Students will be required to take all the SEVEN required courses plus THREE of the elective courses as listed below, i.e. 30 credits in total for the graduation requirements. All of the following courses are three credits each.
The aim of this course is to introduce students to the fundamentals of Python, a general-purpose programming language widely used in the application of Data Science, Big Data Analytics and Optimization to business problems. The course will provide students the skills for implementing your own algorithms as well as using the thousands of Python packages available for data analysis like modelling and decision support. The lab classes will provide opportunity for students to practice their programming skills and obtain formative feedback. The course is focused on practical knowledge, examples and real-world applications for data analytics. The course is very much hands- on with the ultimate goal of turning students into a versatile data analyst for real-world applications.
This course provides a comprehensive overview of statistical concepts, models, and data analysis techniques essential for data science applications. Students will gain practical skills in exploratory data analysis, statistical modeling, machine learning algorithms, and communicating analytical insights. Programming in R or Python will be emphasized to implement methods. Through case studies and hands-on projects with real data, students will learn how to apply statistical thinking and programming skills to extract meaningful information, make data-driven predictions and decisions, and effectively communicate results to stakeholders.
This course is designed to describe the advanced concepts and principles of data management for business. Various types of databases will be discussed in this course, such as objected-oriented, relational, document-oriented, NoSQL, and New SQL. Popular database management systems such as Microsoft SQL Server and/or Oracle will be described. Topics include data models (ER, relational, and others); query language (Structure Queries Language); management of semi- structured and complex data; NoSQL databases. It also covers the essential concepts, options, and best practices for data administration, data protection, privacy control, user security and management, and system configurations. It addresses topics about the general concepts of data disaster recovery, planning, and procedures.
This course teaches the core principles and ideas of data mining. It covers a range of data mining approaches used to extract knowledge from vast amounts of valuable databases in diverse fields such as business, finance, urban planning, and medicine. Data mining techniques such as classification, clustering, association rules will be covered. In addition, advance topics such as sequential data mining, graph mining and social network analysis will also be covered. Furthermore, students will develop quantitative analytical skills to interpret data mining models. By the end of the course, students will have a comprehensive understanding of the theory and practice of data mining and will be equipped with the necessary skills to extract valuable insights from databases.
This course provides students the chance to demonstrate innovative abilities and initiatives in data science problems. Students will be required to carry out independent work on a major project, which can be theoretical or practical, under the supervision of individual staff member. The course develops the capability to integrate and apply data science knowledge and data analytical skills to different scenarios. The course also serves as a platform of presenting and sharing novel investigations of academic and/or industrial problems in real-world via data science knowledge.
Artificial intelligence (AI) is a new technical science that studies and develops theories, methods, techniques, and application systems for simulating and extending human intelligence. AI techniques and models have been widely employed in various domain-specific applications due to their promising performance compared to conventional methods. This course focuses on fundamental concepts, techniques, and potential business applications of artificial intelligence. The course provides an overview of waves of AI, intelligent agents, problem-solving, planning, reasoning, learning. It includes topics about search, logic, genetic algorithms, computational learning methods, and some potential business applications like expert systems, news analysis, and so on.
Machine learning is a branch and one of the most popular AI techniques in recent years. Machine learning models and techniques have been widely used in many fields, such as natural language understanding, machine vision, and pattern recognition. This course will introduce the concepts, techniques, and business applications of machine learning. The course will cover the supervised, semi-supervised, unsupervised, transfer, and reinforcement learning paradigms. The techniques include regression, probability generative model, logistic regression, neural networks, support vector machine, Q-learning, and so on. The business application examples of these courses will be included and introduced in this course.
This course introduces fundamental concepts and design principles in cybersecurity as well as highlight different methodologies of protecting information and data in the cyber world. Topics include CIA (Confidentiality, Integrity, and Availability); introduction to security; cyber-attacks and threats; cryptographic algorithms and applications; network security and infrastructure.
With the rapid development of cloud resources, majority of traditional data centers has been replaced by cloud platforms (such as Amazon Web Services, Microsoft Azure, Google Cloud Platform) due to the limited bandwidth of resource. A qualified data scientist needs to equip with cloud computing skills, learning to perform a series of tasks in the data pipeline on the cloud such as data acquisition, data cleansing, data transformation and data mining, as well as model training and testing. This course aims to examine the latest trends of cloud computing and provide students with the fundamental knowledge of cloud computing. Students will learn the best practices in deploying and implementing cloud computing applicable for unique business requirements. The course discusses the conceptual topics of cloud technologies and provide hands-on experience through projects utilizing public cloud infrastructures. Topics include cloud delivery models (SaaS, PaaS, and IaaS); Cloud computing overview; Public cloud infrastructure, On-demand self- service, and resource pooling; rapid elasticity; measured service; cloud storage architecture (data distribution, durability, consistency, and redundancy); data deduplication; cloud security issues; case studies of current cloud computing platforms.
This course is an introduction to Natural Language Processing (NLP). It covers a brief overview of the field, including the cutting-edge text processing tasks (e.g., text summarization, named entity recognition, document classification, etc.), their computational problem setting and general thoughts of methodologies. State-of-the-art techniques will also be discussed, including generative sequence-to-sequence models, multimodal data modelling (e.g., image-to-text, video/audio-to-text), chatbot, question- answering system, topic modelling, etc.
This course will introduce the techniques for visual data processing and analysis. Topics include image processing and analysis in spatial and frequency domains, image restoration and compression, image segmentation and registration, morphological image processing, representation and description, feature description, face recognition, iris recognition, fingerprint recognition, image analysis topics, such as medical image analysis.
This course introduces operations management in real-world situations (e.g., manufacturing and service industries). Students will learn how to design, operate, and improve processes to increase efficiency and effectiveness. The course covers key topics such as process design, capacity planning, inventory management, quality control, and supply chain management. Students will learn the importance of operations management, as well as the various techniques and strategies used to optimize processes and improve organizational performance.
This course introduces the fundamental visualization techniques to transform complex data sets into understandable and insightful visual representations for the purpose of data storytelling. The curriculum spans across a range of topics, including the design principles, human visual perception, open source visualization tools, visualization techniques for CT/MRI data, computational fluid dynamics, graphs and networks, time-series data, text and documents, Twitter data, and spatio-temporal data. The course adopts a hands-on approach, incorporating practical exercises using popular data visualization tools like Tableau, PowerBI, and D3.js. It also emphasizes the importance of data preparation and cleaning, ensuring students understand the entire data visualization process from data collection to final visualization. Throughout the course, students will be tasked with creating their own data visualizations, culminating in a group project where they will present a data story using the skills learned.
This course examines the intersection of social behavior and computing technology. Students will learn techniques for collecting, analyzing, and visualizing social data through areas like network analysis, data mining, and information visualization. The course covers computational tools to study social structures, dynamics, communities, and information diffusion at scale. Students will consider ethical issues in social data as they apply these techniques through programming assignments and a social network project. The course also explores user-centered design of social platforms and applications. Students will critically examine popular social systems while gaining practical experience building social interfaces and applications.
Mobile Edge Computing (MEC) is an emerging technology that extends cloud capabilities to the network edge. This course will introduce students to the key concepts, architectures and enabling technologies of MEC. Students will learn about the motivation for MEC and challenges it addresses in mobile cloud computing. Fundamentals of MEC frameworks and deployment models will be covered. Enabling 5G networking and edge infrastructure technologies facilitating MEC will be examined. Applications such as IoT, augmented reality and smart cities will also be explored. Techniques for offloading tasks, computation partitioning and managing resources at the edge will be studied. Topics including edge artificial intelligence and data processing frameworks will be discussed. Security, privacy preservation and open source/industry edge platforms will be topics of focus. Students will gain an understanding of MEC frameworks for developing low latency applications. Through this practical course, students will learn the foundations of the impactful new Mobile Edge Computing field.
Organizations often need to make decisions in their best interests in different situations, and Microsoft Excel is one of the most popular software that business people use to assist their decision making. This course introduces commonly used quantitative analysis techniques that facilitate scientific and systematic decision making. Students will learn how to employ appropriate decision making techniques to obtain the best solutions for a variety of business problems, and learn about the best-practices of spreadsheet modelling for clarity and communication. Through practicing these techniques and Excel functions, students will develop analytical and computer-based problem-solving skills, which can help them improve their performance at work or in daily life.
Deep learning is one of the bleeding-edge technologies of machine learning. It is a neural network used to establish and simulate the human brain for analytical learning and to interpret data by imitating the mechanism of the human brain. Deep learning is widely used in computer vision, speech recognition, natural language processing, and other fields. This course aims at providing an intensive understanding and hands-on experience of the existing deep learning approaches. The topics will cover how to select deep neural networks, how to design deep neural networks, and how to train and optimize the neural networks for practical applications. The course will cover deep neural network models, including convolutional neural networks, recurrent neural networks, long short-term memory networks, deep residual networks, generative adversarial networks, attention-based models, adversarial learning models, and training techniques including dropout, batch normalization, selection of activation functions and so on. TensorFlow, Pytorch, or other state-of-the-art deep learning tools will be introduced and applied to solve different classes of problems with huge datasets in business domains.
This course provides an understanding of the concept and challenge of big data. The focus is on the data analytic techniques to tackle the V’s (volume, velocity, variety, veracity, valence, and value) in big data and how these impacts data collection, monitoring, storage, analysis and reporting. The following topics across the big data domain will be introduced: distributed file systems; big data analysis techniques; high-performance processing algorithms for big data; big data search and query technologies. An example (Apache Spark) of big data management system to manage and process large-scale data is introduced in the course. Big data analytics applications in business will also be elaborated. Students will actively participate in the delivery of this course through assignments, portfolio development, and projects.
Blockchain, as a decentralized open ledger, has proven to be a phenomenal success. This ground-breaking technique holds a huge promise in various fields, digital identification, data marketing, cryptocurrencies like bitcoin, etc. This course introduces students the fundamentals of blockchain, distributed ledger technology, alternative consensus, smart contracts and security, and cryptocurrencies. Case studies of cryptocurrencies and examples of application (e.g., Bitcoin) will be also elaborated. Students will understand the impact of blockchain technologies on financial services and other industries through assignments and projects.
Healthcare analytics transform the traditional medical system in an all-round way, making healthcare more efficient, more convenient, and more personalized. This course will introduce student the key technologies that support smart healthcare. It explains how to build the surveillance infrastructure and how the data is collected and transmitted back from various wearable sensors of multiple sources, by using the technologies of Internet of Things (IoT): MAC protocols, routing protocols. This course will also describe data fusion of health and healthcare data, data models, data management, machine learning algorithms, and analytics techniques and tools for health risk prediction. Case studies and examples of application will be elaborated in this course.
This course is about geographic foundations of location intelligence and geographical information system (GIS). The contents cover how GIS facilitate geospatial data analysis and communication to address complex geographic concepts or problems. Understanding how location analytics and technology could practically support business professional to analyse geospatial data from multiple sources and create location intelligence, to empower understanding, insight, intelligent decision making and prediction. Cutting edge topics and applications of location analytics in business will be introduced. The ethical, legal, and societal issues in the field will also be reviewed and addressed. This course combines classroom teaching and hands-on tutorial to learn GIS analytical skills by practice.