Becoming skilled in data doesn’t necessarily mean becoming a data analyst.
Data analytics is a rare skill set that can be applied to every digital business, in almost every industry and sector. It applies to engineering, finance, agriculture, fashion, retail and marketing, just to name a few. Each industry (and company) has unique data needs and roles; there are trend market analysts in fashion and marketing and data and AI engineers in software and retail.
In this blog, we explore the state of the data industry and the diverse career opportunities available once you have a foundation in data analytics.
The state of the data industry
Specialist recruiter Robert Walters’ The Future of Data report shows that over 80% of all tech vacancies in the UK are for either data analysts, data engineers, or data scientists.
The industries most hiring data pros are tech, media and telecom (41%), banking (15%), professional services (9%), and insurance (9%). Others actively hiring data specialists are retail, the public sector, healthcare, real estate, energy, and industrials.
Salaries for data analysts range between £27,000 and £71,000, increasing significantly for those who specialise or advance into senior data science or machine learning (ML) engineering positions. For example, the top salary for a London-based data engineer is £80,000, and for a quantitative analyst, it’s £152,000 per year.
Data analysts are in demand, and businesses are struggling to fill data roles. According to the UK Parliament, a recent study found around 178,000 unfilled data specialist roles in the UK.
Employees have the upper hand in this industry; the skills gap means that professionals with data skills are highly sought after (with companies willing to pay for such talent). But, this ‘gap’ and opportunity won’t last for long: to fill positions companies are prioritising internal data upskilling, and data analysis is becoming an expected skill for many.
Experience multiple careers with data analysis skills
Gaining data analysis skills provides an excellent foundation for many careers.
Here’s a summary of the key skills we teach in the LSE Data Analytics Career Accelerator – the same skills you’ll need wherever your data career takes you:
- You’ll build coding skills in the high-demand data programming languages Python and R, and you’ll practise their application in practical data projects that focus on real-world business scenarios.
- You’ll learn to speak the language of a business to tell compelling data-driven stories that lead to actionable business decisions.
- You’ll use Tableua as you learn data visualisation techniques and create reports to present insights.
- You’ll learn about ethical and regulatory frameworks, and identifying algorithmic bias.
Data analytics career opportunities
For data analysts, career growth is not always linear. There are multiple possibilities for career growth and diversity, and with experience and additional certifications, you can move to higher or specialised positions. That said, there are three general trajectories most analysts take:
- From junior analyst upwards. Move from a junior data analyst role into a senior one, ending in a company leadership position, such as chief data officer.
- Focus on advanced data analytics. You can upskill to move into pure data positions, such as data science, data engineering, data architecture, or ML.
- Data and industry specialist. Take the specialist route, honing your data analysis expertise in a field such as business, finance, research or marketing.
Let’s look at the most prominent data-based roles and the skills you’ll need – over and above data analytics – to enter these fields.
Business data analyst
Not to be confused with business analysts who look at a company’s IT processes, organisational structures, and financial reporting, a business data analyst investigates and analyses a business’ performance using data and statistical analysis. You’ll create insightful reports that are used to, for example, improve products, marketing, or customer experience. A background in IT or business admin or studies will help secure this position.
Financial analyst
You’ll need good data and market research abilities alongside interpretation skills. Collaborating with management, report presenting, developing financial models, and providing financial forecasts are all part of the required skillset. A qualification or background in finance, economics, or maths is normally required.
Logistics analyst
You’ll collect, interpret, and analyse various logistics data related to product supply chain management, sourcing, and distribution. You’ll use this data to understand, predict, or control logistics operations and processes. Prior knowledge of logistics or finance is ideal, but not essential.
Market research analyst
You’ll source and use data to analyse market trends, perform experimental research design and – at an advanced level – use ML to generate new ideas. You’ll employ attribution modelling and do forecasting and trends reports. An understanding of data and research is essential to enter this field.
Marketing analyst
A marketing analyst uses data to identify the target audience and monitors the performance and cost of paid online advertising. They experiment with pricing and product improvements through marketing tactics, and guide the marketing strategies and campaigns on various media channels.
You can learn about digital marketing strategies and performance management on the LSE Digital Marketing Strategy & Analytics Career Accelerator. It’s designed to give you data-driven digital marketing expertise that advances you into a leadership role. Learn more about the programme here.
Quantitative analyst
Also known as “quants”, quantitative analysts use data to forecast changes in the valuation of financial instruments (e.g. bonds and stocks) and develop complex mathematical models that reduce risk and generate profits. You’ll need to handle pressure well and have an advanced maths, economics, finance, or statistics qualification, along with your data skills.
Machine learning engineer
You’ll use large amounts of data to build models that can predict future outcomes or events, and develop, optimise, maintain, and train algorithms to solve data-based problems. Skills include understanding probability theory, statistics, and computer science fundamentals like data structures and algorithms. This is a highly-specialised field; experience as a software engineer, and/or a maths, data science, or computer science qualification are required.
Data scientist
You’ll be involved with designing data modelling processes, creating algorithms, and finding new ways of capturing and analysing data. Transitioning from analyst to scientist typically involves advancing your programming skills and studying ML. While a degree in data or computer science remains useful, more hands-on, practical programmes are now being favoured (by both employers and professionals).
The new Data Science Career Accelerator from the University of Cambridge Institute of Continuing Education is a seven-month programme that features over 20+ practical industry projects, and is focused on applying data science skills to real-world business needs. Find out more today.
Data architect
You’ll design and manage a business’ overall data architecture and define data flows, models, and storage systems to support business objectives. You’ll work with business analysts, data engineers, and data scientists to develop data integration and warehousing strategies. You’ll need solid qualifications and experience in business intelligence, application architecture, and/or network management to reach this senior position.
Data engineer
You’ll create and manage data pipelines from various sources and ensure data quality, security, and scalability. You’ll be involved in managing metadata and implementing appropriate data governance practices while ensuring compliance with regulatory requirements. A background or qualification in math or science, along with data analysis skills and experience handling big data sets, will be required.
This is where to start
We’ve just scratched the surface regarding where data analysis skills can take you in your career. New data specialisations are consistently emerging, making this one of the most relevant and long-term career paths to explore.
So, where do you start building this toolkit?
The Data Analytics Career Accelerator from The London School of Economics and Political Science (LSE) will introduce you to the core concepts of data analysis. You’ll build coding skills in high-demand programming languages, and practise your skills in practical projects focusing on real-world business scenarios. You’ll also work with a dedicated Career Coach to help you decide where to go in your career.
