Module Specification

Data programming (R and Python)

London school of INNOVATION

Module Specification

Data programming (R and Python): Uncovering Real-World Data Insights


In the modern landscape, the ability to programme and interpret data is as critical as ever. This module spans two crucial programming languages—R and Python—anchoring your knowledge in both. You will encounter real-world datasets, grappling with common challenges and solutions that industry professionals face daily.

You will learn about core programming elements, focusing on variables, data types, loops, functions, and error handling. This module will explore essential frameworks like Pandas and dplyr for advanced data manipulation. From list comprehensions in Python to vectorized operations in R, the curriculum teaches practical applications for efficient data handling.

Completing this module equips you with versatile skills applicable across various domains. You will be adept at reading and writing multiple data formats, visualising data trends with tools like Matplotlib and ggplot2, and performing complex data transformations. These competencies provide a solid foundation to excel in data-centric roles, making you a valuable asset in any analytical team.


Code Number of Credits ECTS Credits Framework HECoS code
DP71 15 7 FHEQ - L7

Learning outcomes

Code Attributes developed Outcomes
LO1 Knowledge and Understanding Demonstrate a systematic understanding of core programming concepts in R and Python for data analysis.
LO2 Intellectual Skills Analyse and interpret complex datasets to draw meaningful conclusions using advanced error handling and data manipulation techniques.
LO3 Intellectual Skills Critically evaluate the use of key frameworks like Pandas and dplyr for advanced data manipulation in complex scenarios.
LO4 Technical/Practical Skills Design and implement robust data visualisations using Matplotlib, Seaborn, and ggplot2 for clear communication of data insights.
LO5 Technical/Practical Skills Develop and execute advanced data aggregation and statistical analysis methods, including correlation matrices and linear regression.
LO6 Professional/Transferable Skills Critically reflect on ethical implications in data handling and apply professional codes of conduct in programming practices.

Assessment Patterns

Weighting Format Outcomes assessed
40% Invigilated Exam
This is a time-limited and closed-book exam with a mix of multiple-choice and analytical written questions that students undertake during the summative assessment period as scheduled under the School’s remote invigilation conditions to ensure quality and academic integrity.

The exam enables the students to demonstrate their successful attainment of the module learning outcomes, primarily related to knowledge and understanding, and secondarily related to Professional/Transferable Skills.

The analytical written questions will consist of problem questions representing issues and dilemmas students are likely to encounter in professional life and students have to synthesise and apply what they have learnt on the module in order to produce sound and reasoned judgements with respect to the problem.

To enable the students to practice and prepare, various formative assessment activies, including quizzes and a AI-augmented assignments and mock exams are built into the module. Additionally, throughout the course, students will regularly receive feedback on their knowledge and assignments from AI as well as peers and staff to indicate how to improve future work and how to give constructive feedback to others.		 K LO1
I LO2
I LO3
P LO6
60% Technical Analysis and Solution Assessment
This assessment requires students to develop a solution to a complex problem within a simulated domain, followed by a detailed analysis and reflection on their design and its theoretical underpinnings. The aim is to assess students' abilities to design practical solutions, critically analyse their work, and articulate their understanding of the technical and theoretical aspects of the module.		 K LO1
I LO2
I LO3
T LO4
T LO5
P LO6

Student workload

Activity Total hours
Introductory lecture 1.50
Concept learning (knowledge graph) 18.00
AI formative assessment 9.00
Case Study Review 9.00
Workshop/Lab Sessions 13.50
Individual or group assignments 18.00
Independent reading, exploration and practice 51.00
Summative assessment 30.00
150.00

Content Structure

Week Chapter Name Chapter Description
Week 1 Programming Fundamentals Master the basics of programming with R and Python. Focus on core concepts like variables, data types, loops, and functions. Understand why these elements are crucial for structuring efficient code. Learn how to use them to automate repetitive tasks and write clean, reusable code that lays the groundwork for more advanced data operations.
Week 2 Error Handling Learn how to predict, catch, and manage errors in R and Python code. Understand why anticipating errors is essential for building robust, real-world applications. Explore techniques like try-except in Python and tryCatch in R to prevent your programs from breaking unexpectedly and ensure smooth execution even when something goes wrong.
Week 3 Data Structures Mastery Explore essential data structures in R and Python like lists, vectors, and DataFrames. Learn why efficient data storage and manipulation are critical for scaling analysis. Develop skills to navigate, transform, and structure large datasets quickly and logically using key tools like Pandas and dplyr.
Week 4 Data Input and Output Learn how to read, write, and manipulate data in common formats such as CSV, JSON, and Excel. Understand the importance of seamless data exchange in real-world applications. Master practical file I/O skills that enable you to integrate and process data from various sources, making your analyses versatile and flexible.
Week 5 Advanced Data Manipulation Master powerful data manipulation techniques in both Python and R. Explore how to group, merge, filter, and summarise data to uncover insights efficiently. Learn why data wrangling is the backbone of any meaningful analysis, allowing you to reshape raw datasets into structured formats ready for analysis.
Week 6 Data Visualisation Techniques Learn how to create compelling and informative data visualisations using libraries like Matplotlib, Seaborn, and ggplot2. Understand why effective data visualisation is key to communicating insights. Develop skills in plotting trends, distributions, and relationships, making data stories clear and impactful to stakeholders.
Week 7 Statistical Computing Learn how to apply key statistical techniques like linear regression, correlation, and matrix operations using Python’s NumPy and R's base functions. Understand why statistical computing is crucial for transforming raw data into actionable insights, enabling data-driven decisions and predictions in real-world contexts.
Week 8 Applied Data Analysis Integrate your programming, data manipulation, and visualisation skills to conduct comprehensive data analysis. Learn why applying all these skills together allows you to extract meaningful insights from complex datasets. Develop the ability to handle messy, real-world data and turn it into actionable outcomes through structured analysis.
Week 9 Real-World Problem Solving Solve real-world problems using R and Python, applying everything you’ve learned in the course. Understand why solving complex, practical problems is the ultimate goal of data programming. Develop a strategic mindset to tackle diverse challenges, whether it's optimising workflows or generating predictive insights from data.

Module References

There are no module reference contents to display.

Methods of teaching/learning


Introductory lecture (1.50 hours)

This is the first weekly session, dedicated to providing a comprehensive introduction to the module. The module leader will present an overview of the subject, elucidating its importance within various digital engineering professions and its interrelation with other modules. Students will need no preparation ahead of attending this session.

The module leader will provide a structured breakdown of the content to be covered in the subsequent 9 sessions. Students will also receive an outline of the essential reference materials, alongside suggestions for supplementary reading. The format and criteria for the summative assessment will be delineated, followed by a dedicated period for questions and answers.

A recording of the session will be available to facilitate async engagement for any other student who missed the class, also offering an opportunity to review the content again.


Concept learning (knowledge graph) (18.00 hours)

Our institution's approach to teaching is primarily based on flipped learning. Ahead of each weekly session (Workshop/Lab), students will be required to study the essential concepts that are used in the coming session so they are familiar with the theories and ideas related to that session. The study material will be in the form of written content, illustrations, pre-recorded lectures and tutorials, and other forms of content provided through the AGS.

This content is self-navigated by the students, accommodating different learning styles and schedules, allowing students to watch or listen to them at their own pace and review them as needed.


AI formative assessment (9.00 hours)

Once each concept of the theory is studied, students will be prompted to engage in formative assessment with instant AI feedback. They include multiple-choice questions, socratic questions and answers, written questions, role-play and other AI-assisted practice scenarios.

The purpose of this automated formative assessment is to provide students with immediate feedback on their understanding of module material and highlight any areas that need support or further study. They are also used to track student progress, boost motivation and promote accountability.


Case Study Review (9.00 hours)

In this learning activity, students explore recent real-world case studies relevant to their course topic. The case studies will have been selected and curated by the module leader to represent up-to-date examples. They guide students through key details, contextual factors, and outcomes. This approach enhances students' understanding of current industry trends, challenges, and solutions, preparing them for real-world scenarios they may encounter in their future careers.

The learning experienced will be augmented by AI (virtual private tutor) allowing the students to critically engage with the content and discuss the case studies.


Workshop/Lab Sessions (13.50 hours)

The 9 weekly sessions following the introduction (weeks 2 to 10) will be dedicated to teaching the contents of the module during interactive workshops. These sessions will complement the theory with practice, experience or analysis. Their purpose is to advance the student's cognition from 'knowledge' to 'understand' and 'apply'.

Depending on the nature of the content, challenges and learning activities will be pre-designed to apply flipped learning, and may include hands-on project work, group discussions or debates, roleplay, simulation, case study or other presentation, and other learning activities and opportunities. These workshops present an opportunity to apply critical thinking and problem-solving skills. They also encourage collaboration and foster a sense of community among students. There will be an opportunity also for Q&A in every session.


Individual or group assignments (18.00 hours)

Each Workshop/Lab session will be followed by an assignment. Assignments are used to reinforce learning and encourage independent thinking and problem-solving. They help the students identify the gaps in their understanding of the subject and provide them with an opportunity to apply what they have learned in a practical setting.

Assignments can be individual or group-based (teams of 2 to 4). They can take many forms, including essays, presentations, or projects. When they are group-based, teams will be randomly picked by AGS, in order to promote broader teamwork practice. Assignment files will be uploaded to AGS by the students ahead of the next weekly session. Feedback will be provided on each submitted assignment.


Independent reading, exploration and practice (51.00 hours)

This activity challenges students to engage with the reference material and independently explore and analyse academic literature related to the course topic. Students are expected to select relevant sources, practice critical reading skills, and where applicable technical skills, and synthesise information from multiple references. This is an opportunity to enhance research abilities, critical thinking, and self-directed learning skills while broadening and deepening subject knowledge.

Programmes this module appears on

Programme Term Type
1 Data Science and Analytics (MSc) 1 Core
2 AI for Business Transformation (MSc) 2 Optional
Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change.
Module Spec: Data programming (R and Python): Uncovering Real-World Data Insights (DP71)