Data Analyst vs Data Scientist (Qualification, Job Responsibility, Salary)

How much data has been generated in the world today?

Every day, 2.5 quintillion bytes worth of data is collected online. This staggering number is so vast that it is hard to wrap our heads around it. All we know is that a massive amount of priceless information is waiting to be decoded and analysed for future profits.

There are various methods to extract the value from this data,

One is to build the system and tools that allow proper structuring and analysis of the data, which is what a data scientist does.

The second is to analyse the data, which is the data analyst’s job.

Both professions are extremely important, yet have their fair share of challenges.

Over 90% of the world’s data has been collected in the last two years alone. As time goes on, data collection and analysis will get more intense and complicated. Data scientists and analysts have to work together to ensure they can create high-quality functioning data systems that help companies optimise their processes and improve productivity.

So which is the right job for you? Data analyst vs. Data Scientist? To understand this better, you need to understand the responsibilities, skills and differences between data analysts and data scientists.

Data analyst vs. Data Scientist — how to choose what is best for you

Let’s take a closer look at both job profiles so we can understand them better. Once you have all the facts, you can take a call on what role suits you best.

Data analyst vs. data scientist: Job Responsibilities

Data Analyst: Ajay is a data analyst working with HSBC Bank. He describes his primary job responsibility as creating insights that help senior management of his company make strategic decisions. He is required to review and analyse large data sets, record his findings and create presentations to support his analysis. Ajay looks at data to solve business issues. He converts raw data into useful information through careful analysis, enabling companies to make business decisions and solve problems.

Ajay’s day-to-day workload:

• Identify and analyse business and market trends using analytical tools and statistical methods.
• Create and maintain databases that methodologically organise information.
• Use automated tools to extract, review and correct data.
• Analyse patterns in various companies and industries.
• Prepare analytics reports for senior management and relevant teams.
• Collaborate with data scientists, engineers and programmers to improve data analytical methods.
• Work on exploratory data analysis (EDA) projects, which analyse and recognise patterns.

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Data Scientist: Richa is a data scientist working with Barclays. She is a competent specialist who uses her mathematical skills and problem-solving abilities regularly. Her job is to manage big data by creating and designing tools and systems for data modelling. She builds algorithms, predictive models and prototypes for data-centric services. Richa finds her work challenging and satisfying. It appeals to her to be at the forefront of data-centric services and to offer inventive results.

Richa’s day-to-day workload:

• Meets with a client or company who is facing certain issues within the business.
• Identifies the specific problem that is causing these issues.
• Work with data engineers to retrieve data sets connected with the business problem.
• Collaborate with the data analyst for insightful analysis of information.
• Build algorithms and predictive models.
• Offer solutions and strategic plans to the clients.

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Data analyst vs. data scientist: qualifications

Data Analyst: Ajay has an engineering degree along with a certificate in data analytics. Most data analysts tend to have degrees in maths, science or technology, but it is not mandatory. A certification in data-related course will help immensely, as you will learn about subjects such as programming, modelling and predictive analytics.

Ajay’s core skills:

• Knowledge of programming languages and systems such as R, Python, SQL, etc.
• Problem-solving and critical thinking abilities
• Good understanding of data mining techniques
• Awareness of new industry technology and advancements
• Strong communication skills

Also Read – Excel functions every Data Analyst must know

Data scientist: Richa holds a Master’s degree in computer science, which has given her a strong foundation in data science. She has also completed a data science course to ensure she has the necessary skills and tools in her possession. Most data scientists tend to own advanced degrees, However, even if you are a commerce student, you can still do a data science program and become a full-time professional in this field.

Richa’s core skills:

• Has advanced knowledge of data mining techniques like regression, text mining, generalised linear models, etc.
• Good understanding of statistical model building, decision tree learning, artificial neural networks and data architecture creation.
• Experience in coding languages such as SQL, R and Python
• Good presentation skills when offering recommendations and solutions to senior management or clients.

Also Read – Everything You Need to Know About Data Science Jobs

Data analyst vs. data scientist: Job opportunities and salaries

Data Analyst: Ajay has been in this industry for over four years. He has worked on some incredible projects and takes home an annual salary of Rs 6.5 lakhs. His salary is on the higher side as he works for a multinational bank in Mumbai. The numbers vary according to location and company, but here are the average yearly salaries for data analysts below:

• Entry level: Rs 2.5 lakhs
• Mid-level: Rs 5 lakhs
• Senior level: Rs 10 lakhs+

Source: payscale.com

Data scientist: Richa has been working at her job for almost five years. She commands a salary of Rs 8.4 lakhs a year as she has proven herself to be a reliable, competent data professional working with a big MNC.

• Entry level: Rs 3.5 lakhs
• Mid-level: Rs 9 lakhs
• Senior level: Rs 20 lakhs+

source: payscale.com

Differences between data analysts and data scientists

About Proschool’s courses in data analytics and data science

One of India’s leading institutes in analytics, IMS Proschool is famous for its comprehensive programs for data science, financial analytics and business analytics. The courses are taught by leaders in the finance and business fields. These experts offer engaging classroom sessions where students effectively learn through active methods and practical skill training. When you enrol for this course, you learn through various case studies and real-world projects. This deepens your application of concepts and theories, ensuring you learn real-life scenarios and not just textbook jargon.

Here are some of the benefits of learning with Proschool:

• The course covers all basic and advanced concepts, so you can join even if you don’t have a background in data.
• The syllabus includes relevant programming tools such as MS Excel, PowerBi, Python, SQL and Tableau.
• Your problem-solving skills are enhanced in this course.
• The programs are taught in a classroom setup at various centres across India.
• Flexible online sessions are also available.
• Proschool offers a placement program that ensures all certified students can access job openings on the portal.

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FAQs

What role is better: data analyst vs. data scientist?

Both profiles are equally challenging and rewarding. However, a data scientist is a more technical and slightly advanced designation compared to a data analyst. So choosing between the two depends on your set of skills, education and aptitude.

What is the difference between a data scientist’s and a data analyst’s salary in India?

Both credentials offer good remuneration. However, because data science is a more technical field, data scientists tend to make a slightly higher salary than data analysts.

Do data analyst and data scientist jobs require coding?

Yes. Both jobs require a certain amount of proficiency in systems like Python and R. Don’t worry, you don’t need to know very advanced coding to work in data-centric jobs.

In conclusion

In the battle of data analyst vs. data scientist, it is safe to say both profiles are winners. Big data-based services are in high demand and companies are willing to pay the price to ensure they have an efficient team of data professionals. The main difference between a data analyst and a data scientist is the technical skills and education. However, it is not unusual for data analysts to become data scientists later in their careers. Whatever path you choose, rest assured that this field offers amazing opportunities for your future.