How Much Do Data Analysts Make? (Plus How to Become One)

Updated 26 January 2023

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The demand for data analysts has been seeing a constant increase over the past few years and it has given rise to numerous opportunities for data analysts. Many students have started pursuing data analytics courses, but there is a need for quality talent in this field. Learning more about the different types of data analyst jobs and salaries can help you choose the right path as a data analyst.

In this article, we discuss the average salary of a data analyst, the different types of data analyst jobs, the skills you may need to become one and whether it is a good career choice.

How much do data analysts make?

The answer to "How much do data analysts make?" is that it largely depends on the organisation they work for and the years of experience they have. The average salary of data analysts is ₹ 32,042 per month. Some people begin their career as data analytics interns, where the average salary of a data analytics intern is ₹13,842 per month. As you gain experience, the average salary of a senior data analyst can go up to ₹8,23,097 per year.

Different types of data analytics jobs that are in high demand

Apart from becoming a data analyst, you can also choose to take up other jobs that need data analytics skills. Some other data analyst jobs include:

1. Business intelligence analyst

National average salary: ₹8,29,056 per year

Primary duties: Business analysts use data visualisation, data modelling and data analytics techniques to identify trends that can help departments within the organisation to optimize their business processes. They use different business intelligence software for mining complex data and comparing available data with competitors or industry leaders. Most often, business analysts work across the organisation rather than just working with a particular department.

2. Data scientist

National average salary: ₹8,47,747per year

Primary duties: People often use the terms data scientist and data analyst interchangeably, but they are both quite different. While data analysts deal with existing data, data scientists work on gathering data and making them usable for data analysts. They analyze, model and process data and often also create algorithms and predictive models that can deal with structured and unstructured data sets.

3. Data engineer

National average salary: ₹10,56,981 per year

Primary duties: Data engineers collect, manage and convert raw unstructured data into structured data that data analysts interpret. They are also responsible for maintaining their organisation's data infrastructures like databases and data pipelines. Data engineers often collaborate with data scientists and analysts on projects. They even develop algorithms that help in making raw, unstructured data usable by organisations.

4. Quantitative analyst

National average salary: ₹22,895 per month

Primary duties: A quantitative analyst uses quantitative methods to help businesses make strategic and financial decisions. They extensively research market strategies and trends and develop complex mathematical models that are then used by financial firms. Along with mathematical and statistical knowledge, having quantitative programming and financial research skills are important for quantitative analysts.

5. Operations analyst

National average salary: ₹5,29,299 per year

Primary duties: Operations analysts help companies identify operational problems and implement strategic solutions. They use several tools and software for statistical modelling, data validation and data analysis. Since they primarily collect a lot of raw data and do thorough research of companies, they are often referred to as operations research analysts.

What are the skills needed to become a data analyst?

There are several technical and soft skills that are needed to become a data analyst. Some of these skills include:

SQL

Also known as Structured Query Language, SQL is the most common industry-standard database language that lets analysts access, read and manipulate databases and retrieve data from them. Since data analysts deal with large sets of data, learning this skill is very important. A majority of organisations need people who know SQL since there is so much useful data available.

R and Python

While having extensive coding knowledge is not mandatory to become a data analyst, knowing the programming languages R and Python are quite important. While SQL helps you to work with databases, R and Python help you clean, analyze and visualize the data. Both these languages are easy to learn and you can easily learn Python if you have some knowledge of Java or C++.

The choice of the language depends on your interests and career goals. If you want to work with artificial intelligence, big data and deep learning algorithms, Python is a good choice. If your interest lies in statistics and data visualisation, R is the better option.

Data visualisation

One of the primary tasks of a data analyst is to make available data visually appealing through graphs, charts and diagrams. Through this skill, data analysts help decision-makers get a visual view of complex problems, identify trends and gain data-driven insights. Having this skill can help you tell appealing stories about your data so that you can convey the findings of your data while engaging an audience. Organisations pay special importance to this skill as not all decision-makers are good with numbers and a visual representation of data can help them interpret better.

Data cleaning

Most of the data that is available is in the raw form and is unstructured. To make use of this data, the first step is cleaning the data, which makes this skill one of the most essential technical skills. Often, data analysts combine multiple datasets into one large dataset, and there may be inconsistencies and errors. Data cleaning also involves removing these errors so that the analysts themselves and other team members can use the data. Before modelling or analyzing any data, all the data has to be cleaned.

Machine learning

Even though machine learning is not a skill, bearing knowledge in machine learning can help recruiters to notice you during their selection process. To learn machine learning, good statistical programming skills are important. If you become proficient in this area, you may even start building machine learning models.

Subject knowledge

To become a data analyst, mathematics, statistics and knowledge of economics are also very important. Having a strong foundation in maths is necessary because data analysis is all about numbers. Being good at maths can help you deal with large data sets, identify patterns, process data and more. Knowing statistics is important for formulas and understanding the relationship between numbers and datasets. Even though you may not understand the direct relationship between data analytics and economics, the study of economics can help you interpret data, identify solutions to data-related problems and generate data-driven insights.

Microsoft Excel

Microsoft Excel is a powerful tool and even though you may think of it as a spreadsheet, there is a lot more that it can help you with. Although data analysts use different tools, being proficient at Microsoft Excel can help you process smaller datasets and quickly analyze them. Even if you have never used Excel before, there are a lot of online resources and guides which can help you learn this tool.

Critical thinking

Critical thinking skills are very important for data analysts, as they often analyze lots of data carefully, connect different pieces of information and extract insights from available data. Having critical thinking skills can help you think analytically and apply yourself to solving complex problems. You can develop this skill by continuously challenging yourself and taking up critical thinking exercises.

Communication

Often, data analysts explain complex data-oriented topics to people with less understanding of data as a part of their job. Having excellent communication skills can help you convey your findings and help decision-makers get data-driven insights so they can solve problems effectively. Also, you may work with other departments within the company and collect data from each of them. Having excellent communication skills can make the process very steady.

Is data analyst a good career choice?

Data analyst is becoming one of the highest-demand jobs because there is so much data available and more data is being created every day. There is a need for quality data analysts to process this data, make them usable and derive data-driven insights. Since there is a high demand for qualified data analysts, companies are willing to pay lucrative salaries to data analysts. Even as a junior data analyst, the salary is above the average of other fresher jobs.

As a data analyst, you can be a part of large teams and work across the organisation. So, it also provides excellent networking opportunities. Depending upon the company that you work for, there may also be the opportunity to travel for work. There is ample scope of learning and growth, which is ideal for professionals who enjoy working in fast-paced, challenging environments.

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, academic background and location. Please note that none of the companies mentioned in this article are affiliated with Indeed.