Introduction

The course materials have been crafted on the Rise platform, a part of Articulate 360, renowned for its ability to create responsive, engaging, and interactive e-learning courses. As you embark on this educational adventure, you should discover content that adapts seamlessly to any device, ensuring that whether you're on a desktop at home or on your smartphone on the move.

Rise courses are designed with the learner in mind, combining intuitive navigation with a variety of multimedia and interactive elements to enhance your understanding and retention of the material. From interactive quizzes to compelling videos and dynamic animations, every lesson is structured to engage you deeply with the content, making learning not just effective but hopefully enjoyable, too.

Course Requirements

To ensure a productive and enriching learning experience, participants are expected to meet the following requirements:

• Technical Requirements: Access to a computer or mobile device with internet connectivity, capable of running data analysis software and tools used throughout the course. 

• Time Commitment: Willingness to dedicate approximately 24 hours of study time, split between online learning modules and live sessions.
• Online Engagement: Willingness to engage with interactive materials, seminars, workshops, and questionnaires.
• ZOOM: If working from a mobile phone or tablet, you may want to install the Zoom App from your app store. It is readily available for Apple and Android. You can also download Zoom as a plugin for Google Chrome and Firefox web browsers on laptops and PCs. Zoom Downloads. 

Learning Outcomes

By the end of the programme, participants will be able to:

1. Understand Fundamental Concepts: Grasp the core principles of data science and artificial intelligence, including their history, current applications, and future potential.
2. Apply Data Analysis Techniques: Employ statistical methods to analyse and interpret data, using both descriptive and inferential statistics to derive meaningful insights from datasets.
3. Recognise Data Visualisation Tools: Utilise data visualisation techniques and tools to effectively communicate data insights through graphs, charts, and interactive dashboards.
4. Recognise AI in Practical Applications: Apply AI techniques to real-world problems, demonstrating the ability to design and implement AI solutions in various industries such as healthcare, finance, and technology.
5. Embrace Ethical AI Practices: Recognise the ethical implications of data science and AI, including privacy, security, and fairness, and apply ethical considerations in the development and deployment of AI systems.
6. Engage in Effective Problem-Solving: Develop critical thinking and problem-solving skills using a data-driven approach to address challenges and make informed decisions.
7. Collaborate and Communicate: Work effectively in teams to tackle projects and communicate complex data science and AI concepts.

Additional Skills

Participants will also acquire soft skills and practical experience through the program's structure, including time management from the self-paced online components and teamwork and networking opportunities through the face-to-face sessions. These outcomes ensure that graduates of the programme are well-equipped not only with theoretical knowledge but also with practical skills and ethical considerations to navigate and contribute to the fields of data science and AI effectively.

Is there a simple algorithm for intelligence?

Michael A.Nielsen, “Neural Networks and Deep Learning”, Determination Press, 2015

http://neuralnetworksanddeeplearning.com/sai.html