Start Date
Start date: 7 October 2024
Accepting late registrations until 21 October
Time commitment
20 hours per week, 7 months online learning (plus break weeks)
Support
One-to-one career coaching plus bi-weekly mentoring with an industry expert
Portfolio development
Work on 20+ industry-relevant projects and assignments, including a live 6-week project with the Bank of England
Price
£8,395
£7,895* (upfront)
*Benefit from a reduced rate by making payment upfront, prior to the start of the programme, or ask an Enrolment Advisor about our flexible payment plans.
We are committed to educating the data professionals who will define what innovation looks like at the intersection of Data Science and the Digital Economy, while at the same time prioritising diversity and inclusion, ethical responsibility and legal compliance.
Dr. Ali Al-Sherbaz
Assistant Professor and Academic Director for Digital Skills courses at University of Cambridge Institute of Continuing Education
Most of the world’s leading companies employ data scientists. But there's a common problem that separates junior data scientists from senior ones, as well as successful companies from those that fail: how to make data actionable and impactful.
This Data Science Career Accelerator from the University of Cambridge Institute of Continuing Education (ICE) aims to solve this challenge by equipping you with the skills to become a data scientist that drives and affects business results.
Over the next seven months, you’ll develop an in-depth understanding of statistical concepts, data science principles, and machine learning methods through the lens of various commercial contexts. By implementing your newfound skills and technological concepts into business strategy, you’ll learn how to choose the best applications to achieve business goals.
You’ll also explore advanced concepts such as time series analysis and natural language processing (NLP), with wide-ranging uses across business settings. Alongside this, you’ll explore the moral, legal, and ethical aspects of data and technology to holistically and inclusively understand and manage a data project within the corporate world.
Towards the end of the programme, you’ll focus on cutting-edge topics such as generative AI, and learn how they can be used to support business needs to drive innovation and refine automation. With this knowledge, you’ll be ready to play a crucial role in the digital economy, with a business mindset that sets you apart.
Richard Tomlinson
Analysis Director, CACI
Orientation (3 weeks):
Your seven-month Career Accelerator starts after three weeks of orientation, where you'll familiarise yourself with the digital campus, be introduced to your support team, connect with fellow learners and plan your studies.
Course 1 (6 weeks):
Applying statistics and core data science techniques in business
Course 2 (6 weeks):
Solving business problems with supervised learning
Course 3 (8 weeks):
Applying advanced data science techniques
Course 4: Exploring the future of data science, with an Employer Project
You will finish the programme with a live business project to build a portfolio of evidence that showcases the range of skills and competencies gained throughout the course.
*Note: University of Cambridge ICE is currently in the process of developing this programme and details are subject to change.
To provide flexibility in how you approach the programme, the majority of your learning happens asynchronously, while you also have the opportunity to join weekly live sessions. This allows you to manage your life around weekly deadlines and work through the course material when it suits you.
A series of live classes, masterclasses, and career coaching sessions.
Multiple industry-informed projects, more than 20 portfolio activities and a 6 week employer project with our partners in the industry, all leading to a diverse, career-transforming professional portfolio of advanced data science skills.
A range of rich media, including videos, graphics, and interactive experiences, to articulate complex concepts.
Peer reviews allow you to learn by evaluating the work of others and through receiving feedback.
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Doing the technical work can only get you so far – data scientists need to think about the business value and outcome of their work. This programme will help you to:
Make data actionable: Creating insights that challenge existing thinking and drive commercial outcomes.
Prioritise an inclusive approach: Identify potential bias and promote inclusivity in all content and processes.
Demonstrate legal, moral and ethical responsibility: Emphasise critical examination of legal requirements, moral obligations, and ethical implications in data science.
Recognise data science as a cornerstone of the digital economy: Highlight the key role data scientists play in the broader digital economy and digital skills landscape.
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80%
All data-related roles are forecast to see substantial growth as companies adopt frontier technologies that rely on data.
31
financial services
37
retail and wholesale of consumer goods
42
supply chain and transportation
Source: World Economic Forum, Future of Jobs Report, 2023
Ask your Enrolment Advisor for more information.
This programme brings together academic and industry perspectives to design, build, and deliver a curriculum that represents the best of both worlds.
Dr. Ali Al-Sherbaz
PhD, MSc, BSc, Electronic and Communications Engineering
Assistant Professor in Digital Skills at University of Cambridge Institute of Continuing Education
Author of more than 80 peer-reviewed papers with expertise in Cybersecurity, IoT, Data Science, AI, Blockchain and 5G. Passionate about guiding research and innovation strategies.
Shanup Peer
MBA, Operations/Marketing, MS, Electrical Engineering, B.Tech, Electrical and Electronics Engineering
Data Scientist and Programme Industry Expert
Principal Data Scientist, AI Curriculum Architect and Data Science Mentor, having fulfilled engagements with Government entities and corporate clients, developing technology solutions that have been deployed on a country-wide scale.
Robert Hardman
Chief AI & Innovation Transformation Officer, Inchcape Digital
Robert is an industry trailblazer with a career spanning over 25 years working with Fortune 100 companies, such as Facebook and Uber, guiding them through digital business transformations. His command over advanced mathematical techniques and knowledge of global technological ecosystems has made him a specialist in employing state-of-the-art technologies such as Generative AI, LLM’s, ML to transform & reimagine businesses.
Dr. Alexia Cardona
BSc, MSc, PhD
Training Programme Lead in Data Science at Newnham College, University of Cambridge.
Alexia is also a Tutor and Postgraduate Mentor at Newnham College, and a Senior Teaching Associate in the Department of Genetics. Her research interests focus on teaching and learning in the areas of data science, reproducibility, and Bioinformatics.
Dr Giovanna Maria Dimitri
Assistant Professor in Deep Learning and Artificial Intelligence, University of Cambridge
Giovanna is a researcher at the University of Siena. She completed her Master's and PhD in Computer Science at the University of Cambridge under the supervision of Prof. Pietro Liò, focusing on Artificial Intelligence and Machine Learning for biomedical data processing at the Department of Computer Science. She has a research publication record of over 45 papers in peer-reviewed and international journals, as well as broad experience in teaching and supervising. She has been interviewed by several journals and TV shows in Italy for her expertise in Artificial Intelligence and Computer Science and has considerable experience in science communication events. Her research interests focus on artificial intelligence, in a wide spectrum of applications, as well as in the development of foundational models.
Dr Russell Hunter
PhD Computational Neuroscience, Senior Teaching Associate (Online Education and Web Technology), Department of Engineering, University of Cambridge
Dr Hunter's varied career has spanned industry, research and teaching. His PhD was in the field of Computational Neuroscience, and his research has focused on image processing and computer vision in Formula One motor racing. He continues his research as a Post Doctoral Candidate in the Department of Engineering, developing novel educational tools. Dr Hunter is also R&D in a personalisation team, working with big data, data science, and machine learning to develop industry-first products from end-to-end. In this role, he leads on innovation strategy.
Jon Howells
AI and Data Science professional and founder of AI consultancy, Qualifai
Jon Howells is a seasoned AI and Data Science professional with a decade of experience in the field. He runs an AI consultancy called Qualifai and is currently working on a book titled "Data Science for Decision Makers". Jon has worked with various companies, including Nestlé, Unilever, and Capgemini, developing and deploying data science services and solutions. He holds a Master's degree in Computational Statistics & Machine Learning from UCL. Jon is particularly interested in the application of Large Language Models (LLMs) in consumer-focused businesses, such as leveraging LLMs for consumer research and feedback analysis, personalised content generation, and enhanced customer support, ultimately helping businesses better understand and engage with their customers.
Jack Gannaway
Data Analytics Leader and founder, Future Consulting
Jack Gannaway has been working in data analytics and data science across the UK and The Netherlands for the past 16 years. Spanning roles in both the public and private sector, his work has focussed on how to support decision-making with data.
Throughout his career Jack has worked with a huge variety of data, models and methodologies including demand modelling, b2b cross-sell prediction, predicting bankruptcy using accounting data, discrete event simulation and his favourite, dynamic stochastic microsimulation.
Diwakar Patwal
Head of Data Science and Chief Data Officer, Doji
Diwakar is leading the Data and AI practice at Doji, a UK based tech start-up which is reinventing the way consumers buy and sell refurbished electronics. He has had a long global career in building Data Science and AI solutions across the financial services, e-commerce and logistics industry.Developing complex ML and AI solutions in marketing optimisation, operational automation, customer engagement and logistics optimisation have been some of his career highlights. He has leveraged advanced machine learning techniques such as Computer Vision, Natural Language Processing, LLMs and Supervised ML models to deliver effective and impactful solutions.
Alexander Smirnov
Data Science Consultant
Alex advises merchants and banks on applying data science to improve their sales performance. In his previous roles Alex consulted clients on the issues of analytics and finance across multiple sectors, including energy, transport, and fixed income markets. Alex holds an MSc Financial Economics from Oxford and MSc Machine Learning from UCL. He has also successfully completed all CFA and CAIA examinations.
More academic and industry educators to be announced
Shanup Peer
Data Scientist and course subject lead
By the end of this programme you will be able to:
Critically analyse complex data to generate strategic insights and create innovative, commercially-aware solutions to real-world challenges.
Investigate and interpret key trends and relationships across diverse data sets by applying advanced statistical thinking and relevant techniques.
Develop, validate, and optimise advanced modelling solutions to address complex data science challenges with accuracy and efficiency.
Synthesise advanced skills in Python for data science, designing innovative solutions to complex problems.
Evaluate the potential applications and challenges of AI in organisational contexts, demonstrating awareness of ethical implications in AI implementation by addressing privacy and bias concerns.
And you will have gained:
A diverse, career-transforming professional portfolio of advanced data science skills featuring more than twenty industry relevant projects.
Experience of working with organisations like the Bank of England and PureGym on live data science projects tackling key business problems.
An understanding of your personal strengths and strategies for attaining your career goals developed with your career coach.
A certificate from the University of Cambridge Institute of Continuing Education to evidence that you have completed the programme.
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Learn the advanced tools, techniques, and skills that are currently getting data professionals promoted.
Course 1
Applying statistics and core data science techniques in business
What is statistical thinking? Why does it matter? What value does this approach add to business?
The best data scientists critically engage with the business problem before working on their analysis. This course will teach you how to identify potential issues at the analysis and solution planning stage that could result in huge costs to the business or have wider ethical implications.
You will understand how to select and apply relevant statistical techniques to create the simplest solutions to a range of business problems, saving the business time and money.
You will then continue to look at feature engineering and consider how it can be used to add value before diving into unsupervised learning techniques and how they can be used to solve business problems.
Course 1 learning outcomes
By the end of this course you will be able to:
Week 1: Developing commercial awareness through statistical thinking
This week explores commercial thinking, collaboration, communication, critical thinking, problem-solving, and project management skills, then lays the foundation for selecting and applying statistical analysis techniques.
By framing statistical thinking and feature engineering as tools for developing human problem-solving and critical thinking skills, you will begin to make connections between theoretical concepts – statistics and machine learning (ML) techniques – and practical applications in business.
Week 2: Applying advanced statistical techniques for data science
This week explores hypothesis testing using Python, as well as how to find causal relationships and make predictions with regression techniques.
Week 3: Taking a critical approach to selecting statistical techniques
This week explores alternative methodologies and highlights the importance of critical selection of appropriate techniques.
Week 4: Engineering features and reducing dimensions
This week provides hands-on experience in feature engineering techniques, including developing the ability to extract meaningful features from real-life organisational data to address specific challenges. The latter part of the week focuses on transforming high-dimensional data into visual stories using powerful techniques like PCA and t-SNE.
Week 5: Detecting anomalies with unsupervised learning
This week, you will practise anomaly detection, apply decision-making skills to real-world scenarios, perform fraud detection, and communicate insights that drive organisational success.
Week 6: Performing clustering with unsupervised learning
This week focuses on performing clustering using unsupervised learning techniques. The two main clustering techniques you will examine include k-means clustering and hierarchical clustering. The content covers the concepts, methods, and practical implementation of both techniques, along with considerations for cluster evaluation and real-world applications.
Example Project
Customer segmentation using clustering techniques
Total duration: 12 hours
What? Customer segmentation enables businesses to understand their customers better, tailor their strategies to specific segments, and ultimately drive growth, profitability, and customer satisfaction.
Why? Select, test, and apply effective unsupervised learning techniques to solve business problems.
How? Segregate customers based on their demographic, behavioural, and transactional data into distinct clusters to improve marketing strategies and enhance business profitability, using clustering techniques in Python.
Course 2
Solving business problems with supervised learning
This comprehensive supervised learning course offers a strategic blend of ML modelling and practical business applications, equipping you to unravel valuable insights from complex data sets.
The focus is on an array of regression, classification, and boosting algorithms, as well as sophisticated ensemble techniques. You will practise translating data insights into actionable decisions that drive business excellence across a spectrum of organisational contexts. Through the exploration of deep learning techniques, you will discover how to draw hidden insights from intricate data structures.
Practical activities include crafting and fine-tuning advanced ML models tailored to diverse organisational scenarios.
Actionable results will be achieved through data preprocessing skills, strategic model selection, and insightful analysis, to lead to proposed solutions and the formulation of comprehensive strategies that resonate with business objectives.
Course 2 learning outcomes
By the end of this course you will be able to:
Week 1 - Exploring supervised learning fundamentals
This week, you will develop a strong foundation in supervised learning, progressing from the basics to advanced techniques like polynomial regression and logistic regression. They will also gain practical insights into using activation functions effectively for improved model performance.
Week 2 - Deciphering patterns with neural networks
This week, you will explore neural networks as universal function approximators. There is a specific focus on deep learning and how cascading hidden layers enhance the modelling of complex decision boundaries. You will understand how to predict outcomes through forward propagation and optimise models through backward propagation and gradient calculations. Finally, you will explore the power of frameworks, particularly TensorFlow, to intuitively create neural networks.
Week 3 - Mastering TensorFlow to optimise SL models
This week covers key concepts and practical aspects of utilising TensorFlow for building and tuning supervised learning models. The content covers various layers, model architectures, evaluation metrics, hyper-parameter tuning techniques, and optimisation algorithms.
Week 4 - Optimising model generalisation
This week covers advanced techniques for optimising model generalisation and mitigating overfitting in supervised learning. The focus is on concepts such as bias and variance trade-off, addressing overfitting and underfitting, regularisation techniques including L2 regularisation and dropout, as well as employing callbacks, like early stopping, to enhance model performance.
Week 5 - Mastering decision trees and ensemble techniques
This week covers decision trees and ensemble methods, exploring their power in both classification and regression tasks. There is a focus on fundamental decision tree construction, model training techniques, the learning process, and advanced ensemble methods including bagging, boosting, and the random forest algorithm
Week 6 - Applying advanced modelling techniques
This week focuses on advanced model tuning strategies, AdaBoost, XGBoost, and the interpretation of model decisions through feature importance techniques like SHAP (SHapley Additive exPlanations). Finally, you will complete a 12-hour project activity that encompasses the supervised learning techniques covered throughout the course, enabling you to apply your acquired knowledge to a comprehensive real-world challenge.
Example Project
Classification or regression analysis?
Total duration: 8 hours
What? Explore an organisational scenario, define the problem, and perform relevant analyses.
Why? Embedding selection and problem-solving skills in the context of comparing the methodologies within organisational scenarios will continue to embed the real-life application of key concepts and relevant methodologies.
How? You will be provided with a scenario and will analyse the context to identify the problem. You will then define whether it is a regression or classification problem, select the appropriate methodology, evaluate the model, and summarise the outcome. The output will include an evaluation and report.
Course 3
Applying advanced data science techniques
This eight-week course covers two of the most critical advanced data science techniques for application in business – natural language processing (NLP) and time series analysis.
Driving many of the recent advancements in AI, NLP offers almost unlimited potential when utilised in the world of business. The ability to break down natural language into data opens up a world of analytical possibilities.
Equally, every business in the world needs to forecast something – whether that’s sales, resources, or other metrics. Time series forecasting offers business solutions to a wide range of challenges, regardless of the business’s domain or the team’s function.
With plenty of time dedicated to hands-on project-based work that will look impressive in any data scientist’s portfolio, you will build upon skills developed earlier in the programme to cover both topics in detail. An in-depth knowledge of these advanced techniques will set you up for senior data science roles in a wide range of sectors.
Course 3 learning outcomes
By the end of this course you will be able to:
Week 1 - Preprocessing for natural language processing (NLP)
This week covers essential concepts and techniques in natural language processing (NLP) preprocessing, sentiment analysis, and word embedding vectors. Preprocessing involves preparing text data by removing noise, stemming, lemmatising, tokenising, and analysing n-grams. Sentiment analysis explores using tools like spaCy and NLTK to assess the emotional tone of text. You will then explore embedding vector methods like one-hot representation, word2vec, GloVe, and, finally, how to calculate cosine similarity for representing and comparing words.
Week 2 - Building a basic NLP model
This week, the focus is on applying basic methods like Bag of Words (BoW) and term frequency-inverse document frequency (Tf-idf) to analyse the text corpus. You will then move on to recurrent neural networks (RNNs), including both GRU and LSTM, and discover their practical applications in NLP. Finally, you will advance your skills by building and fine-tuning NLP models
Week 3 - Working with transformer models
This week, you will explore transformer architecture, covering self-attention, multi-head attention, and positional encoding. You will explore and apply advanced models like BERT and GPT for text classification and generation tasks. The focus extends to developing chatbots with NLP capabilities and tackling tasks like topic modelling/text summarisation. The goal is to equip you with essential NLP skills and the ability to address real-world challenges effectively.
Week 4 and 5 - Portfolio focus and consolidation
These two weeks focus on starting two projects that consolidate and apply knowledge gained in the previous three weeks, allowing you to apply NLP for text summarisation and modelling in a real-life context.
Week 6 - Performing time series analysis
This week’s focus is on time series data analysis, essential for understanding and forecasting temporal trends in various domains. The content covers the fundamental characteristics of time series data. You will explore crucial statistical tests for model diagnostics and selection. Additionally, you will explore techniques for handling time series data, including decomposition and transformations, to prepare the data for accurate modelling and forecasting.
Week 7 - Exploring time series forecasting models and methods
This week is dedicated to mastering time series forecasting techniques, with a focus on exponential smoothing, ETS models, the Holt-Winters method, ARIMA models, and advanced modelling techniques. By understanding these methods, you can make precise forecasts, improve decision-making, and optimise resource allocation in various domains.
Week 8 - Applying machine learning and deep learning techniques
This week focuses on utilising ML and DL techniques for time series analysis. You will explore how to leverage the power of both ML and DL statistical techniques by using them in conjunction to form a hybrid model. This will enhance the overall predictive power of the model. You will preprocess, model, and forecast data effectively, arriving at solutions for real-world business challenges and enhancing operational efficiency.
Weeks 9 and 10 - Consolidation and topic project
In the final weeks, you will consolidate and apply your new knowledge of time series analysis with an in-depth 40-hour project.
Example Project
Using time series analysis for sales and demand forecasting
Total duration: 2 weeks (±40 hours)
What? Focus on utilising time series analysis techniques to forecast sales and demand. It involves the analysis of historical sales data to make accurate predictions of future sales trends and demand patterns.
Why? Enhance decision-making by providing reliable sales and demand forecasts to help organisations reduce costs, maximise profits, and optimise resource allocation..
How? You will analyse the employer scenario and explore the data set. You will then clean historical sales data, perform time series analysis using ARIMA, DL, or hybrid methods, and validate the models to ensure their accuracy in predicting future sales and demand patterns. The output will include an evaluation and report.
Course 4
Exploring the future of data science, with a live business project
You will explore the future of data science before demonstrating your skills developed throughout the programme with a live business project run by a real employer.
To begin, you will consider the business and ethical implications of Generative AI, ChatGPT and other large language models (LLMs) in the context of the Data Science profession.
You will finish the programme with a project to showcase the range of skills and competencies gained throughout the course. You will collaborate effectively within a cross-functional team, using multidisciplinary approaches to solve complex real-world problems set by leading industry partners and create strategies that maximise potential business value.
Tools/Platforms:
Google Colab, Jupyter Notebook and GitHub
Programming Language: Python
Benefit from 1:1 executive coaching
Personalised coaching
Benefit from six 1:1 coaching sessions that provide tailored career advice, aiming to enhance your professional growth in data science. The sessions focus on your unique career aspirations, tackling your questions and equipping you with practical strategies for success.
Group sessions
Participate in four group sessions that address essential career topics, such as how to set meaningful career goals and stay on track, managing your mindset for career success, navigating imposter syndrome with confidence, and team effectiveness. These sessions offer a shared learning experience, enabling you to gain insights from peers while understanding the broader context of their career journeys.
Please note: University of Cambridge ICE is in the process of developing this programme and details are subject to change.
"We are thrilled to be collaborating on the Employer Project to, not only provide the learners with the unique opportunities to practice data science on offer at the Bank of England, but to also give us the opportunity to reach a broader pool of potential talent from diverse backgrounds and experience who we know have the skills we need to help us achieve our mission.
With every area deeply involved in data and analysis in some way, there are opportunities at the Bank to partner with experts in financial markets, in supervision, prudential policy and financial stability, in macroeconomics, in finance and human resource, and to deploy data science to inform data-driven decisions made in the public interest.”
James Benford, Chief Data Officer at Bank of England
“I’m really excited to work with learners on the Data Science Career Accelerator and look forward to the perspectives they'll provide on our unique data challenges. At PureGym, our mission is to inspire a healthier nation, and it will be great to allow more people to deploy their data science skills to help us do that. Fundamentally, these types of programmes are really important to bring people into data domains and it becomes a key source of talent for us as a business.”
Niklas Ek, Group Head of Data, Insights and Analytics at PureGym
Find out how to use this Career Accelerator at your company.
During this programme you’ll be supported every step of the way to ensure you have a rich career-first learning experience tailored to help you go further, faster.
Through a mix of regular 1:1 sessions and interactive masterclasses, your career coach will work with you to identify and develop the employability traits and personal skills needed to thrive in the industry. Your coach will help you to build skills development plans and will help you network with employers to identify the next step in your career – whether it’s a new role or a promotion.
Your success manager is focused on your professional success and career needs. You’ll develop a meaningful relationship with them through ongoing 1:1 sessions during the programme. This powerful learning partner will help you to define and achieve your goals.
Using their subject-matter knowledge, programme tutors guide you through content-related challenges, support your understanding of complex concepts, and provide feedback to enable your development. The experience and expertise your tutors provide are invaluable. Tutors run weekly live sessions and provide feedback on submitted projects and portfolio activities.
Using both their subject-matter knowledge and industry experience, industry mentors provide a business focus to content-related challenges, providing guidance and context through real-world examples. Your industry mentor will support you in understanding key topics and will provide industry-focused feedback on project submissions. They will lead industry-focused classes every other week throughout the programme and 1:1s are available on request to cover topics such as portfolio development and project feedback.
As an expert in Python, the technical advisor is on hand to help you with any Python issues and challenges. When needed, you have the option to arrange a 1:1 with the technical advisor in order to dive deeper into any challenges.
Career Accelerators are driven by the excellence of university education, built in collaboration with tech industry leaders, and guided by employer partners to develop the technical and human competencies valued by the world’s most successful companies.
The Institute for Continuing Education’s mission is to make higher education accessible to any motivated adult learner through affordable course provision; flexible modes of course delivery; inclusive and supportive education and a commitment to peer learning.
About The Institute for Continuing Education (ICE)
The Bank of England's mission is to promote the good of the people of the United Kingdom by maintaining monetary and financial stability. Its primary objectives include maintaining price stability and supporting the government’s economic policies.
About the Bank of England
Study Group is a leading international education specialist and strategic partner to more than 50 global universities. They deliver high quality international education solutions that drive success for both partners and students – from outstanding teaching to innovative approaches to international recruitment and student support
About Study Group
Founded in 2008, the PureGym Group is one of the largest gym and fitness operators in Europe. The Group has approximately 1.6 million members across over 500 gyms and holds market leading positions across the UK, Denmark and Switzerland operating under the PureGym, Fitness World and Basefit brands respectively.
About PureGym
Nielsen BookData provides a range of services to the book industry internationally, aiding the discovery and purchase, distribution and sales measurement of books. Their research services provide retail sales analysis for print books in 17 countries alongside research from their Books and Consumers Survey.
About Nielsen BookData
cambridge.ice@fourthrev.com