From Spreadsheets to AI: How To Start & Grow a Career in Data

Artificial intelligence (AI) is changing the data landscape. But the field of data analytics isn’t just weathering a quick trend – AI is poised to drive significant economic growth in the years to come. AI – which is fundamentally driven by data – is projected to increase UK GDP by up to 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 job market revolution.

We discuss these topics in the webinar: “From Spreadsheets to AI: How to Start & Grow a Career in Data”. Brought to you by the creators of the LSE Data Analytics Career Accelerator, this event featured a panel of industry and recruitment experts who shared their insights on:

• The evolving data career landscape
• The skills you need to go from spreadsheets to advanced analytics
• The value of specialised data education

We’ve summarised the key learnings and insights from the webinar below.

 

There’s an ongoing demand for data analysts

The tech industry has been in flux for the last few years; both small and large companies have experienced significant downturns and layoffs. As a result, many tech roles have seen a significant decrease in job postings and demand. But roles like data scientists, data analysts and data engineers have decreased by only about 15% since the end of 2022. To put this into perspective, let’s consider what it means:

1. Stability: While we’ve seen a slight dip, it’s crucial to note that this 15% decrease is relatively small compared to the broader tech industry, which has experienced more significant downturns and layoffs.
2. Quick recovery: The article from Datalore notes that these numbers have remained quite stable since the beginning of 2023, suggesting that the data job market quickly found its footing after the initial adjustment.
3. Ongoing demand: This resilience underscores the continued importance of data skills across various industries. Companies recognise that data-driven decision-making is not a luxury, but a necessity for remaining competitive.

The data skills most needed

We asked our one panellist Elodie Hudson, who is the Director of Development at software company AssessTech and an LSE Data Analytics Career Accelerator graduate, what skills she’s looking for in candidates:

 

We see this need for programming and database skills reflected in the research of the most important skills needed in data roles:

• Programming skills: 86% of data scientists reported Python as their main programming language for current projects. This highlights the importance of Python skills in the data science field.
• Database skills: SQL remains crucial, appearing in up to 60% of all data job posts alongside Python. This underscores the continued importance of database management skills in data roles.
• AI and machine learning: With the rapid growth in AI engineer roles, skills in areas like deep learning, natural language processing and computer vision are becoming increasingly valuable. Data analysts are also expected to understand a few AI-powered tools to enhance their analytical capabilities.
• Data visualisation: Tools like Tableau and Power BI are essential for communicating insights effectively.
• Cloud computing: Familiarity with cloud platforms like AWS, Azure or Google Cloud is increasingly important as more data operations move to the cloud.
• Soft skills: According to Hays, communication and self-motivation are the two most in-demand soft skills among employers right now. These skills are crucial for data professionals who need to explain complex insights to non-technical stakeholders.

Another panellist, Barabra Forbes, who is the Analytics Manager at Birdie, confirmed that communication skills are what sets candidates apart:

 

AI is creating 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 roles in higher demand – for example, electrical engineers and those who work with data.

But, the nature of data work is also changing.

• Routine data tasks are increasingly being automated.
• There’s a growing emphasis on higher-level analysis and strategic decision-making.
• The low-code market, which is closely tied to AI and data analysis, is forecast to grow at an annual average rate of 22.9% from 2023 to 2030. Low-code platforms offer pre-built 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:

 

The most important thing to focus on when starting or advancing your data career

For career starters, hiring manager Cecilia Silvi recommended proof of your continuous learning. Employers want to see your learning experience, particularly training in key skills such as SQL and data visualisation.

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

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

1. Soft skills
2. The willingness to learn
3. Portfolio of work
4. A foundation of technical skills

 

Your skills pathway to data analytics

The main focus of the webinar was revealing and understanding the key skills our expert panel of data leaders and recruiters were looking for in candidates.

The LSE Data Analytics Career Accelerator teaches you both the technical and soft skills that were identified by the webinar panel and our research. Over six months, you’ll:

1. Develop core data-handling capabilities across databases and tools, and use SQL and Excel to identify insights through data analysis.
2. Build technical coding skills in high-demand data programming languages Python and R.
3. Hone your communication skills, including data visualisation, to ensure your analysis and insights support actionable business decisions.
4. Understand advanced analytics solutions to help you achieve business impact, and do dedicated AI and data training in a series of in-depth Masterclasses.

All of these skills are applied to practical projects which later form a portfolio of work. For graduate Joel, that practical application is what helped him achieve his promotion:

 

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

 

Are you ready to fast-track your career with LSE?

The LSE Data Analytics Career Accelerator equips you for a career, not just a task. You’ll build the technical competencies, soft skills and business knowledge that employers look for. You’ll also receive one-on-one carer coaching, to help you make that change or land that promotion, and get ongoing results in your career.

Learn more about the programme content and AI Learning Track by downloading the brochure here.

If you’d like to understand how the Career Accelerator model works, visit our website.

 

This article was published with permission from our partners FourthRev.