It’s been eleven years since Harvard Business Review famously labelled data science the “sexiest job of the 21st century.” The role was relatively new at the time and was most likely performed by a senior data analyst or analyst with a specific skill set. The article was inspired by the fact that, as more companies tried to make sense of big data, they realised they needed data whisperers—people who can ‘coax treasure out of messy, unstructured data.’

Fast forward to 2023, and the number and variety of roles in the field of data collection and analysis have grown and become more defined. Beyond data scientists, we now use titles such as data analysts, machine learning engineers, and data architects to more accurately describe the roles of people working with data in some form or another. 

Each year, there is increasing demand for people who can combine programming and analytics with expert visualisation skills—and data analysts are leading the pack. Statista reports that by 2025, man and machine’s activities will have created 181 zettabytes of data, so it’s no wonder The US Bureau of Labor Statistics estimates the demand for data analysts will see a 23% growth between 2021 and 2031 (5% higher than any other profession).

The good news is you don’t have to be a rocket scientist to become a data analyst. Anyone with an aptitude for mathematics, statistics and technology can enter the world of data analysis. Let’s take a closer look at some of the questions you might have if you’re looking to become a data analyst in 2023.

What does a data analyst do?

Data analysts draw on techniques from a range of disciplines— such as statistics, mathematics, and programming—to collect, organise and study data that provide businesses with valuable, actionable insights. They help business leaders find answers to complex questions and are in high demand at companies that make data-driven decisions.

A recent data analyst job posting by Jenrick Group called for someone who could ‘gather data, undertake data analysis and build dashboards to share quality visualisations and insights that the data reveals.’ The role would be central to identifying key innovation areas within the reinsurance industry.

The BBC also recently put up a job post for a data analyst who can mine various data sources—digital analytics data, the BBC’s own metadata systems and 3rd party datasets—to deliver insights that can drive the media giant’s customer engagement and strategy.

While data analysts can specialise in finance, healthcare, business, marketing or e-commerce, the basic responsibilities for the role are the same. These include:

• Extracting and wrangling (cleaning) data from various databases and sources 
• Analysing and interpreting data results using statistical techniques
• Creating data dashboards that can be accessed by all departments
• Monitoring and auditing data quality
• Automating tasks and streamlining processes

What’s the difference between a data analyst, data scientist and other data roles?

One way to think about the difference in data roles is whether they act before or after the data is collected. Most of the work data engineers and data architects do is performed before the data is collected, whereas data analysts and data scientists are responsible for everything that happens after the data is collected. 

Simply put, data analysts are explorers who investigate data from multiple angles to draw insights about things that have happened in the past. Data scientists, on the other hand, use raw data, statistics, and deep learning to create predictions and analyse opportunities.

Where does a data analyst fit into the business structure?

In some organisations, data analytics processes are highly centralised, and you’ll have a single data team of engineers, analysts and architects serving the entire company. Other businesses will take a more decentralised approach, where each business unit, such as sales, operations or supply chain, will have access to its own data professionals, resources, and processes.

While team structure depends on a company’s size, its goals and how it leverages data, most data teams or individuals will work cross-departmentally with company management and the IT department. Ultimately, a data analyst must have enough technical acumen to be a critical bridge between business and IT, but when it comes to reporting, you’ll be closer to the business side, where you’ll collaborate with either the product manager or the company’s COO.

What salary can you expect as a data analyst?

According to Glassdoor, in London (where pay is normally a little higher), a data analyst can expect to earn around £42,665 per year. Considering benefits such as a company pension scheme, medical insurance, bonuses, profit sharing, etc., you could see an additional £3,000 per year added on top of your yearly salary.

According to Prospects, starting salaries hover around £23,000, while experienced analysts and those in high-level and consulting data analytics jobs can command £60,000 a year or more.

How can I move into a data analytics role?

There are several ways to move into the data analytics field.

If you’re looking for a reputable online programme that has seen several successful career outcomes, consider the LSE Data Analytics Career Accelerator. Co-designed by faculty from LSE and industry leaders from companies like Tableau and GitHub, the six-month online programme will equip you with the technical, business and human skills you need to secure a data analyst role.

Everything you learn is highly practical, and there are no exams. Instead, you complete an Employer Project at the end of the programme, which has been designed to give you the opportunity to work on a real-world problem set by an employer.

To register for the programme, you’ll need:

• Previous education, such as a Bachelor’s Degree in mathematics, computer programming, economics, engineering, research, statistics or a similar field that emphasises statistical and analytical skills OR
• Three years of professional experience with knowledge of Excel. Experience in using Pivot tables is highly advantageous

If you have neither of the above, our admissions team will do an aptitude test to determine how strong your math and statistics skills are. You can hear more about the admissions criteria from our Enrolments team here.

How is the LSE Data Analytics Career Accelerator structured?

After an Orientation period, where you meet your Success team and get comfortable with the online environment, the LSE Data Analytics Career Accelerator is comprised of three six-week courses, which all culminate in an assignment that you can include in a portfolio of evidence that showcases your skills. The courses you’ll work through are:

1) Data analytics for business

2) Data analytics using Python

3) Advanced analytics for organisational impact

In between each course, you’ll take part in a Reflection week, whereby you’ll have time to meet with your dedicated Success Manager, take stock of your progress and plan what comes next. You’ll also spend time with your Career Coach, who will help you set short- and long-term career goals to ensure your investment continues to serve you into the future.

Finally, you’ll complete the Employer Project, where you’ll work with a group to apply your learning to a live data set and solve a unique business challenge set by an Employer Partner.

What sets this Career Accelerator apart from other programmes?

• It’s shorter than a traditional Masters programme (six months online, 15–20 hours learning time per week)
• You’ll have access to a Career Coach and Success Manager who will help you achieve your goals
• You’ll create a digital portfolio of evidence to showcase your skills
• You don’t learn stuff you’ll never use
• You’ll gain industry experience through the Employer Project
• You’ll have access to a recruiter network 
• You’ll get assistance with everything you need to make a career move, including your personal brand statement, CV and LinkedIn profile

Being agile and working with industry partners means the content keeps up with constant innovations in the industry, including changing platforms, tools, trends and business needs.  

The holistic career-focused approach to learning will also give you the crucial mix of technical and human skills that recruiters and HR managers are looking for. On a technical level, you’ll learn advanced Excel, SQL, Postgres, Python, R, and Tableau.

On a soft skill level, you’ll develop strong verbal and written communication, the ability to apply your analytical mind and wire it for problem-solving and the skills needed to collaborate with others as part of a team.  

You can hear more from previous learners about their experience of the programme and how it helped enhance their employability here.

Launch your career as a data analyst

Ready to dive into the world of data analytics?

Book a call with an Enrolment Advisor to learn more about the LSE Data Analytics Career Accelerator and whether it’s the right fit for you and your goals.