Professional Diploma in Web-Based Applied Data Science for Industry

To get the most out of this course, students will need a personal laptop or PC for hands-on projects. It is generally recommended that you have a basic familiarity with HTML, CSS, and JavaScript. This foundational knowledge will help you smoothly transition into designing and building modern web pages for data analysis.

Additionally, a basic comfort level with general mathematical concepts will support your learning during the data science modules. These skills will come together in the final weeks as you learn to integrate predictive models into your web projects, allowing you to build and deploy functional, data-driven applications.

The course module is designed as follows:

Week 1: Introduction to Web Development

This part of the course introduces how the web works and how information is delivered over the internet. It covers HTML basics, which are used to structure web pages. Simple web pages are created to understand how content is organised and displayed in a browser. It also explains how web technologies are applied in real industry systems such as websites and online platforms.

Week 2: Styling Web Pages with CSS

This part of the course introduces CSS fundamentals, which are used to style and design web pages. It covers layouts and responsive design to ensure web pages adapt to different screen sizes and devices. Navigation menus are included to show how users move through a website. It also focuses on building professional-looking web pages with a clean and consistent design, with generative AI tools used for assistance.

Week 3: Interactive Web Development with JavaScript

This part of the course introduces JavaScript basics, which are used to add interactivity to web pages. It covers variables, functions, and events to support dynamic behavior in applications. Form validation is included to ensure correct user input and improve data quality. It also focuses on building interactive user interfaces, with generative AI tools used for assistance.

Week 4: Data-Driven Web Applications

This part of the course introduces python-based library for building web applications using Python. It covers how web pages can be connected with Python to enable backend functionality. User input and output are included to understand how data is processed and displayed in applications. It also focuses on building a simple web application that combines frontend and backend components.

Week 5: Data Analysis Fundamentals

This part of the course introduces Python for data analysis and its role in handling real-world datasets. It covers working with Pandas for data manipulation and exploration. Data cleaning and preparation are included to ensure datasets are accurate and usable for analysis. It also introduces basic statistics to summarize and understand data effectively.

Week 6: Data Visualization and Insights

This part of the course focuses on exploratory data analysis (EDA) to understand patterns and structure in datasets. It includes creating charts and graphs to visualize data clearly and effectively. Identifying trends and patterns is used to extract meaningful insights from raw data. It also emphasizes communicating insights in a clear and understandable way.

Week 7: Introduction to Machine Learning toolkits

This part of the course introduces the basics of machine learning and explains what machine learning is and how it is used in real-world applications. It covers supervised learning concepts, including how models learn from labeled data. Training and testing datasets are used to build and validate predictive models effectively, along with basic model evaluation techniques. It also introduces machine learning toolkits such as Scikit-learn for building simple regression and classification models using pre-built libraries.

Week 8: Web-based project I

This part of the course introduces the basics of machine learning and explains what machine learning is and how it is used in real-world applications. It covers supervised learning concepts, including how models learn from labeled data. Training and testing datasets are used to build and validate predictive models effectively, along with basic model evaluation techniques. It also introduces machine learning toolkits such as Scikit-learn for building simple regression and classification models using pre-built libraries.

Week 9: Web-Based project II

This part of the course introduces a more advanced project using Flask or Django to build a full web application for predictive data analytics. It focuses on developing a backend system that connects data processing and machine learning models with a web interface. Users can input data through the application and receive predictions or analytical results in real time. The project demonstrates how to deploy a complete end-to-end data-driven web solution using industry-style tools.

Week 10: Final Project Completion and Presentation

This part of the course focuses on testing and deployment to ensure the application works reliably in real-world conditions. It includes documentation and a project presentation to clearly explain the work and its outcomes. It also highlights industry applications of the project and explores relevant career pathways in web development and data science.

Learners will be evaluated through two continuous assessments designed to build upon each other. The first is a mid-term project due at week 6, and the second is a final data-driven project due within 3 to 4 weeks of completing the course.

• Assessment One (Due Week 6): Learners will design and develop a fundamental, responsive website consisting of a Home, About, and Sign-Up page. This project focuses on mastering clean layout structure and core logic using HTML, CSS, and basic JavaScript for page interactivity.

Assessment Two (Final Portfolio): Building on their front-end skills, learners will develop an advanced, web-based predictive data analytics application. This platform will integrate modern front-end packages and libraries to process data, run predictive models, and visually display analytics results in a professional format. One section of this application will include a reflective report mapping out future technical enhancements, showcasing the learner’s capacity for independent growth

Why Web-Based Applied Data Science for Industry?

• Attractive Entry Salaries: Graduates entering the tech workforce typically secure junior roles with a strong starting salary range of €30,000 to €40,000 per year.
• Significant Career Growth: With industry experience of over 5+ years, professionals can expect their earning potential to climb up to €60,000 or more annually.
• Rapid Job Market Growth: The demand for development and data skills assisted by generative AI will grow consistently in the coming year.
• Accessible Career Pivot: A formal computer science degree is no longer mandatory; employers heavily favor practical skills and portfolios, offering high career flexibility