Professional Certificate in Applied AI for Educators

Course 1
Python Programming for Teachers

Python is ranked the top programming language both in the recent IEEE Spectrum annual ranking of the top programming languages and in the Popularity of Programming Language Index. Its popularity is driven by the vast number of specialised libraries available for it and its simple syntax which results in high code readability.

At the end of the course, learners will be able to:

• Write Python programmes and execute them using three different methods: Jupyter Notebooks, Python’s Integrated Development and Learning Environment (IDLE) and the command line.
• Import and use the Python standard and external libraries.
• Visualise 2D data using Matplotlib.

Course 2
Data Analytics for Teachers

In today’s rapidly evolving educational landscape, the ability to harness data effectively has become increasingly vital for teachers striving to optimise learning outcomes. This course offers a comprehensive exploration into the practical applications of data analytics within educational settings, equipping learners with the tools and knowledge needed to leverage data for informed decision-making and enhanced student engagement.

Learners will delve into various facets of data analytics, from understanding different types of educational data to mastering essential data analysis techniques. Learners will gain hands-on experience in collecting, cleaning, analysing, and interpreting data, which enables them to extract meaningful insights.

At the end of the course, learners will be able to:

• Master data collection and preparation techniques.
• Gain proficiency in descriptive, inferential, and predictive analytics methods, enabling them to identify patterns, make data-driven decisions, and predict student outcome.

Course 3
Machine Learning for Teachers

Machine learning has become a transformative force with profound implications across various industries, including education. This course is designed to empower teachers with the knowledge and skills to understand and harness the potential of machine learning, enabling them to introduce their students to this exciting field and prepare them for future careers.

Learners will get to explore the fundamentals of machine learning and its applications. From supervised and unsupervised learning techniques to model evaluation and real-world case studies, this course will provide learners with a solid foundation in machine learning concepts and equip them with practical skills to incorporate machine learning into teaching practices.

 At the end of the course, learners will be able to:

• Understand the fundamentals of machine learning and how it can be applied in education.
• Apply supervised and unsupervised learning techniques to analyse and interpret data.
• Evaluate and validate machine learning models using appropriate metrics and techniques.
• Explore real-world applications of machine learning through case studies.
• Apply machine learning techniques to a real-world dataset through a project.

Course 4
Natural Language Processing for Teachers

In today’s digital age, vast amounts of textual data are generated every day, presenting both opportunities and challenges for teachers. Natural Language Processing (NLP) is a powerful set of techniques that enables computers to understand, interpret, and generate human language, opening new possibilities for enhancing teaching and learning experiences.

 At the end of the course, learners will be able to:

• Understand the fundamentals of NLP.
• Preprocess and prepare text data for analysis.
• Represent text data using different techniques.
• Apply NLP techniques for text classification and sentiment analysis.
• Explore advanced NLP topics such as Named Entity Recognition and Topic Modelling.