Data Analyst Vs Data Scientist: A Detailed Comparison

What is a Data Analyst?

A data analyst is a professional who uses data to solve business problems. They collect, clean, and analyze data to identify trends, patterns, and insights. Data analysts use their findings to help businesses make better decisions.

What Does a Data Analyst Do?

Data Analysts in general deal with structured data to solve known or tangible business problems using various visualization tools, statistical analysis, and basic programming languages. They collect data to identify trends or monitor KPIs/metrics to help business managers understand and solve problems. For example, a Data Analyst can collect the sales data for a company and create a dashboard that can help the company to understand in which areas their sales are improving or declining, which product is working better, where they need to focus more, etc.

What is a Data Scientist?

A data scientist is a professional who uses their knowledge of mathematics, statistics, computer science, and domain expertise to extract insights from data. It is a more advanced role than a data analyst. Data scientists use their skills in statistics, machine learning, and programming to develop and implement data-driven solutions.

What Does a Data Scientist Do?

Data Scientists often deal with structured and unstructured data to solve problems that might be unknown. They employ advanced analytical techniques such as Machine Learning, Statistics, etc. to make predictions about the future using programming languages such as Python, R, etc. They spend a lot of time cleaning the data before performing exploration on it. This role can be considered a more advanced version of a Data Analyst. For example, a Data Scientist can collect sales data and model it using time-series forecasting methods which can help companies to plan the capacity and requirements in advance for a given product and location. This may help them optimize resource allocation in manufacturing and distribution. They can also help companies to improve marketing strategies by employing a consumer segmentation approach by analyzing consumers’ buying patterns, demographics, age, etc.

Key Difference Between Data Analyst and Data Scientist

The key difference between a data analyst and a data scientist is their focus. Data analysts typically focus on analyzing existing data to solve specific business problems. Data scientists, on the other hand, focus on developing and implementing data-driven solutions, such as predictive models and machine learning algorithms.

Data Scientists are focused on advanced data modeling and machine learning to predict future trends and make prescriptive recommendations, often requiring strong programming skills and advanced degrees. On the other hand, Data Analysts primarily focus on descriptive analytics, data cleaning, and visualization to provide insights into past and current trends.

Data Analyst vs Data Scientist – Education

Data Analysts typically have a bachelor’s degree in engineering, statistics, economics, etc., or a master’s degree in MBA. While Data Scientists generally have a master’s degree or Ph.D. in computer science, statistics, data science, economics, or closely related fields.

This has also been suggested by various reports. For example, in a survey by IBM in 2017, only 6% of the job postings for Data Analysts required them to have a master’s degree, while a study performed by Burtch Works suggested that 94% of Data Scientists have at least a master’s degree.

Though having a bachelor’s or master’s degree is not a mandatory requirement to build a career in both paths as long as you have the right set of technical skills required to become a Data Scientist or Data Analyst.

Data Analyst vs Data Scientist – Skills

Data Analysts and Data Scientists deal with the data, but their roles require different skills. There would be some overlap in skills for these two job profiles, but the main difference is that Data Scientists spend a lot of their time cleaning, processing the data, and modeling it by applying advanced analytical techniques using programming languages. Here is how both compare in terms of skills -

Data Analyst vs Data Scientist – Job Responsibilities

Data Analysts are responsible for applying statistical and visualization methods to prepare dashboards, charts, reports, etc. by collecting and processing the data using basic programming languages. They generally deal with structured data to solve known business problems. Their typical job responsibilities are -

• Data collection from structured databases such as SQL
• Analyze collected data using Excel and any other visualization approach to spot trends or monitor KPIs
• Create dashboards using various business intelligence software such as PowerBI, Tableau, etc.
• Communicate findings to the senior management to drive business decisions

Data Scientists are responsible for collecting and processing structured and unstructured data, cleaning and preparing them in a format that is usable and understandable. They apply advanced programming languages and tools to build and develop predictive or prescriptive models. Data Scientists must be able to deal with large amounts of structured and unstructured data to solve known or unknown business problems. Their typical job responsibilities include -

• Understanding business requirements and formulating them into data problems
• Collect structured or unstructured data using SQL, Web Scraping, ETL pipelines, etc.
• Data cleaning by discarding irrelevant information and handling NULL values
• Extensive data exploration using programming languages such as Python, R, etc.
• Developing predictive and prescriptive models using various machine learning or deep learning algorithms
• Communicate findings and recommendations to the business management

Data Analyst vs Data Scientist – Salary

In USA, the average salary for a Data Analyst range between 65K - 70K USD while a Senior Data Analyst earns around 97K USD on average. In India, a Data Analyst earns around 6 lacs per annum on average, while the average salary for a Senior Data Analyst is approximately 10 lacs per annum. These figures are based on the Glassdoor survey.

According to Glassdoor, in the USA a Data Scientist earns around 120K USD on average, and the average salary for Senior Data Scientist comes to around 145K USD. In India, as per Ambition Box, the average salary for a Data Scientist is around 10.5 LPA while a Senior Data Scientist earns around 20.5 LPA.

So both job profiles offer very promising and high-paying career paths across the world. Both jobs are currently in high demand, and this demand is expected to be there for the next decade as well. Now is the time to upskill yourself if you wish to make a career in any of the above profiles.

Data Analyst vs Data Scientist – Career Growth

If you want to build a career in a Data Analyst profile, you can learn the required skills and apply for an entry-level Data Analyst job where your major responsibilities would be querying databases and creating dashboards and reports to generate insights based on business requirements. As you gain more experience and skills, you can become a Senior Data Analyst or Analytics Manager, where you would be more involved in strategic decisions and apply more advanced analytics techniques. In another case, you can also upskill yourself by learning additional skills such as programming languages, Machine Learning, etc. to get into a Data Scientist profile as well.

In the Data Scientist profile, currently, there is a big skill gap across industries. There are many open positions where organizations are looking for Data Scientists who can leverage data to drive the decision-making process by applying various data science techniques. A Data Scientist can sharpen their skills and become a Senior Data scientist or Data Science Managers as well.

Both job profiles offer great career growth from technical as well as managerial points of view.

Data Analyst vs Data Scientist – Key Differences

We have discussed almost every aspect of the difference between these two profiles in the previous sections.

Here we summarize these differences and put them in a tabular format -

Choosing Between a Data Analytics and Data Science Career

Now you have firmly understood the differences between Data Analyst and Data Scientist’s responsibilities and skills requirements. This guide can help you evaluate which career path is the best fit for you. We have listed down three factors that you should consider while deciding your future career path

Educational Background

• Most Data Analysts holds bachelor’s degree, while in the case of Data Scientists, many organizations prefer having advanced degrees such as Master’s or Ph.D. in engineering, Data Science, or closely related fields. Though having an advanced degree is not a mandatory requirement for a Data Scientist profile as long as you have learned and mastered the skills required for the job of a Data Scientist.
• So, if you are an undergraduate student and don’t have any plan to pursue further education, Data Analyst might be a good fit for your career. Though, an undergraduate student can learn the required skills and decide to become a Data Scientist. If you have earned an advanced degree, have plans to pursue a master’s or Ph.D. in the future, or does not shy away from having further education, then you can also choose Data Scientist as your career path.

Personal Interests

• Data Analysts are passionate about numbers, data, and statistics, while Data Scientists require in-depth knowledge of computer science concepts and advanced statistical and analytical techniques. Data Scientists also need the acumen to understand the business world and its requirements.
• So, understanding which job profile best matches your personal interests will help you make informed decisions about choosing the best career path for you between Data Analyst and Data Scientist.

Salary and Career Growth

• Compensation for these two roles vary based on experience and skills. Data Analysts earn 6 LPA on average, while the mean salary of a Data Scientist is 10.5 LPA in India.
• From a career growth point of view, both job profiles offer many opportunities. Many Data Analysts upskill themselves by learning additional programming skills and advanced analytical techniques such as Machine Learning, Deep Learning, etc. to become Data scientists. There are many opportunities for Data Scientists to move to senior roles such as Data Science Manager, Data Architect, etc.
• So, identifying your desired salary and career trajectories of both job profiles may help you decide your right career path.

FAQ's

Q. Are data scientists and data analysts the same?

• No. There are some overlapping responsibilities for both roles, but they differ in how they work with the data. Data Analysts mostly deal with structured data to create reporting and dashboards to monitor metrics and KPIs by applying basic analytical techniques, while Data Scientists work with structured as well as unstructured data and apply advanced techniques such as Machine Learning, Deep Learning, etc. using programming languages to develop predictive models.

Q. Can data analysts become data scientists?

• Definitely Yes! Many Data Analysts upskill themselves by learning additional skills such as Machine Learning, Deep Learning, Programming Languages, etc., and become Data scientists.

Q. What is the difference between a data analyst and a data scientist?

• Data Analysts are responsible for applying basic statistical and visualization techniques to prepare dashboards, reports, etc. by processing structured data.
• Data Scientists are responsible for collecting and processing large amounts of structured and unstructured data, cleaning and preparing them in a format that makes the data usable and understandable. They apply advanced programming languages and tools to build and develop predictive or prescriptive models.

Q. Who earns more data scientist or data analyst?

• Data Scientist is one of the highest-paid jobs in the industry. In the USA, a Data Scientist earns on average 120K USD annually compared to 70K USD in the case of a Data Analyst.

Q. Which is a better data scientist or data analyst?

• Both jobs are currently in demand and offer promising and lucrative career paths. Drawing a comparison between the two varies from person to person as it is dependent on multiple factors such as their educational backgrounds, personal interests, etc.
• If you are an undergraduate student and want to start a career in analytics, then Data Analyst might be a better fit for you. While if you have the skills and aspirations to build advanced machine learning models using advanced programming languages, then Data Scientist may be a good fit for you.