The Complete Roadmap to Become a Data Scientist

The Complete Roadmap to Become a Data Scientist

Data-Science is at the core of our modern lifestyle. Be it our YouTube video recommendations, Google ads, Instagram reels, or our autocorrect suggestions on various messaging apps, all these aspects are related to the field of Data Science, and its related operations. We will discuss a roadmap to become a data-scientist.


The term Data-Scientist means someone who studies, analyzes, and researches raw data. Such people are highly skilled in programming, statistics, and mathematics. Data Scientists acquire large chunks of raw data, analyze it to spot financial, stock, cybersecurity, and other related trends, and interpret it to predict market behavior, customer demands, threats, benefits, etc.

Data Scientists often use state-of-the-art analyzing approaches, including Machine Learning, Edge Computing, Predictive Modelling, and Blockchain as a part of their work. Interestingly, the role of a Data-Scientist was first listed as a job title on Facebook and LinkedIn in 2008 and is presently one of the most sought-after jobs with high salaries. If we consider the data available on the internet, data science jobs have increased by more than 62% in the past four years and salaries in this sector have increased by more than 25% compared to the past year.


There are countless different ways to learn Data-Science, and someone might even undergo a series of trials and errors to figure out the correct pathways and resources. In this article, you will find the most comprehensive roadmap to become a data-scientist.

Learning a programming language

The first step in the journey of learning Data-Science is to learn a programming language. The most popular and widely used programming language in this domain is Python programming language. It is easy to learn, has numerous inbuilt libraries and resources, and thus offers an immense output with lesser input in contrast to its counterpart, the R programming language, which is also used in this field. When learning Python, it is essential to primarily focus on loops, data types, how to define variables, etc.

It is further recommended to learn certain libraries in Python such as Numpy, and Panda, specifically because they are easy to learn, provide high performance, and can be used for analyzing numeric and tabular data, respectively.

Basic knowledge of a Database

One must also know the basic use of at least one database, e.g. MySQL, MongoDB, how to connect and use it with Python, and be able to store and retrieve data using the database.

Knowledge of Statistics/Statistical Mathematics

A large part of Data-Science comprises collecting, sorting, and organizing data, due to which statistical knowledge is crucial. However, one does not need to master statistical studies for this cause. To have a basic idea of 9th to 12th-grade statistics, including concepts like grouped and ungrouped distribution, mean, median, mode, Standard deviation, etc., and the ability to apply it to the mined data is more than enough.

Data Visualization

It is another imperative aspect of Data Science to be able to visualize massive chunks of data through charts, graphs, etc. This process can also be eased through the use of two other data visualization libraries in Python, namely Seaborn and Matplotlib, used to explore data to form statistical graphs.

Machine learning

It is one of the core parts of Data Science. Briefly, Machine learning is a sub-branch of Artificial-Intelligence, which aims to build machines and systems which can learn to imitate human behavior without specifically requiring to be programmed for the same. It also helps to effortlessly understand the acquired data and apply the necessary algorithms to it.

Basic knowledge of Linux and Git

Having a basic knowledge of the Linux Operating System and being able to work using it is necessary because most corporate companies today use Linux OS in their devices. In addition to Linux, it is important to learn Git as it allows a person to distribute codes, and it is especially helpful for data science teams or collaborators to effectively integrate, deploy and work toward their projects.

Implementing Projects

It is by far one of the most important points for aspiring Data Scientists. To be able to apply the knowledge of the concepts learned into a project is the best way to check one’s progress and ensure regular practice. Practice can also be ensured using certain associated websites like Kaggle, which provide community platforms for those passionate about Data Science and Machine-Learning.

Interview Preparation

The final and most underrated part of learning data science is interview preparation. Along with acquiring the knowledge required in the field of data science and being able to use it in projects, it is also highly important to prepare for interviews with our dream companies in which we aspire to the position of data scientist. Interview preparation includes thorough research about the company we are interested in, follow roadmap to become a data-scientist and brief research about the job position. Continuing along these lines, it is also a key skill to have good communication and negotiation skills.


In a nutshell, roadmap to become a data-scientist involves acquiring, preparing, through analysis through modeling, visualization, and interpretation to help discover useful information, and predict future decision-making. It is a very interesting field to venture into and learning Data-Science is no rocket science. One must know a programming language, preferably Python, learn to use some data science and machine learning-related libraries like Panda, Numpy, Seaborn, etc., know basic statistics, be able to visualize data, have a good hand at machine learning, and be able to implement projects.

However, it is of paramount importance that the learner is passionate about Data-Science and research and keeps using new and various resources and keeps interacting and engaging with fellow data science enthusiasts and co-learners, either physically or through online community platforms, and is dedicated to his/her goal. The learner must also be updated with the recent news in the world of technology and data science, keep track of trending Git repositories, and have one’s interest growing throughout the complete adventurous journey of learning the beautiful subject.

Data Science jobs are in high demand today and the salaries offered are highly lucrative and bankable. Thus, it is one of the great career options to explore and follow for anyone interested in Computer Science and programming.

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