linear Algebra, Multivariable Calculus & Optimization Techniques, are three things that are very important as they help us in understanding various machine learning algorithms that play an important role in Data Science. Similarly, understanding Statistics is very significant as this is a part of Data analysis. Probability is also significant to statistics and it is considered a prerequisite for mastering machine learning.

Computer Science

There is much more to learn in computer science. But when it comes to the programming language one of the major questions that arise is: 

Python or R for Data Science?

There are various reasons to choose which language for Data Science as both have a rich set of libraries to implement complex machine learning algorithms, visualization, and data cleaning. Please refer to R vs Python in Data Science to know more about this. Knowing both of these languages will provide an extra boost in your career as a data scientist.

Apart from the programming language, the other computer science skills you have to learn are:

  • Basics of Data Structure and Algorithm
  • SQL
  • MongoDB
  • Linux
  • Git
  • Distributed Computing
  • Machine Learning and Deep Learning, etc.

Communication Skills

It includes both written and verbal communication. What happens in a data science project is after concluding the analysis, the project has to be communicated to others. Sometimes this may be a report you send to your boss or team at work. Other times it may be a blog post. Often it may be a presentation to a group of colleagues. 

Regardless, a data science project always involves some form of communication of the project’s findings. So it’s necessary to have communication skills for becoming a data scientist.

Learning Resources

There are plenty of resources and videos available online and it’s confusing for someone where to start learning all the concepts. Initially, as a beginner, if you get overwhelmed with so many concepts then don’t be afraid and stop learning. Have patience, explore, and stay committed to it.


Data Scientist vs Data Analyst

Here is a quick comparison of Data Scientist and Data Analyst

AspectData ScientistData Analyst
ScopeBroader focus: machine learning, predictive modeling.Focus: analyzing data, and providing insights.
FocusUncovering patterns, and predicting trends.Summarizing historical data, providing insights.
ResponsibilitiesEnd-to-end processes, complex models.Proficient in tools, statistical methods, and reporting.
ToolsAdvanced: machine learning, Python/R.Tools: Excel, Tableau, Power BI.
Data TypesStructured, unstructured, large datasets.Primarily structured data, occasional smaller sets.
OutcomeExtract actionable insights, and solve complex problems.Summarize data, and provide insights for decision-making.
OverlapSome overlap and Analysts contribute to the early stages.Distinct roles, potential for collaboration.

Average Salary of a Data Scientist

The average salary of a data scientist varies depending on several factors, including experience, location, and skillset. However, it’s generally a high-paying profession with strong growth prospects. Here’s a breakdown:

Global Average

  • The worldwide average annual salary for a data scientist is around $105,000. (Source: Glassdoor)

United States

  • In the US, the average annual salary for a data scientist is $124,678. (Source: Indeed)
  • The median salary is $103,500, according to the Bureau of Labor Statistics. (Source: BLS)
  • Entry-level data scientists can expect to earn around $86,000, while experienced data scientists with specialized skills can make upwards of $156,000. (Source: Glassdoor)

India

  • In India, the average annual salary for a data scientist is ₹7,08,012. (Source: PayScale)
  • Freshers can expect to start at around ₹5,77,893, while experienced professionals can earn as much as ₹19,44,566. (Source: KnowledgeHut)



Factors Affecting Salary

Multiple factors might affect your salary as a data scientist:

  • Experience: As with most professions, experience plays a significant role in determining a data scientist’s salary. The more experience you have, the higher your earning potential.
  • Location: Salaries for data scientists tend to be higher in major tech hubs like San Francisco, New York, and Bangalore compared to smaller cities or rural areas.
  • Skills and Expertise: Data scientists with specialized skills in areas like machine learning, natural language processing, or specific programming languages can command higher salaries.
  • Company Size and Type: Large tech companies and startups may offer different salary structures and benefits packages.



Compiled By : Pushpendra Maurya

profession : Data Scientist


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