1. Core Module
Python and R programming are the most in-demand coding languages to solve analytics problems. This module will equip students with the ability to write customized solutions to inform business decision, integrate statistical libraries for data analysis, and construct visuals or reports for business understanding. It will provide students with individual hands-on practices to hone their coding skillset and opportunity to develop coding solution in a team. Student will learn to utilise modern development tools to turn information into insights, learn to understand the development environment of programming language.
The aim is to help students to build a holistic understanding on programming basics and the ability to write code independently.
In almost every form of business, we make decisions under uncertain business outcomes, risky payoffs, unknowable parameters, imprecise opinions and measurements. Most importantly, the elusive element called “chance” seems to influence all our decisions. A firm mastery of statistics and analysis methods will, thus, provide foundation to deal more intelligently in these situations. We will examine concepts, theory and methods of statistical applications, especially when applied in business situations. The emphasis will be on broad coverage of important statistical concepts, and sufficient depth to allow for creative applications of topics to new business situations. Ability to exploit these computational tools will lift the student’s ability to deal with more conceptually demanding problems in which complex modeling and analysis are required and precise calculations of formulas will be relegated to calculators and computer software. At the end of the course, students should cover fundamental probability and statistical concepts, theory, solution methods and application areas. Students will also learn to infer from the quantitative results to give a real-life interpretation of the calculated values, and how the results impact the decisions.
This course introduces participants to analyses, design, tools, techniques, and issues concerning the management of data. Data management and visualization is the foundation for organizations’ business analytics; and for problem solving and decision-making. Participants will analyze a variety of business process, identify data requirements and develop an information system to support business processes and gather information for business analytics. In this course, participants will have an opportunity to apply their design and development competencies by implementing an information systems prototype.
Upon successful completion of this course, participants will have gained competencies to identify and analyse business-related problems, to use appropriate technologies to resolve these problems, to evaluate alternative solutions, to make appropriate recommendations and to develop a prototype of their recommended solution.
Analytics involves the art of data exploration, visualization, communication and the science of analyzing large quantities of data in order to discover meaningful patterns and useful insights to support Business decision-making. Machine Learning provides an automated intelligent approach to surface insights from business data.
The primary objective of this course is to introduce students to various techniques available to extract useful insights from the large volumes of data. At the end of the course, students will not only see the substantial opportunities that exist in the business analytics realm, but also learn techniques that allow them to exploit these opportunities.
The modern organization has scores of data that are often under-utilized for business planning and market research opportunities. This course equips participants with the knowledge and skills, to investigate existing business operational data to continually develop innovative insights and new solutions for business decisions. Participants will be exposed to the process of engaging firms, assess data quality, developing business cases for analytics, and data journalism. The course will summarize classes of data modelling techniques using real-life business case data and showcase common applications for supply and demand forecast, pricing and profitability forecast, consumer trend analysis and the likes.
Overall, the course will introduce participants to approach problems data-analytically, envision data-mining opportunities in organizations, and also follow up on ideas or opportunities that present themselves.
AI and Big Data make analyzing businesses has become easier through the set of data tools such as Tensor Flow and Hadoop. AI is the most in-demand methodology to solve business problems today. This module will equip students with the ability to apply AI in areas like HR, Marketing, Operation Management, Business Law, Strategic Management. Student will learn to utilise modern development tools to turn information into insights, learn to understand the development environment of AI including cloud-based AI.
The aim is to help students to build a complete understanding on AI basics and the ability to apply AI in Business.
The aim of Project I & II is to create sharp business analysis to ensure they make the right decisions. These will enable students to harness the power of data science, big data, statistics and machine learning to optimize results and achieve strategic objectives. Students will work on real industry projects, obtained from their internship organizations, industry partners and organizations where students are working (for part-time students). Projects will be mentored by instructors and industry mentors.
Students’ ability to combine insights from mathematics, computer science and business with highly developed quantitative and communication skills will make them key to the success of any organization. The project will deepen students’ knowledge in these areas and give them the opportunity to specialize in computational intelligence, business process optimization and business management.
This course is to provide a broad understanding on how to manage data, the process of preparing data for analysis, basics of analytics, using AI to automate financial analysis process and generate accounting reports. This course will equip you with the ability to write customized solutions to make informed business decisions, integrate statistical libraries for data analysis, create AI models to automate accounting and financial process. This module will provide you with individual hands-on practices to hone your coding skills and opportunities to develop coding solutions in a team.
We utilize R and Python language as the medium of learning because it is one of the most in-demand coding language and its user-friendly syntax is well suited for the beginner level. You will utilise modern development tools to turn information into insights including Keras’ Deep Learning model, Google Brain TensorFlow, Hadoop, Spark and AWS.
Automated intelligent fraud detection techniques are now possible with Analytics and AI. The critical concepts and best practice in successful applications will be illustrated. The in-depth analysis of suspicious records and mitigation strategies can then be accomplished via Forensic Analysis.
From surveys, reviews, and loyalty programs, to in-store, online, and mobile transactions, marketers are being inundated with data about preferences and consequent choices of customers. Such voluminous data could enable customer analytics to offer profound insights for marketers that can be utilized to make and justify various business decisions. However, a greater volume of data does not always lead to a better decision, strategy, or market performance.
The ability to translate data into profitable outcomes necessitates various capabilities. This course will help students to (1) identify, explore, and refine raw data to derive basic insight and to generate a statistically analyzable dataset for Customer Analytics, (2) apply appropriate quantitative analyses depending on the business problems and corresponding datasets, and (3) draw valid inferences and meaningful implications from numerical results. The quantitative analyses will include brand and customer asset measurements, and STP (i.e., segmentation, targeting, and positioning) analysis to identify an optimal marketing mix as well as various regression approaches, and experimental designs to evaluate and design effective marketing campaigns. After completing the course, students should be able to exploit customer analytics extensively in diverse marketing activities.
Supply chain analytics is a set of approaches utilized to efficiently integrate suppliers, manufacturers, warehouses and retail stores and to efficiently manage material, information and financial flows so that merchandise is produced and distributed at the right quantities, to the right locations, and at the right time, in order to maximize system-wide surplus or value. In this course, students will learn analytics techniques (e.g. optimization, stochastic modeling, game theory) to leverage on the six drivers of facilities, inventory, transportation, information, sourcing and pricing in order to address the four supply chain challenges of complexity, uncertainty, dynamic environment and fragmented ownership.
This course introduces the key principles and techniques of decision modelling and analysis in the context of operations and optimization.
Such data-driven quantitative approach is grounded in management science which identifies the key objectives and variables for a decision problem, constructs a mathematical model to represent the logical relations among the objectives and variables, and uses the mathematical model and information gained from data analytics to systematically and critically evaluate decision alternatives and ways to optimize.
Lean operations refers to approaches that focus on the elimination of waste in all forms coupled with smooth, efficient flow of materials and information throughout the value chain to obtain faster customer response, higher quality and lower costs. Both manufacturing and service industries will be covered.