Calculus plays an important role in the understanding of today’s world. This is an introductory course that presents the ideas and techniques of calculus using an informal, intuitive and geometric approach. Students need to learn both differential calculus and integral calculus and their applications to real-life situations in business, economics, the social sciences, natural sciences and visual arts. This course will equip students with the calculus background required for further study in many disciplines.

(Note: Students should take CDS1002 Calculus previously or take both this course and CDS1002 Calculus in the same term)

Probability and Statistics are fundamentals of quantitative analysis. This course introduces the basic theory of probability and statistics to students. It provides a strong mathematical background for students to understand the quantitative techniques used in the analysis of practical problems across different disciplines. Emphasis is placed on the underlying mathematical theories of probability and statistics. The topics covered include descriptive statistics, basic probability, probability distributions, correlation and the use of statistical software for data science.

(Prerequisite: CDS1003 Probability and Statistics 1 or BUS1102 Statistics for Business)

Probability and Statistics are fundamentals of quantitative analysis. This course covers statistical techniques for inferential statistics, such as estimation and hypothesis testing for population parameters. The topics covered include point estimate and interval estimate of population parameters, comparing means, non-parametric techniques and the use of statistical software for data science.

(Prerequisites: CDS1001 Introduction to Programming for Data Science)

Artificial intelligence is the study of intelligent agents. Due to the continued success of applying artificial intelligence to different challenging problems requiring high-level intelligence, there is an explosive interest in this field for scientists, dreamers, entrepreneurs and educators. This course is designed for students to understand and appreciate the basic principles of artificial intelligence. It covers computational intelligent systems, which can support decision making, interact with humans, navigate vehicles and achieve many other interesting and useful tasks. These intelligent systems are extremely useful in business, science, the humanities and other fields.

This course focuses on basic Linear Algebra used in econometrics and in social research. It will cover some basic techniques of matrix arithmetic and algebra, and in particular matrix multiplication and solution of linear equations.

View Course Details at https://www.ln.edu.hk/econ/upload/ln_econ/download/ssc2113%20course%20outline%20202009.pdf

(Prerequisites: CDS1003 Probability and Statistics 1 or BUS1102 Statistics for Business)

Organisations often need to make decisions in their best interests in various situations. This course provides an introduction to modelling, analysing and solving decision problems under certainty and uncertainty. Students will develop the concepts of spreadsheet modelling, optimisation and uncertainty, and learn how to apply quantitative techniques of optimisation and decision-making under uncertainty and forecasting to solve a variety of problems that arise in business settings. Microsoft Excel will be used to model and solve most of these problems. Through practising these techniques and Excel functions, students are expected to develop basic analytical skills and managerial insights, and also acquire competency in Excel.

(Prerequisite: CDS2003 Data Structures and Object-Oriented Programming)

This course is designed to introduce the concepts and principles of database and data warehouse. Various types of databases will be thoroughly discussed in this course, such as objected-oriented, relational, document-oriented, graph, NoSQL, and New SQL. Popular database management systems such as Microsoft SQL Server and/or Oracle will be described and implemented. Topics include data models (ER, relational, and others); query language (Structured Query Language); implementation techniques of database management systems (index structures and query processing); management of semi-structured and complex data; distributed and NoSQL databases; the dimensional modeling technique for designing a data warehouse, and data warehouse architectures, OLAP and the project planning aspects in building a data warehouse.

(Prerequisites: CDS1003 Probability and Statistics 1 or BUS1102 Statistics for Business or CLD9003 Statistics in Modern Society)

This course is designed for Data Science students. They are required to have previous knowledge of probability and statistics. Simulation is a commonly used and practical technique for modelling and analysing real operating systems to make more effective decisions. Examples of such systems include transportation, supply chain networks, job flow, airports, banks, ocean terminals, information systems, emergency response systems and human behaviour in a social network. Due to the considerable complexity of real systems, many people and organisations find it difficult to investigate human activities, manufacturing and service designs and processes without a computer simulation model. More than 89% of Fortune 500 firms have used simulation for system improvement and have experienced the benefits for a long time. This course is designed to introduce basic concepts of system modelling and computer simulation. The process and methodology of using simulation for problem solving and decision making are emphasised. Moreover, the course assignments involve the use of the simulation language “Arena”. Some of the assignments may require the ability to write computer programs in a language of the student’s choice or the use of a spreadsheet.

(Prerequisites: CDS2001 Probability and Statistics 2, CDS2002 Introduction to Artificial Intelligence and SSC2113 Linear Algebra)

This course is designed to introduce methods in machine learning (i.e., the study of how to create a machine that is capable of improving its performance in some tasks, such as perception, cognition and action, by learning from experience and data). This makes computers more intelligent. Machine learning is a powerful tool for recognising hidden and potentially useful relationships in data. It is essential in many organisations, as they can generate huge amounts of data in their daily operations and machine learning enables them to discover important knowledge from this data. The students will learn some powerful software packages for performing machine learning and apply these packages to handle different kinds of problems.

(Prerequisite: CDS2002 Introduction to Artificial Intelligence)

Data mining is an important component of data science that discovers knowledge from huge databases. Data mining is an interdisciplinary field, integrating statistics, pattern recognition, neuro-computing, machine learning and databases. It is also one of the fundamentals to extracting interesting knowledge (domain-specific rules, patterns, constraints and regularity). Students will learn the basic principles and core ideas of data mining. This course also covers many data mining approaches to discovering knowledge from a vast amount of valuable databases in business, finance, urban and medicine. Quantitative analytical skills are taught to interpret data mining models. Current IT skills are also covered in the course.

(Pre-requisite: CDS1003 - Probability and Statistics 1 or BUS1102 - Statistics for Business)

Marketing is evolving from an art to a science. Many firms collect massive information about consumers’ choices and how they react to marketing campaigns, but few firms have the expertise to intelligently act on such information. This course introduces students to state-of- the-art marketing analytics and demonstrates how to practically apply these analytics to real- world business decisions. The purposes of this course are to (1) present various marketing analytics that aid students in interpreting data from various sources, such as customer transactions and social media, (2) introduce some commonly used marketing analytics, such as RFM, Customer Lifetime Value and Sentiment Analysis, and (3) enable students to gain the competency to interact with and manage a marketing analytics team.

View Course Details at https://www.ln.edu.hk/mkt/courses/mkt3006/mkt3006cs.pdf

(Prerequisite: CDS3001 Databases and Data Warehouses)

Companies need to have more efficient systems to support their decision-making. This is critical to their competitiveness, and has become a critical component of their business strategy. Generally, large organizations have a vast and complex landscape of systems. More often than expected, such companies have multiple and silo-based business systems to support their operations, finance, accounting, and analytical business processes. As the scale of systems in an organization’s landscape increases, the complexity for managing the underlying data becomes even more complicated. This course covers the essential concepts, options and best practices for data administration, data protection, privacy control, user security and management, system configurations and data activity and status monitoring. The focus is on issues and principles of managing organizational data. Students will get extensive experience in developing data models, creating relational databases, and formulating and executing complex queries.

(Prerequisites: CDS3001 Databases and Data Warehouses and CDS3004 Data Mining)

This course provides an opportunity for students to integrate their knowledge obtained in other courses in a data science process that involves the preparation, analysis, reflection and dissemination of data in a chosen application setting. The emphasis is on the management and execution of a well-defined project of a suitable scale. Such projects may involve either real-world or experimental data and students may engage in such projects individually or in groups.

(Prerequisite: CDS3004 Data Mining)

Social networking is a major Web 2.0 application. Web technologies, such as JavaScript, not only enrich the user experience of social networking applications, but also handle the collection of data for Web analytics and social network analysis. This course introduces the major Web technologies for rich user experiences with an emphasis on how they also support data collection in social networking applications. Students will learn about the ethical use of these technologies in relevant applications, such as social media marketing.

(Prerequisites:

(a) ECO2101 Introduction to Economics, or Level 3 or above in HKDSE Economics, or BUS2105 Microeconomics for Business, and

(b) ECO3105 Introduction to Statistics and Econometrics (from 2019-20)/Introduction to Econometrics (in 2018-19 or before) (recoded from ECO3101 from 2017-18), or ECO3104 Quantitative Methods in Economics (recoded from ECO3102 from 2017-18), or BUS1102 Statistics for Business), or CDS1003 Probability and Statistics I)

This course introduces students to the art and science of econometric modeling as applied to economics, business, sociology, and all other branches in social sciences. Students could easily apply it to many other areas such as the natural sciences. They are going to learn how to develop, statistically test, and apply econometric models. This course emphasises intuitive understanding of econometrics, as opposed to a more abstract and theoretical approach. Hands-on experience with econometric computer software is an important part of this course.

(Remark: It is preferable for students to have taken CLD9024 Mapping Our Changing World before taking this course.)

Adopting location intelligence by analysing location-based data using a geographical information system (GIS) in business is becoming a success strategy in the competitive global economy. This course introduces students to how location-based data and information can facilitate business professionals to extract, analyse, interpret and visualise business data in maps. The traditional means of business analytics usually present results in spreadsheets, bar graphs or simple charts. Maps allow business professionals to reveal the underlying proximity relationships and trends in their data. With advances in the information and communication technology (ICT), the location-based platform of GIS is instrumental in fostering smart city initiatives to addressing issues of unplanned urbanization for sustainable urban development and better quality of life. Case studies of location-based services and GIS application examples are elaborated.