Turn On Your Future @ UWTSD's School of Applied Computing & Electronics

Posts tagged ‘UWTSD’

Student profile: Hamza Qasim

Name: Hamza Qasim

What University course are you doing? MA Masters in Animation at UWTSD.
This follows my completion of a BA in Computer Games Design at UWTSD, where I spent 3 fulfilling years building my foundation in digital arts and game development.

What truly inspires me about animation and the broader field of digital arts is the unlimited creative potential it offers. I have a genuine passion for games, animation, VFX, and CGI, particularly the captivating effects seen in Marvel movies. The ability to bring ideas to life through digital mediums and create immersive experiences that can transport viewers into different worlds is incredibly motivating.

Video below: BA Game Design animation by Hamza Qasim

Beyond the creative aspects, I’m inspired by how this field connects to my other interests, especially automotive design. The intersection of technical precision and artistic vision in CGI, particularly in automotive visualisation, represents the perfect blend of my passions. What makes this journey even more inspiring is the supportive environment at UWTSD, where I feel encouraged to explore emerging technologies and push the boundaries of what’s possible in animation and interactive media.

Video below: Portfolio from BA Games Design Yr.3 by Hamza Qasim

Throughout my Masters program, I’ve been developing a diverse range of technical and creative skills. On the technical side, I’ve advanced my proficiency in 3D modelling, animation, texturing, and rendering using various versions of Blender (4.0, 4.2, and 4.4). I’ve also expanded into video editing and composition using Wondershare Filmora 13, along with comprehensive sound design capabilities.

Video below: Cans by Hamza Qasim

Video below: Koi by Hamza Qasim

Recently, I’ve ventured into VR development using Unreal Engine 5.5, where I’ve learned to create custom blueprints for asset switching, material switching, and targeted interactions. One of my proudest achievements has been developing a real-time sun movement system that enhances environmental immersion in VR experiences.

Video below: Material Switching & Sun Movement by Hamza Qasim

Beyond technical skills, I’ve been developing crucial soft skills through modules like Leadership, Management and Ethics. This module, though different from animation, has taught me about various leadership styles, management approaches, and proper research methodologies, skills that are invaluable for any creative professional.

Favourite Technology: My current favourite tool is definitely Blender. While we also learn Maya at University, I’ve found that the modelling and animation skills transfer seamlessly to Blender, and it offers several advantages that make it exceptional for independent learning and creative work. What I love most about Blender is its comprehensive nature, I can model, sculpt, texture, animate, composite, and render all within a single application.

The fact that it’s completely free removes financial barriers to learning, and the extensive community support through YouTube tutorials and forums means that virtually any problem I encounter has likely been solved by someone else. The accessibility and community aspect of Blender have been game-changing for my learning process. While Maya still has some advantages, particularly for UV unwrapping, the plugins available for Blender help bridge most gaps, making it an incredibly versatile tool for independent artists and students.

Video below: Liminal Spaces using Advanced Techniques by Hamza Qasim

After you Graduate?
My career aspiration is to work as a CGI Artist at an automotive company, with Mercedes-Benz being my preferred destination. This goal is informed by my previous experience working there before enrolling at UWTSD, which gave me valuable insight into the industry and confirmed my passion for automotive visualisation.

Video below: Car in motion by Hamza Qasim

The automotive industry’s increasing reliance on high-quality CGI for marketing, design visualisation, and virtual showrooms aligns perfectly with my skills in 3D modelling, animation, and rendering. My experience with both traditional animation techniques and emerging technologies like VR positions me well for the evolving demands of automotive CGI, where immersive experiences are becoming increasingly important.

Video below: Cloth Reveal by Hamza Qasim

Advice for students?
My most important advice is simple but crucial: do something that you truly enjoy. Don’t choose a course based on others’ expectations or perceived prestige, choose it for yourself and your genuine interests.

I love what I study because it encompasses everything I’m passionate about: games, animation, VFX, CGI, and automotive design. This genuine enthusiasm makes the work feel less like a chore and more like an exciting challenge. When you’re passionate about your subject, you naturally strive for excellence and find yourself more resilient when facing difficulties.

Fig.: Digital Arts image

Another key lesson I’ve learned is about perfectionism and deadlines. As an artist, I’ll always strive for perfection, but I’ve come to understand that sometimes you need to aim for the best work possible within tight deadlines rather than perfect work that never gets completed. It’s better to submit a strong, complete project than to miss deadlines chasing an impossible ideal of perfection. The balance between ambition and pragmatism is essential for success in any creative field.

Experience at UWTSD?
My experience at UWTSD has been absolutely transformative and couldn’t have been better. The support system here is extraordinary, from Richard Morgan in the Games Design department, who has been my rock throughout my three-year BA journey, to lecturers like Nabeel Masih, Adam Head, Philip Organ, and James Williams, who have consistently encouraged my growth and exploration of emerging technologies. The university’s holistic approach to student support extends beyond academics. The well-being department has been incredibly supportive during challenging times with my mental health, and the entire campus environment, from reception and canteen staff to lecturers, creates an atmosphere that feels genuinely supportive and nurturing.

I’ve had numerous opportunities for leadership and community involvement, serving as Student Representative for both my BA and MA courses and participating in the UWTSD Esports Committee, where we organised events and created lasting memories. The university even pushed me toward pursuing my Masters degree when I wasn’t initially planning to continue, a decision that has proven invaluable. The collaborative environment has allowed me to form lasting friendships with both staff and students, and I’ve been able to contribute through graphic design work for various university projects. If I could change anything about my university experience, it would be nothing, every aspect has been beneficial and has contributed to my growth as both a person and a professional.

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For further information about Computer Games & Animation courses at UWTSD, please click here.

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Project profile: James Frew

Fig.: James Frew

Name: James Frew

Course: BSc (Hons) Software Engineering

Project Title: An affordable device for monitoring noise levels in home studio environments to warn users of duration-based hearing damage.

Purpose: As a drummer and someone who regularly practises in home studio environments, I quickly became aware of how easily you can be exposed to loud sounds for long periods. Many people only think about hearing damage in terms of volume, but in fact, long durations of noises that aren’t excessively loud can be just as dangerous. I wanted to build a simple and affordable device that could help raise awareness of this overlooked danger and help people protect their hearing.

Fig.: Design

Implementation: The system consists of a small microphone and microcontroller, both housed in a compact 3D-printed case. The device connects to a desktop application built in Python. It monitors the noise in the room in real time, calculates how long it’s safe to be in that environment based on the current sound levels, and sends a warning when the average noise levels since starting the application reach a point where it becomes dangerous to continue listening.

Fig.: Case implementation

The GUI displays current noise levels, the average levels since starting, the time remaining before risk becomes high, and how long the app has been running. The system was built for under £20 and was tested with real users, receiving very positive feedback for its usability and clarity.

Fig.: Application

Conclusion:
The final product was a reliable, accurate tool that helped users understand their sound environment better. It successfully raised awareness of long-term hearing risks and provided clear, real-time feedback. The project met all its core goals and achieved an excellent System Usability Scale (SUS) score of 89.5. It also received valuable suggestions from users, which could shape future improvements, such as wearable versions and mobile app integration.

Next steps: I’m hoping to move into a role that allows me to work with people and ideally in music or creative environments. I’m particularly interested in opportunities that combine practical problem-solving with collaboration. My degree in Software Engineering has given me a strong foundation in project planning, technical thinking, and building things that work in the real world – skills that will be useful in many industries, not just computing.

Advice for students: Your time and energy are limited, so it’s important to prioritise your studies and project work, especially in your final year. Balancing other commitments is part of life, but giving focused attention to your degree when it counts will really pay off. Also, don’t be afraid to use the tools available to you – make use of AI, but without breaching academic standards. Do use it to help guide research and explore ideas related to your studies, don’t use it as a copy-and-paste tool to blindly research information. AI isn’t going to go away, so it’s important you learn how to use it to assist your studies as early as possible in your degree. Good luck with your studies!

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For further information about Computing & Computer Science courses at UWTSD, please click here.

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Project profile: Kelly Casey

Name: Kelly Casey

Course: BSc (Hons) Software Engineering

Project Title: Women in STEM Study Buddy – A Mobile Application for Academic Networking

Logo
Fig.: FemSTEMConnect

Project introduction:
The motivation behind this project was to address the under-representation and isolation that many women in STEM fields experience during their academic journey. I wanted to create a platform that would allow female students to connect, support one another, and collaborate through shared academic interests. The project reflects both my technical interests and my desire to contribute to inclusion and equity in higher education.

Overview of the Practical implementation:
The application was developed in Android Development Studio using the Kotlin programming language, with Firebase Backend-as-a-Service (BaaS) platform providing services for authentication, data storage, and real-time messaging.

Kotlin, Android Studio and Firebase image
Fig.: Kotlin | Android Studio | Firebase

Key features include:

  • Email link login (no passwords required)
  • Personalised academic profiles
  • Study partner matching using course and location filters
  • A Messaging system for real-time communication.
Fig: Coding

The project was tested on both emulator and physical Android devices for performance and usability.

Here are some screenshots from the final app up and working

Fig.: Navigation screen
Messaging inbox
Fig.: Messaging inbox
Fig.: Home screen & News feed

Conclusion: The final product successfully met the core functional goals. Users can register, create a profile, search for study partners, and communicate within the app. Although some features like the community forum were postponed, the application is stable, secure, and ready for future expansion. It represents a strong foundation for a peer-support platform specifically tailored to women in STEM.

What Career and job role are you hoping to move Into after Graduation? I’m planning to progress onto a Master’s degree and then move into a software engineering role, ideally within a company focused on education technology or socially impactful work.

Top Tips for Students interested in completing a University Degree: Don’t procrastinate. Choose a project that is manageable not just exciting. It’s better to complete something simple and solid than to get stuck in something too complex to finish on your own. Also, don’t be afraid to ask for help.

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For further information on Computing courses at UWTSD, please click-here.

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Guest Lecture: Network Automation and CI/CD

Guest Lecturer:
Nagaraj Ravinuthala

This week, Nagaraj Ravinuthala, a DevOps trainer at HCL Technologies Limited and a specialised trainer for WorldSkills UK Squad on Infrastructure Automation, delivered a Guest Lecture on Network Automation and CI/CD to our students.

The lecture began with basic yet powerful concepts of deployment and CI/CD, connecting the dots between writing code, integrating APIs, and automating the process of pushing updates live. The session emphasized simplicity and clarity, presenting CI/CD as a pipeline that automates code testing, integration, and deployment to production environments.

“Think of CI/CD as a conveyor belt for your code,” explained Nagaraj. “You write it, test it, and deploy it—all in a streamlined process that ensures faster delivery and better reliability, which are essential in modern network automation workflows.”

Students were introduced to tools and techniques aligned with Cisco’s CCNA DevNet, providing a glimpse into industry-standard practices. To make deployment relatable, the lecturer drew parallels with the earlier lectures on Python and API during their Network Programmability Module where the students integrated the OpenWeather API creating and running a Python script, and explaining that deployment involves taking code that works locally and making it accessible to users.

The session concluded with an interactive Q&A, where students enthusiastically asked about real-world applications of network automation and the career paths that mastery in CI/CD can unlock.

This guest lecture was an eye-opening experience for students, sparking curiosity and laying the foundation for further exploration in network automation. It was a step toward preparing them for the evolving demands of the industry.

We would like to thank Nagaraj Ravinuthala for taking time to speak and share valuable industry knowledge with our students.

For further information about our courses, please click-here.

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University Guide: UWTSD Computing #1 in Wales

Guardian University Guide: #1 in Wales and #20 in UK

We’re thrilled to announce that in the Guardian University Guide 2025, our Computing courses have ranked #1 in Wales 🏆 and #20 in the UK for Computer Science and Information Systems subject area.

This achievement reflects the dedication we bring to delivering hands-on, real-world learning experiences, alongside exceptional support that guides our students every step of the way. Our highly qualified, industry-experienced staff work tirelessly to ensure students are prepared for a future in tech with the practical skills and knowledge they need to excel.

A huge thank you to our outstanding students, dedicated staff, and supportive community for making this recognition possible.

Together, we’re shaping the future of tech in Wales and beyond! 🌍💻

For further information about Computing courses at UWTSD, please click-here.

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MSc Project: Cyber Security

Student name:
Sakthi Sangeetha Kandaswamy

MSc Project title: Analysing risk in Vulnerability Assessment and Penetration Testing Methodology

Course: MSc Cybersecurity and Digital Forensics

Introduction/rationale:
The motivation behind choosing the project stemmed from the growing need for more structured and standardized approaches to penetration testing and vulnerability assessment. With cyberattacks becoming increasingly sophisticated, organizations struggle to identify and remediate vulnerabilities efficiently.

TECH TERM: Penetration testing, often referred to as ‘pen testing‘, is a cybersecurity practice where ethical hackers simulate cyberattacks on a computer system, network, or web application to identify vulnerabilities that could be exploited by malicious hackers. The main goals of penetration testing is to:
1. Identify security weaknesses
2. Assess the effectiveness of security measures
3. Improve overall security posture: By identifying and fixing vulnerabilities, organizations can strengthen their defenses against potential cyber threats.

The MITRE ATT&CK Framework provides a well-defined structure for understanding adversarial techniques and tactics, making it ideal for enhancing VAPT (Vulnerability Assessment and Penetration Testing) methodologies. This project aimed to leverage MITRE’s capabilities to analyze risk, improve testing scope, and ensure that testing efforts are comprehensive and aligned with real-world threats.

Project overview:
In this project, we integrated the MITRE ATT&CK Framework into the traditional VAPT methodology to refine the testing scope and increase effectiveness in detecting vulnerabilities. The steps involved included:

  • Defining the Scope: The project began by clearly defining the boundaries and goals of the penetration test. Using the MITRE Framework, specific attack vectors and techniques relevant to the organization’s environment were identified.
  • Conducting Vulnerability Scanning: Automated tools were used to perform initial vulnerability scans, identifying weak points that attackers might exploit.
  • Simulating Attacks Using MITRE Tactics: We designed attack scenarios based on the tactics and techniques outlined in MITRE ATT&CK, simulating adversarial behavior. This allowed us to target the actual risks that real attackers would exploit, rather than theoretical vulnerabilities.
  • Analyzing Results and Refining Scope: Post-attack analysis identified network gaps and weaknesses in current defenses. The scope of the testing was iteratively refined based on these findings.
  • Reporting and Remediation: Finally, comprehensive reports were generated, providing actionable insights for the security team, along with specific recommendations for closing vulnerabilities.

Visual Representation: A flow diagram illustrating the process of integrating MITRE with VAPT in Penetration Testing Stages, is located below:

Project outcome & conclusion:
The integration of the MITRE ATT&CK Framework significantly enhanced the scope and depth of the VAPT process. By aligning testing activities with real-world adversarial tactics, the project was able to identify previously overlooked risks and vulnerabilities. The methodology provided a more focused, risk-based approach to penetration testing, ensuring that organizations could better prepare for and mitigate threats. The project demonstrated that using MITRE not only strengthens the identification of vulnerabilities but also offers a more comprehensive understanding of the adversarial techniques that could affect critical systems.

OpenVAS is a full-featured vulnerability scanning tool, that was used for this purposes of this project. An example output of vulnerability findings can be seen below:

Q. What Career and job role are you hoping to move into after graduation?
After graduation, I am hoping to pursue a career in cybersecurity, with a specific focus on roles like:

  • Penetration Tester: Using tools and methodologies (such as MITRE ATT&CK) to identify vulnerabilities in an organization’s IT infrastructure.
  • Cybersecurity Analyst: Monitoring, analyzing, and defending against cybersecurity threats.
  • Security Consultant: Advising organizations on how to improve their security posture by implementing effective VAPT practices.

These roles align with my passion for understanding and mitigating cyber risks, particularly in offensive security and ethical hacking.

Q. Please share a top tip/advice for students who are interested in completing a University Degree?
My top tip for students is to focus on practical applications of what you learn. Theoretical knowledge is important, but real growth happens when you apply it in real-world scenarios, whether through internships, projects, or labs. Hands-on experience not only solidifies your understanding but also makes you more marketable in the job market.

For further information about Computing courses at UWTSD, please click-here.

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MSc Project: Fusion neural network

Name: Ze Wu

Q. Course: MSc Software Engineering and Artificial Intelligence

Q. Project title: Detection of fusarium head blight on wheat spikelets using a multi-scale feature fusion CNN model.

Q. Introduction/rationale: The motivation for choosing this project stems from the significant impact of fusarium head blight (FHB) on wheat production, which poses a threat to global food security. By leveraging advanced artificial intelligence techniques, this project aims to develop a robust detection system that can assist farmers in early identification of FHB, ultimately contributing to better crop management and yield optimization.

Q. Overview of the practical implementation:

  • Data Collection: Acquiring a dataset of wheat spikelet images, both healthy and affected by FHB.

Figure 1: Dataset Samples

  • Model Development: Designing a multi-scale feature fusion convolutional neural network (CNN) to analyze the images. Built on the advanced YOLOv9 framework, the model incorporates a Multi-Scale Feature Enhancement and Fusion (MSFEF) module, which plays a critical role in extracting and dynamically enhancing features from various scales. To enhance computational efficiency, as shown in Figure 2, optimized convolutional layers such as RepNCSPELAN and DWConv are used, reducing the overall computational load while maintaining high detection performance.

Figure 2: Proposed network overview

  • Training and Testing: The model was trained on the dataset using techniques like data augmentation (Mosaic) to enhance its robustness. Testing was conducted to evaluate accuracy and performance.

Figure 3: Training batch samples

  • Deployment: The final model can be integrated into a user-friendly application for real-time detection, offering researchers and farmers an efficient tool for early disease detection and management.

Figure 4: Model predictions

Q. Overview of outcome/conclusion:

The project successfully demonstrated that the proposed multi-scale feature fusion CNN model can accurately detect fusarium head blight in wheat spikelets, achieving an accuracy of over 90%, which highlights the potential of AI-driven solutions in agriculture, paving the way for future advancements in plant disease management and crop monitoring.

Q. What Career and job role are you hoping to move into after graduation?

After graduation, I aspire to work as a machine learning engineer, focusing on applying AI in agricultural technology. My goal is to contribute to innovations that enhance food security and sustainability through smart farming practices.

Q. Please share a top tip/advice for students who are interested in completing a University Degree?

One essential piece of advice is to stay curious and actively engage with your coursework beyond the classroom. Seek out projects, internships, and collaborative opportunities that apply your knowledge in real-world contexts. Building a strong network and gaining practical experience will significantly enhance learning and career prospects.

For further information about Computing courses at UWTSD, please click-here.

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MSc Project: LSTM Networks

Project title: Development of Stock Price Prediction Model using LSTM Networks

Course: MSc / Master of Science in Data and Artificial Intelligence

Student name: Mohammed Talha Sajidhusein Vasanwala

Rationale: What was the reason/motivation for choosing the project?

The volatility of stock markets presents a major challenge in financial forecasting. Traditional methods, while effective in some cases, struggle to capture the inherent complexities of financial data, such as time dependencies, trends, and market shocks. Tesla’s stock prices, which are known for their rapid fluctuations, piqued my interest for this project. The motivation behind choosing this project was twofold:

  1. To explore how Long Short-Term Memory (LSTM) networks can be optimized to capture and predict the volatile patterns in stock price data.
  2. To address the limitations of traditional models and highlight the potential of LSTM models for accurate financial forecasting, especially in dynamic environments like stock trading.

Q. Brief Overview of the Practical Implementation (Text Description and a Few Images)

The practical implementation of this project involved multiple stages, including data preprocessing, building and testing LSTM models, and evaluating the models’ predictive performance. Here’s a breakdown of the key steps:

Data Preprocessing:

  • Tesla’s historical stock data (specifically ‘Close’ prices) was collected and cleaned.
  • The data was normalized using MinMaxScaler, which is crucial for speeding up the LSTM training process.
  • The dataset was then split into training and testing sets, ensuring proper chronological order for the time-series data.

Model Building:

  • Thirty different LSTM configurations were tested, focusing on adjusting layers, units, activation functions, and learning rates.
  • The final best-performing model consisted of three LSTM layers, each with 64 units and ‘tanh’ activation functions. The model also used the ReduceLROnPlateau and EarlyStopping callbacks to optimize training and prevent overfitting.

Training and Evaluation:

  • The models were trained with RMSE, MAE, MSE, and R² score as key evaluation metrics.
  • The best model achieved an RMSE of 0.0456 and an R² score of 0.944, demonstrating its high accuracy.

Q. Overview of Outcomes/Conclusions

The project successfully developed an optimized LSTM model that significantly outperformed traditional forecasting methods. The model was able to capture the temporal dependencies in Tesla’s stock price data and deliver highly accurate predictions. Some key outcomes include:

High predictive accuracy: The final model achieved a strong RMSE of 0.0456 and an R² score of 0.944, reflecting its superior performance in predicting Tesla’s stock prices.

Overcoming common challenges: Issues such as overfitting were effectively addressed using advanced regularization techniques and dynamic learning rate adjustments.

Despite the success, the study recognized the limitation of relying solely on historical data. Incorporating real-time data such as news sentiment and company-specific updates could further improve the model’s performance in capturing sudden market changes.

Q. Top Tips/Advice for Students Interested in completing a University BSc/MSc Degree:

Start Early: Begin your project as soon as possible, especially right after submitting your research proposal. This will give you ample time to explore different ideas, refine your methods, and address unexpected challenges along the way.

Be Proactive in Securing Data: Data accessibility can sometimes be a bottleneck. Make sure you identify and secure the necessary datasets early in your project, even if they require permissions or payments.

Iterate and Experiment: Machine learning projects, especially those involving deep learning models like LSTMs, benefit greatly from iterative experimentation. Small changes in hyperparameters can have a significant impact on model performance, so don’t hesitate to test various configurations.

Understand your Tools: Take time to thoroughly understand the libraries and tools you’re using. In my case, libraries like Keras and TensorFlow were vital for building LSTM models. Understanding how to efficiently use these tools sped up my development process.

Consult your Supervisor Regularly: Keep in close contact with your supervisor. Their feedback is invaluable, especially when it comes to refining your methodology and solving challenges related to your project. Stay Resilient: Research can be unpredictable. You might face challenges like overfitting, lack of data, or even model failure. The key is persistence and a willingness to adjust your approach as needed.

For further information about Computing courses at UWTSD, please click-here.

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MSc Project: Machine Learning (ML)

Student name: Sriskantharaja Mithushan

Course: MSc in Data Science and Analytics

Project title: A Comparative Evaluation of Machine Learning Techniques for Sales Forecasting

/\ Sriskantharaja Mithushan

Rationale: What was the reason/motivation for choosing the project?

The motivation behind choosing this project stemmed from the increasing importance of accurate sales forecasting in business decision-making. Companies rely heavily on predictive models to optimize inventory management, plan marketing strategies, and drive revenue growth. I was particularly interested in how different machine learning techniques could enhance the accuracy of these predictions, compared to traditional forecasting methods. My goal was to explore and compare the effectiveness of various machine learning models in improving sales forecasts, which could have a significant impact on business operations and profitability.

Machine learning (ML) is a branch of artificial intelligence (AI) and computer science that focuses on using data and algorithms to enable machines to learn from experience, much like humans do. In analyzing data, the ML algorithm, processes data multiple times to learn / adjust itself to improve accuracy.


Q. Brief overview of the practical implementation? 

The project was conducted in several stages, starting with data gathering, followed by preprocessing, model execution, and evaluation. A rich dataset was collected from various retail and e-commerce platforms, containing sales records, customer demographics, product categories, and revenue figures. This data was cleaned and transformed to handle missing values, normalize scales, and ensure proper formatting for model training.

Five machine learning models were implemented:

  • Random Forest
  • Support Vector Regression (SVR)
  • LightGBM
  • XGBoost
  • Gated Recurrent Unit (GRU) Neural Network

Each model was trained on the prepared dataset using Python, with libraries such as Scikit-learn, LightGBM, XGBoost, and TensorFlow.

After training, predictions from each model were compared to the actual sales data. Visualization tools like Matplotlib and Seaborn were used to graphically depict the performance of each model, with side-by-side comparisons of RMSE and MAPE metrics. These visualizations helped to highlight the strengths and weaknesses of the various models.

The Profit Over Time graph below, tracks monthly profit trends, providing a clear view of how profitability fluctuates over time. Key insights include: Seasonality and Profit Growth or Decline. For example Seasonality highlights periods of increased or decreased profits, often aligned with sales cycles or specific marketing efforts. This helps in identifying high-profit months and adjusting strategies for low-profit periods.

This visualization is crucial for understanding financial performance, aiding in strategic decision-making, and optimizing resource allocation for long-term profitability.

/\ Profit Over Time: Illustrates monthly profit trends.

This pairplot chart below, simultaneously shows the distributions (diagonal plots) and relationships (scatter plots) between the key variables: Sales, Quantity, Discount, and Profit. For example the scatter shows a strong positive relationship, confirming that higher sales lead to greater profits. A weak negative trend suggests that offering larger discounts may slightly lower profits. This pair plot provides a comprehensive overview of how these variables relate to each other and how each is distributed, helping in identifying trends, correlations, and potential outliers.

/\ Pairplot: Distributions and Relationships Between Sales, Quantity, Discount, and Profit.

The chart below is a Heat-map. The Heatmap shows the correlations between the key business metrics of Sales, Quantity, Discount, and Profit. The color intensity represents the strength and direction of the relationships, with darker colors indicating stronger correlations. For example, a deep hue between Sales and Profit highlights that as sales increase, profits rise significantly. This visual tool helps identify how these variables interact and guide strategic decisions on pricing, sales, and profit optimization.

/\ Correlation Matrix Heatmap: Sales, Quantity, Discount, and Profit

Q. Overview of outcomes/conclusions?

The project concluded with Random Forest emerging as the top-performing model in terms of prediction accuracy.

The research delved into the use of machine learning techniques for sales forecasting in the retail and e-commerce sectors, with the goal of identifying which models provide the most accurate predictions. The study examined five machine learning algorithms: Random Forest, Support Vector Regression (SVR), LightGBM, XGBoost, and Gated Recurrent Unit (GRU) neural networks. The models were evaluated using metrics such as Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE).

This study contributes to the understanding of how machine learning can be applied to sales forecasting in retail and e-commerce. It shows that tree-based ensemble methods, particularly Random Forest, are among the most effective techniques. However, deep learning models like GRU also show potential, particularly for capturing temporal dependencies. A balanced approach, combining multiple models and fine-tuning hyperparameters, can lead to more accurate sales predictions. By acting on these insights, retail and e-commerce companies can improve their forecasting accuracy, optimize inventory management, and ultimately enhance customer satisfaction and profitability. 

Q. Please share some top tips/advice for students?

Completing a Bachelor of Science (BSc) or a Master of Science (MSc) at a university like the University of Wales Trinity Saint David (UWTSD) can be a rewarding and challenging journey.

The University offers a variety of Computing courses. Ensure you choose a program that aligns with your interests and career goals. During both BSc and MSc, you’ll often have the flexibility to choose elective modules. Select modules that allow you to develop key skills that are in-demand in your field, or that attract your personal interest. Balancing lectures, labs, independent study, and personal commitments is crucial. Use digital tools to organize deadlines, assignment dates, and exam preparation to stay on track. The University also offer career services to help students prepare for employment. Take advantage of these CV workshops, interview practice, and employability training.

For further information about Computing courses at UWTSD, please click-here.

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Training & Selection for Team UK WorldSkills Squad

The international and prestigious WorldSkills competition started in the 1950’s and brings together skilled young professionals from over 80 countries. WorldSkills supports young people across the world via competition-based training, with national teams taking part and testing their ability against each other in a world-class standards ‘Skills Olympics’ every two years. The skills young people gain from taking part in the competition embed world-class training across the world and helps to increase jobs and economic growth.

Participants compete in over 50 diverse fields including IT Network Systems Administration (IT-NSA).

Image: WorldSkillsUK IT-NSA Team training & selection at SOAC UWTSD

The squad for WorldSkillsUK (in partnership with Pearson) IT-NSA competition is selected based on UK National Competitions. For IT-NSA we had around 6 squad members who were trained by experts over the year, testing their skills and benchmarking international standards participating in various International Competitions. Our Competitors over the last year competed in Euro Skills 2023 in Gdansk Poland and Asia Skills 2023 in Abu Dhabi, UAE. Considering the fact that the squad members are the best of the best from UK, only one out of them represent the UK in the upcoming WorldSkills Competition in Lyon, France 2024. 

Image: University of Wales Trinity Saint David (UWTSD)

Nitheesh Kaliyamurthy (Senior Lecturer from the School of Applied Computing (SOAC) at the University of Wales Trinity Saint David (UWTSD), took over as an Expert Training Manager for the WorldSkillsUK IT-NSA squad last year and has been involved in Training the squad. The School of Applied Computing at UWTSD, hosted various Technical Bootcamps in the year, starting with 1st Technical BootCamp in June 2023 for 3 days focusing on EuroSkills Test Project, 2nd Technical Boot Camp in November 2023 for 3 days focusing WorldSkills 2022 Special Edition Test Project and 3rd Technical Boot Camp for raising stars in the squad in January 2024 for 3 days. We also support IT NSA Squad for WorldSkills UK with International Standard Infrastructure enabling them to practice their Test Projects.

Image: School of Applied Computing (SOAC)

A WorldSkills Competition selection event to represent the WorldSkillsUK IT-NSA squad was hosted at SOAC-UWTSD last week with a 2-day Competition, where participants tested their skills in Microsoft, Linux, Troubleshooting aspects, Cisco and Infrastructure Automation.

Image: WorldSkillsUK IT-NSA Team training & selection at SOAC UWTSD

The competitors performed well. The competitors representing the UK squad will be announced later next month (April) during WorldSkills UK (in partnership with Pearson) Moderation Week. Intense training for that one competitor is planned over the coming months in May, June and July before they fly to Lyon, France in September 2024 to compete with other International Countries.

For more about WorldSkills please click-here. For information about Computing courses at UWTSD please click-here.

For inquiries related to WorldSkillsUK IT-NSA at UWTSD please contact Nitheesh Kaliyamurthy.

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