From spreadsheets to AI: how to start and grow a career in data

Artificial intelligence (AI) is changing the data landscape. But the field of data analytics isn’t just weathering a rapid trend; AI is poised to drive significant economic growth in the coming years. AI – which is fundamentally driven by data – is expected to increase UK GDP by as much as 22% by 2030.

This statistic tells us something crucial: developing your data skills isn’t just about job security, it’s about positioning yourself at the forefront of an economic and labor market revolution.

We discuss these topics in the webinar: ‘From spreadsheets to AI: how to start and grow a career in data’. Made by the makers of the LSE Data Analytics Career AcceleratorThe event featured a panel of industry and recruitment experts who shared their insights on:

The evolving data career landscape
The skills you need to move from spreadsheets to advanced analytics
The value of specialized data education

We have summarized the key lessons and insights from the webinar below.

From Spreadsheets to AI How to Start and Grow a Career in Data

Accelerate your path to a fast-growing career in data analytics with a six-month intensive learning experience including expert-led AI masterclasses, from LSE and industry leaders.

There is a constant demand for data analysts

The technology industry has been evolving in recent years; both small and large businesses have experienced significant recessions and layoffs. As a result, many technical roles have seen a significant decrease in vacancies and demand. But roles like data scientists, data analysts, and data engineers have only decreased by about 15% since the end of 2022. To put this into perspective, let’s look at what this means:

Stability: While we have seen a slight decline, it is critical to note that this 15% decline is relatively small compared to the broader tech industry, which has seen larger recessions and layoffs.
Quick recovery: The article from Datalore notes that these figures have remained relatively stable since the beginning of 2023, indicating that the data labor market has quickly found its footing after the initial adjustment.
Ongoing question: This resilience underlines the continued importance of data skills across sectors. Companies recognize that data-driven decision making is not a luxury, but a necessity to remain competitive.

The data skills that are most needed

We asked our sole panelist Elodie Hudson, director of development at software company AssessTech and graduate of the LSE Data Analytics Career Accelerator, what skills she looks for in candidates:

“We are specifically looking for an advanced SQL data analyst. We are also looking for a data engineer, so someone who can build pipelines to create a data lake or data warehouse for us. There are many different types of jobs in the data sector, and for anyone making the transition there, it’s good to understand what that landscape looks like, what appeals to you and what you want to do.“

We see this need for programming and database skills reflected in the research on the key skills needed in data roles:

Programming skills: 86% of data scientists reported Python as their top programming language for current projects. This highlights the importance of Python skills in data science.
Database skills: SQL remains crucial and appears alongside Python in no less than 60% of all data vacancies. This underlines the continued importance of database management skills in data roles.
AI and machine learning: With the rapid growth in the number of AI engineering roles, skills in areas such as deep learning, natural language processing and computer vision are becoming increasingly valuable. Data analysts are also expected to understand a number of AI-powered tools to enhance their analytical capabilities.
Data visualization: Tools like Tableau and Power BI are essential for communicating insights effectively.
Cloud computing: Familiarity with cloud platforms such as AWS, Azure or Google Cloud is becoming increasingly important as more data operations move to the cloud.
Soft skills: According to Hays, communication and self-motivation are the two most important in-demand soft skills among employers at the moment. These skills are crucial for data professionals who need to explain complex insights to non-technical stakeholders.

Another panelist, Barabra Forbes, Analytics Manager at Birdie, confirmed that communication skills set candidates apart:

“What I really appreciate are people who can have a conversation with someone who is not a data-specific person. So they talk to a product manager, or they talk to an account manager, and they can come out of that conversation knowing exactly what data is going to be useful for that other person to do their job better.“

AI creates more data roles

According to the World Economic Forum, AI could result in the creation of more jobs. These range from newer specialist jobs, such as prompt engineer, to positions that are in higher demand, for example electrical engineers and people who work with data.

But the nature of data work is also changing.

Routine data tasks are becoming increasingly automated.
There is increasing emphasis on higher-level analysis and strategic decision-making.
The low-code market, which is closely linked to AI and data analytics, is expected to grow annually annual average rate of 22.9% from 2023 to 2030. Low-code platforms provide ready-to-use components for creating workflows and applications without manual coding, accessible to both business users and IT developers. This suggests an increasing demand for professionals who can work with low-code and no-code platforms to democratize data analysis.

Ollie Gower, the Vice President of Product at FINN, confirmed the low-code boom during the webinar:

“We focus a lot on no-code and low-code solutions. So we’ve actually created a role called an enterprise automation manager, and their specific job is to get out of code or on-premise solutions. If you haven’t already, give it a try. You can literally integrate it with another platform in about three minutes and then you’ll have those insights gathered in no time. For example, you have a lot of unstructured data and you want to see how often customers complain about price. This ranges from hourly or weekly tasks to just a few minutes.“

The most important thing to keep in mind when starting or advancing your data career

For career starters, hiring manager Cecilia Silvi recommended proof of your ongoing learning. Employers want to see your learning experience, especially training in key skills like SQL and data visualization.

For careerists, the ability to convey data insights to both leaders and junior data analysts is what Cecilia looks for when hiring.

We also had LSE Career Accelerator graduate Joel Hawkins join the panel, and he listed what he believes were the key factors in securing his new role as a data analyst:

Soft skills
The willingness to learn
Portfolio of work
A foundation of technical skills

Your skills path to data analysis

The main focus of the webinar was to reveal and understand the key skills that our expert panel of data leaders and recruiters were looking for in candidates.

The LSE Data Analytics Career Accelerator will teach you both the technical and soft skills identified by the webinar panel and our research. For six months you will:

Develop core data processing capabilities in databases and tools, and use SQL and Excel to identify insights through data analysis.
Build technical coding skills in the in-demand data programming languages Python and R.
Improve your communication skills, including data visualization, to ensure your analytics and insights support actionable business decisions.
Understand advanced analytics solutions to help you achieve business impact, and gain dedicated AI and data training in a series of in-depth masterclasses.

All these skills are applied to practical projects that later form a portfolio of work. For graduate Joel, this practical application helped him earn his PhD:

“I could see that the theory I learned actually applied to my work. That practical element allowed me to really make progress here and ultimately get the promotion.“

Elodie’s journey was slightly different. She had no previous coding experience and joined the program with the goal of making a complete career change from teaching to data. She achieved this goal within a few months of starting the LSE Career Accelerator, followed by two promotions, first as Lead Data Scientist and then as Director of Development (all within the space of 11 months). That’s possible read more about her story here.

“The course has given me so much more than I expected. The technical skills were fantastic, it was all new to me, but it was the career coaching that really helped me make that leap.“

Are you ready to boost your career at LSE?

The LSE Data Analytics Career Accelerator prepares you for a career, not just a job. You build the technical competencies, soft skills and business knowledge that employers are looking for. You’ll also receive one-on-one caregiver coaching to help you make that change or land that promotion and achieve lasting results in your career.

Read more about the content of the program and the AI Learning Track download the brochure here.

To understand how the Career Accelerator model works, visit our website.