CSMPSP: Problem Solving with Python

Module code: CSMPSP

Module provider: Computer Science; School of Mathematical, Physical and Computational Sciences

Credits: 20

Level: 7

When you’ll be taught: Semester 1

Module convenor: Dr Lily Sun , email: lily.sun@reading.ac.uk

Pre-requisite module(s):

Co-requisite module(s):

Pre-requisite or Co-requisite module(s):

Module(s) excluded:

Placement information: NA

Academic year: 2025/6

Available to visiting students: Yes

Talis reading list: No

Last updated: 3 April 2025

Overview

Module aims and purpose

The module aims to cultivate students' ability to think computationally, breaking down complex problems into manageable parts and developing algorithmic solutions, developing critical and analytical thinking skills through structured problem-solving exercises, fostering a systematic approach to challenges, and practising application of Python concepts tools through hands-on projects, case studies, and collaborative exercises.

This module also fosters the development of transferable and professional skills, such as critical thinking, teamwork, technical report writing, self-reflection, and appropriate, effective, and ethical use of Generative AI technologies to enhance learning.

Module learning outcomes

By the end of the module, it is expected that students will be able to:

1. Acquire foundational knowledge and skills in Python, applying problem-solving methodologies to specific business contexts;
2. Design and implement algorithms to address challenges, selecting and applying appropriate methodologies;
3. Demonstrate proficiency in Python syntax, including data types, control structures (such as loops, conditionals, and functions);
4. Manage file input and output by effectively reading from and writing to files in Python;
5. Utilize standard libraries and external modules (such as NumPy and Pandas) to enhance problem-solving capabilities; and
6. Evaluate developed solutions in Python through assessing their results, effectiveness, constraints to meeting business requirements.

Module content

This module will cover the following topics: 

• Introduction to Problem Solving Process
  • Identifying and understanding problems within the business context and objectives.
  • analysing the problem, identifying constraints, and setting goals.
  • abstracting and specifying the problem conceptually by breaking down the problem into a clear sequence of actions.
  • translating business solutions into computational algorithms.
  • testing and verifying solutions which are accurate, efficient, and meet business requirements.
• Developing Algorithms for Problem Abstraction and Decomposition
  • using structured English to outline step-by-step processes for addressing problems.
  • utilizing flowcharting to create visual representations that outline solution steps, decision points, and the flow of logical sequences.
• Python Basics for Problem Solving
  • understanding Python as a Tool for AI assisted problem solving, including an overview of how Python supports business applications like automation, data processing, and analysis.
  • core Concepts for implementing abstractions in algorithms
    • Data Types: Understanding and using strings, integers, floats, lists, and dictionaries.
    • Variables: Storing, manipulating, and organizing business data.
    • Basic Operations: Using arithmetic, comparison, and logical operators to calculate and evaluate conditions.
    • Input and Output: Getting information from users and displaying results effectively.
• Control Structures for Decision-Making
• conditionals: Using if, else, and elif statements to define business logic and make decisions.
• Loops: Implementing for and while loops to perform repetitive tasks until specific conditions are met.
• Data Management and Manipulation
• ºÚ¹Ï³ÔÁÏÍø and Writing Files: Accessing and modifying data stored in databases or text and CSV files.
• Functions and Library Tools
• creating reusable blocks of code for commonly used business tasks.
• overview of useful libraries like Pandas for data manipulation, NumPy for calculations, and Matplotlib for data display.
• applying library tools to automate tasks and simplify analysis.

Structure

Teaching and learning methods

This module will take a problem-based learning approach. The lectures will deliver concepts and principles outlined in the indicative content. Students will be supervised in the practical sessions to apply the methods, tools, to a given problem context and develop a verified solution. The lectures and practical sessions will enable students to develop innovative solutions, and critically apply the Python tools to real-world datasets.  

There will also be learning materials in digital forms when they are required to support learning.   

Study hours

At least 48 hours of scheduled teaching and learning activities will be delivered in person, with the remaining hours for scheduled and self-scheduled teaching and learning activities delivered either in person or online. You will receive further details about how these hours will be delivered before the start of the module.

Please note the independent study hours above are notional numbers of hours; each student will approach studying in different ways. We would advise you to reflect on your learning and the number of hours you are allocating to these tasks.

Semester 1 The hours in this column may include hours during the Christmas holiday period.

Semester 2 The hours in this column may include hours during the Easter holiday period.

Summer The hours in this column will take place during the summer holidays and may be at the start and/or end of the module.

Assessment

Requirements for a pass

Students need to achieve an overall module mark of 50% to pass this module

Summative assessment

Penalties for late submission of summative assessment

The Support Centres will apply the following penalties for work submitted late:

Assessments with numerical marks

• where the piece of work is submitted after the original deadline (or any formally agreed extension to the deadline): 10% of the total marks available for that piece of work will be deducted from the mark for each working day (or part thereof) following the deadline up to a total of three working days;
• the mark awarded due to the imposition of the penalty shall not fall below the threshold pass mark, namely 40% in the case of modules at Levels 4-6 (i.e. undergraduate modules for Parts 1-3) and 50% in the case of Level 7 modules offered as part of an Integrated Masters or taught postgraduate degree programme;
• where the piece of work is awarded a mark below the threshold pass mark prior to any penalty being imposed, and is submitted up to three working days after the original deadline (or any formally agreed extension to the deadline), no penalty shall be imposed;
• where the piece of work is submitted more than three working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.

Assessments marked Pass/Fail

• where the piece of work is submitted within three working days of the deadline (or any formally agreed extension of the deadline): no penalty will be applied;
• where the piece of work is submitted more than three working days after the original deadline (or any formally agreed extension of the deadline): a grade of Fail will be awarded.

The University policy statement on penalties for late submission can be found at: /cqsd/-/media/project/functions/cqsd/documents/qap/penaltiesforlatesubmission.pdf

You are strongly advised to ensure that coursework is submitted by the relevant deadline. You should note that it is advisable to submit work in an unfinished state rather than to fail to submit any work.

Formative assessment

Formative assessment is any task or activity which creates feedback (or feedforward) for you about your learning, but which does not contribute towards your overall module mark.

Weekly practical exercises (some may be in the form of groupwork) will be used as formative assessment. Feedback on weekly practical exercises will act as feedforward in learning for the coursework assessments. 

Reassessment

Additional costs