In this unit, students learn about the conceptual foundations of relationship marketing and customer relationship management. We will then explore the different types of website, social media and search engine big data that marketers have to work with, focusing on the 5Vs of big data; volume, velocity, variety, veracity and value. This will enable students to develop practical skills in using big data for marketing.
Students are able to (1) critically reflect on the role big data plays in marketing, organisations and society; (2) critically reflect on the theories underpinning the use of big data in marketing; (3) exercise judgement to recommend solutions around the integration of big data in marketing; (4) use marketing analytics on big data to build customer relationships; (5) demonstrate competencies to work effectively in diverse teams; (6) demonstrate the ability to give oral presentations in a clear and coherent manner; and (7) demonstrate the ability to produce clear and concise written communication.
In this unit, students learn about search engine marketing, which is one of the most prominent marketing approaches today. The unit covers search engine optimisation (SEO) and paid search advertising, and balances theory and practice. Students will work with Google Ads to plan an actual search engine marketing campaign for a local organisation.
Students are able to (1) critically reflect on the role search engines play in marketing, organisations and society; (2) critically reflect on the theories underpinning the use of search engines in marketing; (3) exercise judgement to recommend solutions around the integration of search engines in marketing; (4) utilise search engines to communicate to a range of stakeholders; (5) demonstrate competencies to work effectively in diverse teams; and (6) demonstrate the ability for clear and coherent oral and written communication.
IIncreasingly, businesses are pursuing impactful and productive applications of AI to their business processes, offerings, and strategies. Examples include Netflix’s content recommendation system, Danske Bank’s fraud detection system, and Facebook’s use of image detection and object classification, Burberry’s approach to personalisation, and Google’s self-driving car initiative. All these applications have been made possible by recent and significant breakthroughs in artificial intelligence. Briefly, the methods of machine learning are the traditional cornerstone of predictive analysis. These methods, and especially recent applications of artificial neural networks have led to the development of deep learning and the emergence of “smart” AI (broadly defined as unsupervised learning). The purpose of this course is to provide students with in-depth coverage of the methods and models of deep learning, with an emphasis on recent advances in the application of artificial neural networks (including the use of estimation software).
In this course, students will learn the fundamentals of deep learning and gain hands-on experience with Convolutional Neural Networks (CNNs) using Keras with TensorFlow. They then apply their knowledge to real-world problems such as object identification, classification, and movement prediction for self-driving cars. Students also explore data augmentation techniques to deal with training data shortages and delve into fraud detection with Autoencoders. Towards the end of the course, students learn about Natural Language Processing and apply their knowledge to text generation using Recurrent Neural Networks (RNNs) and sentiment analysis with word embeddings. R programming language will be used for this course.