What Does a Data Analyst Do? Your 2024 Career Guide

A data analyst collects, cleans, and interprets data sets to answer a question or solve a problem. They work in many industries, including business, finance, criminal justice, science, medicine, and government.

What kind of customers should a business target in its next ad campaign? What age group is most vulnerable to a particular disease? What patterns in behaviour are connected to financial fraud?

You might be pressed to answer these questions as a data analyst. Read on to learn more about what a data analyst is, what skills you'll need, and how to start on a path to becoming one.

What is data analysis?

Data analysis is the process of gleaning insights from data to inform better business decisions. Analysing data typically moves through five iterative phases:

• Identify the data you want to analyse

• Collect the data

• Clean the data in preparation for analysis

• Analyse the data

• Interpret the results of the analysis

Data analysis can take different forms, depending on the question you’re trying to answer. Briefly, descriptive analysis tells us what happened; diagnostic analysis tells us why it happened; predictive analytics forms projections about the future; and prescriptive analysis creates actionable advice on what actions to take.

Hear from experts in the field about what data analysis means to them.

Data analyst tasks and responsibilities

A data analyst is a person who gathers and interprets data to solve a specific problem. The role includes plenty of time spent with data but entails communicating findings too. 

Here’s what many data analysts do on a day-to-day basis:

• Gather data: Analysts often collect data themselves. This could include conducting surveys, tracking visitor characteristics on a company website, or buying data sets from data collection specialists.

• Clean data: Raw data might contain duplicates, errors, or outliers. Cleaning the data means maintaining the data quality in a spreadsheet or through a programming language so that your interpretations won’t be wrong or skewed. 

• Model data: This entails creating and designing the structures of a database. You might choose what data types to store and collect, establish how data categories are related, and work through how the data appears.

• Interpret data: Interpreting data will involve finding patterns or trends in data that can help you answer the question at hand.

• Present: Communicating the results of your findings will be a crucial part of your job. You create visualisations like charts and graphs, write reports, and present information to interested parties.

What tools do data analysts use?

Data analysts often use various tools to make their work more accurate and efficient during data analysis. Some common tools in the data analytics industry include:

• Microsoft Excel

• Google Sheets

• SQL

• Tableau

• R or Python

• SAS

• Microsoft Power BI

• Jupyter Notebooks

Data analyst salary and job outlook

The average base salary for a data analyst in India is ₹6,60,000 [1]. This can vary depending on your seniority, location in India, and other factors.

Data analysts are in high demand. It’s estimated that 97,000 data analyst jobs remain unfilled annually in India, and demand for these employees has created a 45 per cent increase in data analyst jobs on the Indian market [2]. 

Data analyst vs. data scientist: What's the difference?

Data analysts and data scientists both work with data, but what they do with it differs. Data analysts typically work with existing data to solve defined business problems. Data scientists build new algorithms and models to make predictions. Learn more about the difference between data scientists and data analysts.

Types of data analysts

As advancing technology has rapidly expanded the types and amount of information we can collect, knowing how to gather, sort, and analyse data has become a crucial part of almost any industry. You’ll find data analysts in the criminal justice, fashion, food, technology, business, environment, and public sectors—amongst many others.

People who perform data analysis might have other titles, such as:

• Medical and health care analyst

• Market research analyst

• Business analyst

• Business intelligence analyst

• Operations research analyst

How to become a data analyst

There’s more than one path toward a career as a data analyst. Whether you’re just graduating from school or looking to switch careers, the first step is often assessing your transferable skills and building the skills you’ll need in this new role. 

Data analyst technical skills

• Database tools:  Microsoft Excel and SQL should be mainstays in any data analyst’s toolbox. Whist Excel is ubiquitous across industries, SQL can handle larger sets of data, and experts consider it necessary for data analysis.

• Programming languages: Learning a statistical programming language like Python or R will let you handle large data sets and perform complex equations. Though Python and R are among the most common, it’s a good idea to look at several job descriptions of a position you’re interested in to determine which language will most benefit your industry. 

• Data visualisation: Presenting your findings clearly and compellingly is crucial to being a successful data analyst. Knowing how best to present information through charts and graphs will make sure colleagues, employers, and stakeholders will understand your work. Tableau, Jupyter Notebook, and Excel are some tools used to create visuals.

• Statistics and maths: Knowing the concepts behind what data tools are doing can help you tremendously in your work. A solid grasp of statistics and maths can help you determine which tools are best to use to solve a particular problem, help you catch errors in your data, and better understand the results.

If that seems like a lot, don't worry—you can find plenty of courses that will walk you through the basics of the technical skills you need as a data analyst.

Data analyst workplace skills

• Problem-solving: A data analyst needs to understand the question and the problem that needs to be solved. They also should be able to find patterns or trends that might reveal a story. Having critical-thinking skills will allow you to focus on the types of data, recognise the most revealing methods of analysis, and catch gaps in your work.

• Communication: Getting your ideas across to others will be crucial to your work as a data analyst. Strong written and speaking skills to communicate with colleagues and other stakeholders are good assets for data analysts.

• Industry knowledge: Knowing about the industry you work in—health care, business, finance, or otherwise—can give you an advantage in your work and job applications. If you’re trying to break into a specific industry, pay attention to the news in your industry, or read a book on the subject. This can familiarise you with the industry’s main issues and trends.

Paths to becoming a data analyst

Acquiring these skills is the first step to becoming a data analyst. Here are a few routes you can take to get them that are flexible enough to fit around your life.

• Professional certificate: Entry-level professional certificate programmes usually require no previous experience in the field. They can teach you basic skills like SQL or statistics while allowing you to create projects for your portfolio and provide real-time feedback on your work. 

• Bachelor's degree: If you’re considering getting a degree to become a data analyst, focusing your coursework in statistics, maths, or computer science can give you a head start with potential employers. In 2020, the Indian government allowed fully online degrees for the first time. Now the country has over 500 approved online degree programmes available [3].

• Self-study: If you want a path that doesn’t include formal training, you can independently learn the skills necessary for data analysis. You can build a portfolio to show prospective employers.

Data analyst career advancement

Being a data analyst can create opportunities to move into other careers, as many who start as data analysts go on to work as data scientists. Data scientists use statistics, maths, and computer science to analyse data like analysts. A scientist, however, might use advanced techniques to build models and other tools to provide insights into future trends. 

Get started today

If you’re ready to start exploring a career as a data analyst, build job-ready skills in less than six months with the Google Data Analytics Professional Certificate on Coursera. Learn how to clean, organise, analyse, visualise, and present data from data professionals at Google.  If you're ready to build on your existing data science skills to qualify for in-demand job titles like junior data scientist and data science analyst, consider the Google Advanced Data Analytics Professional Certificate.

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