What Does a Data Analyst Do? 2024 Career Guide

A data analyst collects, cleans, and interprets data sets to answer specific questions or solve problems. 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 vulnerable to a particular disease? What patterns in behaviour are connected to financial fraud?

You might be faced with questions just like these as a data analyst. Read on to find out more about what a data analyst is, what skills you'll need, and how you can start on a path to becoming one.

What is data analysis?

Data analysis requires working with data to get insights, meaning, and information that you can use to make informed decisions. Given the enormous volume and complexity of data available, having a process to follow makes it easier to get value from the data at hand. The iterative phases of data analysis typically include: 

• Identifying the problem/issue you’re solving

• Collecting the raw data needed to meet your goals

• Cleaning the data by getting rid of duplicates and standardising the structure in preparation for analysis

• Analysing data using varying tools and techniques to find trends and correlations

• Interpreting results to figure out if you answered the question or solved the problem

Data analysis can take various forms, including descriptive, diagnostic, and predictive. 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.

Data analyst tasks and responsibilities

As a data analyst, you’ll gather and interpret data to solve specific problems. 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. Methods include conducting surveys, tracking visitor characteristics on a company website, or buying datasets from data collection specialists.

• Clean data: Raw data might contain duplicates, errors, or outliers. Cleaning the data means maintaining the quality of data 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 types of data 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 will help you answer the question at hand.

• Report data: You’ll communicate the results of your findings to others through graphs, charts, and other visualisations. You’ll also write reports and present information to interested parties.

What tools do data analysts use?

During data analysis, analysts often use a wide variety of tools to make their work more accurate and efficient. Some of the most common tools in the data analytics industry include:

• SQL

• Python

• Microsoft Excel

• Google Sheets

• R

• Tableau

• SAS

• Microsoft Power BI

• Jupyter Notebooks

Data analyst salary and job outlook

The median salary for a data analyst in the UK is £37,920, according to Glassdoor [1]. Bear in mind that your data analyst salary might vary depending on your experience level, your location in the UK, and other factors.

As the amount of data continues to grow, the demand for data analysts remains strong across many sectors. 

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—among many others.

Data analysis may be integral to other job functions. Various positions include its use, including jobs like: 

• Intelligence analyst

• Market research analyst

• Business intelligence analyst

• Medical and health care analyst

• Operations research analyst

• Business analyst

How to become a data analyst

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

Data analyst technical skills

• Database tools: Two essential tools you’ll likely use as a data analyst include Microsoft Excel and SQL. Although Excel is useful across multiple jobs and industries, experts turn to SQL when they need to analyse larger data sets.  

• Programming languages: As a data analyst, you’ll be handling large data sets and performing complex equations, making learning Python, R, and other programming languages helpful. Python and R are popular programming languages, but you might review job descriptions for similar positions to determine the most useful language in the industry you’re considering. 

• Data visualisation: Presenting your findings in a clear and compelling way 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 among the many tools used to create visuals.

• Statistics and maths: As a data analyst, it's important to understand the concepts behind how data tools work. Having a solid grasp of statistics and maths will 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.

Data analyst workplace skills

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

• Communication: Getting your ideas across to other people will be crucial to your work as a data analyst. Strong writing 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—will give you an advantage in your work and job applications. If you’re trying to break into a specific industry, take some time to pay attention to industry news, or read a book on the subject. It 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 that are flexible enough to fit into your life.

• Professional certificate: Entry-level professional certificate programs 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. Several Professional Certificate programs on Coursera, including the Google Data Analytics Professional Certificate do just that.

• Undergraduate qualification: If you’re considering getting an undergraduate qualification to become a data analyst, focusing your coursework in statistics, maths, or computer science can give you a head start with potential employers. Many online programs have flexible scheduling so you can fit coursework around your priorities.

• Self-study: If you want a path that doesn’t include formal training, you can learn the skills necessary for data analysis on your own. Get started with a data analytics reading list for beginners, research portfolio-building opportunities, and start participating in data analytics projects.

Data analyst career advancement

Being a data analyst can also open doors to other careers. Many who start as data analysts go on to work as data scientists. Like analysts, data scientists use statistics, maths, and computer science to analyse data. 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. Once you finish, you can continue with Google's Advanced Data Analytics Professional Certificate to keep deepening your skill set.