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Career Transition From Data Analyst To Data Scientist

When you are watching YouTube, you can see so many advertisements coming up that encourage students to study data science and data analysis. You might have also noticed that on LinkedIn too many techs message you for taking up a course in data. As the world is concentrating on data, industries are reaching out to people who can handle data. Data scientists and data analysts both are in eminent positions. If you are changing your career from data analyst to data scientist here are possible methods to do so.

A Career Transition From Data Analyst to Data Scientist

Before we head on and see what needs to be done to get a perfect data scientist role, to be a successful data scientist, knowing about both spheres is important. Moving from data analyst to data scientist is not a per-contra job.

Being a data analyst first also helps the candidates to get on quickly with the data scientist role. The responsibilities are more and you need to learn more in the data scientist job position. Whereas data analysis is a part of the data scientist’s job.

Thus, changing the study track from data analyst to data scientist is a journey full of learning, experiences, and helping yourself well to understand better and take things in a better way.

Understanding Data Science and Data Analysis

Both data science and data analytics are like a flower of the same data tree, but still, it has some variances. These differences are their identification mark. If one wants to transition their career or move ahead in their career from data analyst to data scientist, they need to understand the subject well.

A projected understanding of how things will change and how to look upon things varies.  Let us hover over what data science and data analysis are all about.

It is beginning with data analysis first. Data analysis as the name suggests is the part of data culture that deals with the analysis or interpretation of data. It is quite known that data if not studied is like trash.

Data needs to be interpreted so that a correct insight is gained from it. The analysis of data helps in knowing the trends and patterns of various things.

 It provides the company with a report that helps the company to place itself high in the competitive market. Thus, data analysis is one of the most crucial steps in the data work process.

Coming to data science. Data science involves all the data activities right from collecting data to the final step, that is deployment of models. Data science is the process that ensures all the work of data is completed thoroughly so that the result gained is fruitful for the companies.

Data science helps in the perfection of the entire data life cycle process. The life cycle of data science involves all the aspects that are necessary for conducting the data evaluation and data deployment process.

The stages of the data science life cycle are:

  1. Data collection
  2. Data cleaning
  3. Data Analysis
  4. Model Building
  5. Model Deployment

These are the five steps of the data science process. Every data scientist should see to it that the entire process is done correctly and smoothly. All these small processes make data science.

The above explanations help us understand what data science and data analysis are involved with and what they mean. It is clearly understood that data science is the broader aspect.

Data science is the subject that includes all the major roles of data. Starting from data collection to cleaning and preparing the data for its analysis, model building, and deploying the data model all go through the data science process.

It is a larger aspect that offers a larger scope. Data analysis, on the contrary, is a narrow aspect. It is part of the entire data science project. Data analysis is the part that involves only the analysis or interpretation of data. Data analysts are required to provide a report that solves only the analysis portion.

They update the company about the ongoing trends and the patterns that are being followed. They share with the company the meaning of the data and the stories behind it and also help them know what should be done to place themselves in a better way to avoid the rush.

This was one of the essential elements to know before moving from data analyst to data scientist. Making a career transition sometimes seems easy, but knowing the consequences of it and what role needs to be played in the concerned subject are equally important to ponder upon.

Especially, in the recent time when jobs are very competitive and it takes a hard time to find a suitable job-changing career a grasp of the subject is pivotal. This opens up our next point of discussion is the difference between the job roles of the professionals.

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Data Analyst Vs Data Scientist

Till now we have checked that the work processes of data scientists and data analysts are entirely different. Data scientists have to look through the whole data life cycle process. Data analysts just have to make interpretations of the said data and present a report.

Data science is the bigger circle whereas data analysis is the circle inside the data science. So, a person willing to move to the bigger circle has to be prepared to take up many challenges and perform many roles.

The role of the data scientist varies from that of the data analyst. The steps involved in the life cycle of data analytics are data extraction, data preparation, data exploration, and data visualization and reporting.

This is how the data process of data analysis works. The role of the data analysts depends on their lifecycle. The lifecycle of data science is goal setting, data collection, data preparation, data analysis, model building, and deploying models.

The role of the data scientists depends on these aspects. They are responsible for the corroboration of the smooth functioning of the lifecycle.

To ensure that the data work is going on smoothly the data scientists need to understand the company and its functions well so that they can find a way to help with the data. They need to set a concrete goal that would help them to find a solution for the company’s desires.

Designing and maintaining the data integration system and data repositories is a part of the job role of data scientists. They are responsible for making the data governance policies.

Thus, they need to work with stakeholders directly. They also need to develop analytical models and languages. Data scientists ensure the data analysis is done properly so that the result can be achieved without much hesitation.

The data scientists prepare the results and present them to the management and the concerned team. They are also given the task of proposing solutions to the organization.

As the data scientists analyze the data and reveal to the management what the data has to say, they also need to tell them about what next action to take so that the organization can redeem or maintain the aura.

As we know data analytics is a part of data science, so the functions performed by the data analysts are in a narrow range. They are primarily responsible for data interpretation. They need to gather data and clean it to decode the trends and patterns that are hidden in the sets of data.

Their work involves working closely with IT to develop data governance policies. Data analysts are in charge of identifying the trends and patterns through data. So, they guide the companies by finding suitable methods to stay ahead.

They also play a part in analyzing the performance and creating dashboards and visualizations. They are also responsible for preparing KPI reports for the stakeholders. They are the ones who prepare the reports and communicate them to the concerned persons and team.

When you are preparing yourself to shift your career base from data analyst to data scientist, it is also necessary to know about the skillsets that are required for the role to be performed. The skills of data analysts and data scientists vary a lot.

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Data science is mostly about the technical part where a profound knowledge of computer science and programming is a complete necessity. Data analysts require statistical and mathematical skills.

They should also be a good communicator and good presenter. Technical skills are a definite requirement for data analysts. They incorporate skills like data integration and management, data modeling, R, SAS, SQL programming, statistical analysis, and reporting.

For effective and smooth processing of work, the data analysts use tools like Power BI, Tableau, Python, Excel, and others. So, data analyzers should be well-versed in using such tools.

On the other side, data scientists also have various skill sets that are an utmost essential for their job roles. As they have a larger scope, the skills are also more. They require managerial and technical skills.

They help in managing the organization’s data infrastructure. They need to communicate and collaborate with all teams throughout the organization. They need to have good statistical skills and good technical knowledge to carry on a smooth functioning of the work.

Data scientists are occupied with all the work of data starting from gathering data to building and deploying data models. This is the reason a data scientist should know all the functions that are essential to conduct data processing.

They also need to know the techniques and skills to use the tools that are required to efficiently process the work. Some of the tools used by them are R, SAS, Python, Apache Spark, TensorFlow, and others.

These are the basic know-how of the data function, roles, and responsibilities of the professional. One should know what roles each one has so that it will make them make a better decision of career transition from data analyst to data scientist.

In deciding the journey towards the career transition there are other essential factors also, that determine if one is suitable for the job.

No doubt people are hired based on experience and their work potential, but qualification also plays an important role. Below we have highlighted the qualifications of data analyst and data scientist.

Educational Qualification of Data Scientist and Data Analyst

Qualification of both a data scientist and a data analyst are essential. It is a deciding factor that will help in determining if one can transform their career in this field.

The data analyst requires an educational degree in subjects like data science, computer science, mathematics, statistics, economics, finance, and management information systems. A person desiring a role as a data analyst should have a degree in any of the above subjects.

Both bachelor’s degrees and master’s degrees can make the way for a stable career as a data scientist.  Bachelor’s degree in computer science, statistics, maths or science is an essential requirement.

Having a master’s degree in data science, computer science or a related field can help in achieving a data scientist career. To enhance the journey of making a career in data science people also opt for certification courses that make their resumes weigh heavily.

Some of the certification courses are SAS Certified Data Scientist, Open Certified Data Scientist (Open CDS), IBM Data Scientist Professional Certificate, Certified Analytics Professional (CAP), and many others.

These courses help the aspirers to learn more practical-oriented matters and help them perform better in the job. The educational qualifications of the data analysts and the data scientists are important. Without having proper knowledge about the subject people would not be able to perform better work.

How to Transit From Data Analyst to Data Scientist

Until this part of the article, we have checked what both fields of data deal with, what skills and knowledge they require, and the respective roles and qualifications required by both. Now comes the thought of what things we can do to build a career as a data scientist.

Working as a data analyst is already halfway through being a data scientist. Still, you need to become strong in many aspects that will help you get a better job as a data scientist.

Below are the following ideas that will help you get a career change.

  1. Taking up a certified data science course will prove beneficial for the ones trying to enter the data science zone. Take certification courses or crash courses that will give you more input on the subject and practical exposure.
  2. Earn a degree or diploma from a data science institute. The institute will not only help you to earn knowledge but will also help you to get better placements.
  3. Try to gain more knowledge and skills from your work environment, that will help you to cope with data science jobs.
  4. The person needs to understand the data science path well. They need to put in effort to learn and know what kind of professional attributes are expected from them.
  5. The person willing to join as a data scientist should also know the applicability of technical tools, they should see a lot about algorithms and coding and they should have updated knowledge about the data market.
  6. Build a data science job portfolio, showcasing your talents, skills, knowledge and your achievements.
  7. Network with people so that they can help you find a suitable data scientist job role in the organization.
  8. You search for data science positions in job portals. There are plenty of jobs available for data science positions. You can find a suitable job and apply for the position.

These are some of the ideas that will help you get a job in data science. The data science field is very competitive as chunks of aspirers are waiting to get a job in the data world. So, you should be prepared thoroughly for locking the job role.

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Benefits of a Data Science Career

It is pretty much certain that when you want to have a transition in your career, you have to think about the advantages that the field has in store for you. Data science is no doubt the buzzword in organizations.

Individuals are aiming to start their careers in data science. They are aspiring to build a career in data science. In the present genre, the world is becoming a data hub. Everything is dependent on data.

Knowing the trends and patterns is necessary for organizations to survive in this market. This is the reason why enterprises are also turning their heads towards data science. Hence a huge requirement for capable and eligible data scientists is soaring up.

This is one of the best benefits for a career in data science. There are plenty of job requirements in the domain. People can get jobs. Another benefit of the profile is that it offers a stable career.

As data science has become the most favorable job sector, it provides a stable and consistent career. People can experience good career growth. This draws more attention from the people who are interested in joining the data table.

Though the salary of the data scientists depends on experience, knowledge, skills, and location, they draw good compensation at the month’s end.

As per Glassdoor, the average amount of PayScale of a data scientist in India in 2024 is around INR 8 Lacs to INR 18 Lacs per year depending upon what functions the data scientist performs.

As data science is the need of the hour, it has become a versatile arena. All the industries need data science. So, data scientists can join any sector. They need not limit themselves to one particular sector.

Besides the career growth of data scientists is good and fast compared to many other jobs. Due to the growing demand and AI overpowering the world, data science careers are secured.

Individuals who have gained various technical skills including AI can expect a stable career in the domain. Another aspect that draws the attention of the individuals in this sector is flexibility.

People can expect to work flexibly. Also, the job is not the same as routine work. The data scientists come across new projects to handle that give them fresh exposure and learning experience.

These are the benefits of making a career as a data scientist. People are interested in joining the domain due to these reasons. For people who are interested in coding and numbers, data science offers a great scope for them.

Many top brands are in look out for efficient data scientists who can handle data and provide the company with a fruitful solution. Freelancing is also a great opportunity the data scientists can dig into.

The freelancing business has become one of the most preferable choices among people who have a good hold in the subject, can do the work all alone, and are confident to help the brands.


1. Is Data Science a Good Career Option for a Career?

Yes, it is the most sought-after job in the current era. It has a lot of scope and offers a firm career.

2. Are There Institutes That Offer Data Science Courses?

There are plenty of certified institutes that offer data science courses to help people to grow.

3. Which Industries Favour Data Scientists?

Healthcare, banking, finance, manufacturing, and plenty of others.

4. Will Data Science Provide a Stable Occupation?

Yes, the domain is a stable source of income.


While concluding the article, would mention that in today’s world finding a job is the utmost difficult thing. Though there are many jobs available in the domain we should also remember that the queue of deserving aspirants is many. The points discussed here will guide you in making a smooth transition in your career. Career selection depends on one’s choice alone but before moving you should know all the consequences that will earn you a profit.

Passion for writing contents, blogs and novels, has motivated Aritra Bose to pursue her career in content writing from IIM Skills. She is eager to explore her journey as content writer and work with an organization where her communication, creativity, adaptability, research work, SEO knowledge and organization skills are utilized and enhanced. Prior to this, Aritra gained 5+ years of experience in HR and Marketing profile, with KONE Elevators, HPE, GAVA Ecocrete and ICICI Bank. She has completed her MBA in HR and Marketing from KIIT University. Her hobbies are reading, content creating in YouTube, cooking and travelling.

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