Data Analyst Vs. Data Scientist (With Salaries And Tips)

Updated 30 September 2022

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Businesses often rely on data to identify trends and make informed decisions. Data analysts and data scientists work with data and carry out a variety of crucial duties that are central to various industries. While both of them share some similar responsibilities, they approach data and data techniques differently to accomplish their individual goals. In this article, we highlight the differences between a data analyst vs. data scientist, outline their responsibilities, explore their career paths and find out their average salaries.

Data analyst vs. data scientist

Understanding the differences between a data analyst vs. data scientist is helpful in identifying which career matches your interests, skill set and professional goals. Data analysts work mostly with structured data by collecting, analysing and mining techniques to provide valuable insight to businesses. In contrast, data scientists collect and analyse both structured and unstructured data and focus on building models to predict trends and make forecasts.

What does a data analyst do?

The role of the data analyst is to gather, analyse and interpret data for an organisation. They provide this data to others within the company who can use it to plan their own strategies and decisions. Their responsibilities may include:

assessing and choosing the appropriate data-gathering method according to business goals and requirements
performing data collection and checking the reliability of collected data
analysing and preparing reports based on the collected data
developing and implementing database systems
analysing patterns and trends in data
preparing and presenting information to managers, stakeholders and decision-makers
creating visual and written reports in response to data findings

What does a data scientist do?

Data scientists analyse large datasets and interpret the results to provide valuable insight. These professionals analyse data and use their knowledge of general mathematics, statistics and computer science to develop algorithms to recognise patterns and predict future outputs. Companies can use the information to gain a deeper understanding of their operations to make more informed business decisions. They build, train and deploy models for various other purposes, such as image classification, object detection, text-to-speech translation and reinforcement learning. Some other job responsibilities may include:

designing customised data solutions for companies to gain a better understanding of their customers
working with the management to develop business solutions based on company data
creating and customising procedures, tools and techniques for evaluating the effectiveness of the models
using hyper parameter tuning to improve the accuracy of models
creating and sharing graphs, reports, charts and dashboards to communicate insights to stakeholders
identifying business needs and developing strategies to address those needs
planning and coordinating research and analyses
focusing on research and development to train models for complex use-cases that can help businesses automate manual tasks and increase efficiency

Skills essential for a data analyst

These professionals play a crucial role in helping companies make strategic decisions. They help executives and managers in interpret complicated data. Some of their skills include:

knowledge of intermediate statistics and advanced mathematics to visualise data
expertise with Excel and SQL databases for slicing and dicing data
expertise in business intelligence tools for reporting
proficiency in statistical software such as Python, R and Statistics Analysis System (SAS)
analytical and organisational skills necessary to handle large amounts of data
data visualisation and presentation skills for presenting the findings effectively

Skills essential for a data scientist

Data scientists analyse the data, learn from it and create algorithms to predict future output, which may include techniques such as regression, classification or clustering to provide valuable insight. Some of their skills include:

knowledge of advance statistics and mathematics to analyse data and design models
proficiency in machine learning and deep learning to develop complex models
proficiency in using big data tools like Hadoop and Spark
expertise in SQL and NoSQL databases for data manipulation
experience with data visualisation tools like Power BI, QlikView and Tableau
expertise in programming languages like Python, R and Scala
excellent verbal and written communication skills to present and publish research findings

Other differences between data analysts and data scientists

While the role of a data scientist is mostly technical, the role of a data analyst requires more business expertise, although this can vary from company to company. In many organisations, analysts work in close collaboration with stakeholders and support business teams. With machine learning and artificial intelligence, data scientists can identify patterns in the data that are not obvious through traditional statistics and visualisation methods. Some other differences between the two professions include:

Education

In terms of education, the two professions differ as follows:

Data analyst

The minimum educational requirement for working as a data analyst is a bachelor's degree. You can also pursue a master's degree to enhance your understanding in this field. Examples of data analyst majors include:

Mathematics: This role requires the basics of problem-solving, mathematical modelling and data analysis. Students can take courses in discrete mathematics, differential equations and complex analysis as part of this program.
Statistics: A major in this area prepares students to gain a solid understanding of data analysis, statistics and probability. Several statistics programmes provide opportunities for students to specialise in data analysis, typically incorporating data processing, data analysis and statistical learning into their core curriculum.
Computer science: Among the subjects taught in this major are algorithms, data structures, operating systems, software engineering and computer architecture. This course can prepare students for high-level analysis and provide them with computer programming skills.

Data Scientist

Though there is no specialised path to becoming a data scientist, it is necessary to have a bachelor's degree, preferably in computer science. A master's degree in research or development can help you gain advanced knowledge and skills. Examples of data analyst majors include:

Computer science: This program teaches students how to build computing systems, build infrastructures, learn maths, logic basics, manage databases and develop computer programs.
Computer engineering: Students studying this major gain expertise in network architecture, cybersecurity and systems engineering. There is a general focus on computer programming, embedded systems and application development.
Information systems: This degree program allows students to gain knowledge and experience in designing, managing and leading computer and network systems. There are several common topics, including data modelling, database design, information security and management.

Salaries

The national average salary of a data analyst is ₹4,63,972 per year. Through experience, they can advance to levels such as senior-level analyst, analytics manager and director of analytics. The national average salary of a data scientist is ₹8,64,729 per year. Through experience, they can advance to levels that involve managerial roles or take up job roles, such as senior data scientist or AI engineer. These salaries can depend on several factors, such as the employer, location, educational qualifications, experience and skills.

Tips for choosing a profession

Currently, there is a high demand for data science and analytics. If you wish to learn how to work with data and identify patterns that can help make decisions, either of these options are appropriate. To decide whether a career in data science or analysis is a good fit for you, consider the following tips:

Consider your personal interests. An analytics career is better suited to those who are passionate about analysing data, have knowledge of data warehousing and advanced mathematics. The field of data science is a better choice for those interested in programming, machine learning and advanced statistics.
Research job descriptions. It might be helpful for you to look at the job descriptions for both types of roles to clarify the differences and see if you are interested in either of them. This can also assist you in gaining a better understanding of how roles differ for various industries.
Network with professionals. Establishing contacts with professionals in the field can be very valuable in gaining insights and self-confidence.
Compare your current skills. Considering your current skills and specialisations can help you decide which field best fits your interests. Learning relevant applications as you advance your skills can further facilitate your career growth.
Consider a certificate. Getting a professional certificate in your area of specialisation can help you showcase your skills to prospective employers. Further qualifications may also facilitate advancement to management or leadership positions at higher levels.
Assess your salary needs. It is also essential to consider your salary expectations when pursuing an advanced speciality. Knowing your earning potential can help you make the most of your investment in your chosen field.

Salary figures reflect data listed on Indeed Salaries at the time of writing. Salaries may vary depending on the hiring organisation and a candidate's experience, background and location.

Please note that none of the companies, institutions or organisations mentioned in this article are associated with Indeed.