Duration
20 July - 7 August 2026
Data Science in Business and Economics
Explore the power of data in business and economics. This three-week programme introduces you to cutting-edge techniques for analysing and interpreting data, combining coding, data visualisation, and machine learning with real-world applications.
This programme offers a practical introduction to working with real‑world data through a blend of interactive teaching and hands‑on coding in R. You will explore how different types of data can be prepared, explored, and analysed, and gain experience applying these techniques to examples drawn from economics, politics, and social media. The course is designed to help you build confidence with computational tools.
Programme structure
Part 1: Data science foundations
The first part of the module introduces core data science methods essential for empirical research. You will learn:
- Data processing and cleaning
- Effective data visualisation for exploration and communication
- Fundamentals of machine learning, including:
- Supervised and unsupervised learning
- Model evaluation
- Algorithms for prediction and pattern discovery
Part 2: Text‑as‑data methods
The second part focuses on techniques for representing and analysing textual information systematically. Topics include:
- Tokenisation and dictionary-based approaches
- Sentiment analysis
- TF‑IDF and vector representations of text
- Topic modelling
- Word embeddings
You will apply these methods to real-world datasets such as:
- Sales records
- Political speeches
- Economic policy documents
- Social media posts
- Course Type
Summer School,
Undergraduate
Why study this course?
Why study this course?
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Hands-on coding: Learn R for data analysis in interactive seminars.
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Modern methods: Explore data processing, visualisation, and predictive modelling.
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Text-as-data: Apply sentiment analysis, topic modelling, and word embeddings to business and economic texts.
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Real-world datasets: Work with sales records, policy documents, and social media data.
What you will learn and experience?
What you will learn and experience?
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Practical coding skills for business and economic data analysis.
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Ability to process, visualise, and interpret structured and unstructured data.
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Insights into machine learning and text analytics for real-world applications.
This programme is designed for international students seeking an introduction to UK academic life. Beyond the classroom, participants will enjoy a vibrant social programme, including cultural visits and networking events, creating an unforgettable experience of studying in Birmingham.
What skills will you develop?
What skills will you develop?
By the end of the module, you will be able to:
- Collect, process, visualise, and analyse both structured and unstructured data.
- Apply machine learning and text‑analysis methods to real datasets.
- Draw meaningful insights and causal conclusions from complex data.
- Use these skills in academic research, policy analysis, and related fields.
Who can apply?
Who can apply?
- International students aged 18+ and studying at an undergraduate level, or registered as an international student at a UK university.
- Demonstrate some prior coding experience in at least one programming language (for example, R, Python, MATLAB, Julia, C++, C, C#, Java, Fortran, Stata). Students must bring their own laptops with R pre-installed.
- Students must be able to demonstrate good academic standing (based on a translated transcript, verified by their university).
- Meet the BISS entry requirements for English Language Proficiency (minimum B2).
How will you be taught and assessed?
How will you be taught and assessed?
Teaching
The programme will be taught through interactive lectures and coding sessions in R. Prior experience in any programming language is strongly recommended; the programme will provide guided practice to help students develop and strengthen their computational skills.
Assessment
At the end of the programme, there will be a formal assessment consisting of a group presentation.
A pass requires a minimum of 40% in combination with a minimum attendance of 90% of timetabled activities.
Please check with your home institution regarding transfer recognition of credits.