When it comes to building a career in data science, what you know is important but being able to show what you can do with that knowledge is what really sets you apart.
That’s the driving force behind the Cambridge PACE Data Science With Machine Learning & AI Career Accelerator, delivered in collaboration with FourthRev.
This isn’t your typical bootcamp. It’s a career accelerator designed to help you translate advanced AI skills into business impact, through hands-on, portfolio-ready projects that prove your abilities in real-world contexts.
In this case study, you’ll discover how five learners used the programme to transform their learning into practical outcomes, from tackling challenges in finance to driving innovation in education and beyond. Their stories are proof that applied learning leads to real-world results, and that outcomes speak louder than credentials.
Learning that proves itself
The Data Science Career Accelerator is built on a core belief: learning makes the greatest impact when it’s applied to real-world challenges.
Over seven months, learners develop advanced techniques, from NLP and time-series forecasting to generative AI and large language models, guided by University of Cambridge academics and industry mentors from organisations including the Bank of England, PureGym, Study Group, and Inchcape.
Through 20+ portfolio projects and a final six-week Employer Project, learners produce work that speaks for itself: code notebooks, AI pipelines, dashboards, and business-ready reports.
By the end of the programme, every learner has something tangible to show and the confidence to back it up.
“It’s not just about algorithms or machine learning techniques; it’s about generating meaningful, actionable insights that can challenge conventional wisdom and enable commercial success.”
Dr Ali Al-Sherbaz, Academic Director for Digital Skills, University of Cambridge PACE
Sanya Setia: building a portfolio that opens doors
Sanya came to the programme looking for depth backed by institutional credibility.
“I decided to do a programme that was backed by a university that's well-renowned and also was long enough to go into the depths of the topic itself.”
As the weeks progressed, so did her portfolio. Each project laid the foundation for the next, building both her technical ability and her confidence.
“My achievements were my projects that I was doing week in and week out because I could always go back to them and see how I approached a problem and how I could solve it and make it fit into my new project.”
That iterative learning led to a portfolio spanning the full spectrum of machine learning: from foundational models to cutting-edge AI applications.
“This programme gave me an immense portfolio of projects on every machine learning topic that I have, and to put that on my GitHub, to put that on my website and to be able to showcase that to my new employers.
The outcome? Sanya landed a job immediately after completing the programme.
“I did get placed in a job right after my course, and I have to say a big part of it in terms of my CV and my confidence came from having done this course where I knew what I was talking about. I knew the data science field in general, and had a lot of confidence in myself.”
Sheldon Kemper: when data engineering meets AI
When Sheldon joined the Cambridge Data Science Career Accelerator, he wasn’t looking to pivot, he was looking to evolve. His goal? To bridge AI with data engineering, creating a role that blends intelligent automation with scalable infrastructure.
With a strong foundation in data pipelines and cloud platforms, Sheldon recognised that AI wasn’t just a bolt-on. It was reshaping how data systems deliver value. While self-study had taken him far, scaling AI solutions required a more structured, business-aligned approach.
“I quickly realised that building AI solutions required a different mindset than traditional data engineering.”
Through the Cambridge PACE programme, that mindset shift became tangible. Each project sharpened his ability to connect AI innovation with engineering rigour — turning theoretical knowledge into practical capability.
Portfolio highlight: Predicting student dropout rates with machine learning
- Goal: Help educational institutions identify at-risk students early.
- Approach: Compared XGBoost and neural networks using SHAP analysis to evaluate feature importance.
- Outcome: Achieved over 97% accuracy in identifying at-risk students, demonstrating the ability to translate technical work into social impact.
Towards the end of the programme, Sheldon was headhunted by Capgemini for a new role as a Data Engineer, with a salary increase and a more AI-driven focus
“Thinking back to my early journey into data science, I started by exploring theory and small-scale projects, but this programme helped me apply AI in a business-critical environment. Cambridge gave me the technical expertise, leadership experience, and problem-solving skills to bridge the gap between data engineering and AI-driven decision-making.”
Explore Sheldon’s full portfolio
Juan Pablo Salazar: from public health researcher to AI-driven analyst
When Juan joined the programme, he brought a background in public-health research, but not computer science. Initially, he worried about his lack of Python experience, but that concern quickly lessened with the help of ChatGPT and drawing on his transferable skills.
“I’m not an expert in Python, so that was challenging. But with ChatGPT, you can work around that if you understand the maths and statistics behind it…. if you know your maths and stats and understand what you're doing, you can complete projects without being a Python expert.”
Across his portfolio, Juan demonstrates how AI can drive smarter decision-making in regulated industries.
Portfolio highlight: AI-driven risk insights from public financial disclosures
- Goal: Explore how NLP and generative AI could support the Bank of England's supervisory monitoring of major banks.
- Approach: Developed a multi-stage pipeline extracting risk and sentiment signals from earnings calls and strategic reports, using FinBERT, BERTopic, and Phi-4.0.
- Outcome: A retrieval-augmented generation system surfacing sentiment drift and regulatory risk — replicating human analyst work with transparency and speed.
The Bank of England gave Juan strong feedback on his project: “This is an excellent submission demonstrating clear understanding of the regulatory problem and the potential of advanced NLP and generative models for supervisory monitoring.”
Sian Davies: detecting anomalies before they become failures
As part of the programme, Sian applied AI to a new and complex domain: ship engine performance. She explored how machine learning could detect subtle anomalies in mechanical systems, identifying early warning signs before failures occur.
In an industry where even minor deviations in engine behaviour can lead to costly downtime or serious safety risks, Sian’s project demonstrated the powerful role of applied data science in driving both operational efficiency and risk mitigation.
“Applying machine learning to this real-world anomaly detection problem was both fascinating and challenging, and underscored the practical value of the skills we were learning. It strongly highlighted the importance of careful assessment of the data…as well as considering the context of the problem to be addressed, i.e. what would be useful information for the client and how real-world confounding factors could impact the interpretation of results.”
Portfolio highlight: Anomaly detection for ship engine performance
- Goal: Build a machine learning system to identify abnormal engine behaviour and enable proactive maintenance before faults occur.
- Approach: Applied unsupervised learning method including Isolation Forest and One-Class SVM, to six key operational metrics: engine RPM, fuel and lubrication pressure, coolant temperature and pressure, and oil temperature. By analysing deviations in these signals, Sian’s model identified patterns linked to inefficiency or early-stage malfunction.
- Outcome: Delivered an intelligent anomaly-detection pipeline capable of flagging early warning signs in large-scale operational data, demonstrating how AI can translate raw performance data into actionable insights for safety and efficiency.
We asked Sian how building a portfolio impacted her readiness for the data science industry:
“Building a portfolio across multiple projects really helped grow my confidence in my own capabilities. Each project added depth to my understanding, as concepts learned separately were drawn together into a cohesive strategy for tackling real data science problems. These projects gave me invaluable experience in handling nuanced scenarios and messy real-world data, which I am carrying into my future work.”
Looking back on her work, Sian sees her portfolio as a record of growth and a clear direction for where she wants to take her career next.
“My portfolio is a great representation of my growth as a data scientist throughout the course, from the early projects focused on specific data science skills, through to the Employer Project with the Bank of England, which covered a wide range of skills and knowledge I'd gained, applied to a live business context.”
“The Employer Project allowed us to choose the direction of the work, reinforcing skills essential for industry work, such as initiative, technical acuity, and the ability to define a problem and communicate the solution effectively”
What current learners are working on
With a background in particle physics, Dr Arunima Bhattacharya was no stranger to complex datasets and advanced modelling. But she wanted more than theoretical rigour, she wanted to see her skills make a real-world impact.
That’s what led her to the Career Accelerator. Through the programme, she’s learning how to translate technical depth into solutions that matter in a business context, where data doesn’t just describe the world, but helps shape strategic decisions.
“Being part of the Career Accelerator has been genuinely transformative. Coming from a research background in particle physics, I was used to handling complex data and building models, but had little experience applying those skills beyond academia. This programme has shown me how my technical expertise can make a real impact on practical problems.”
Balancing research, learning, and career development, Arunima says she’s embracing both the challenge and the reward of applied learning.
“Above all, the programme has built my confidence and helped me grow not only as a scientist, but as someone who can bridge technology and human understanding in any context.”
Explore Arunima’s growing portfolio
Guided by mentors, built for the real world
Behind every project is a support team that makes learning personal. Weekly industry-led sessions bridge the gap between theory and practice, while bi-weekly 1:1 mentoring helps learners shape each project to meet real-world expectations and professional standards.
“Mentorship played a huge role in helping us stay on track and refine our approach. Regular feedback sessions made sure we weren't just building something for the sake of it, but aligning with real-world outcomes.” -Sheldon Kemper
From Dr Ali Al-Sherbaz's academic guidance to the expertise of industry mentors and tutors, this programme blends the University of Cambridge’s academic tradition with business-ready application.
Outcomes that speak louder than words
These learner stories show that graduates of the Career Accelerator don’t just walk away with a prestigious qualification, they leave with evidence of what they can do.
It’s that combination of credibility and capability that helps 88% of Career Accelerator graduates achieve their desired career goal within six months of completing the programme.
“For data engineers, [the programme is] the perfect way to expand into AI while keeping a strong engineering foundation. The experience of working on an industry-led project is invaluable, and the skills you gain, AI integration, retrieval-based models, and structured problem-solving, are exactly what companies are looking for.” - Sheldon Kemper
Your turn to build proof that gets noticed
Every learner’s story starts with a question: What could I achieve with the right structure, support, and tools?
The Cambridge PACE Data Science Career Accelerator gives you all three.
You’ll gain hands-on experience with the tools shaping the future of data science. You’ll apply your skills to real-world business challenges through industry-designed projects. And you’ll graduate with a portfolio that proves exactly what you can do.
So now the question is: what will you build?
To learn more about the Career Accelerator, download the programme brochure.
All portfolio projects featured in this case study were completed as part of the Cambridge PACE Data Science Career Accelerator. Learners’ personal portfolios may also include additional independent or prior work beyond the programme.