Pre-Master Bootcamp

Course Overview

Differential equations and linear algebra are fundamental mathematical tools for engineers and applied scientists. This course establishes a solid foundation in both areas, providing comprehensive coverage of topics essential for students in engineering, technology, and related fields. Key topics include:

• First-order differential equations.

• Linear equations, matrix algebra, and determinants.

• Vector spaces.

• Linear second-order differential equations.

• Theory of higher-order linear differential equations.

• Matrix methods for linear systems.

Course Overview

This course is a systematic introduction to artificial intelligence, carefully designed for non-technical professionals. It builds a complete cognitive framework spanning the foundational principles of AI through to its cutting-edge applications. In an era where AI is reshaping every field, this course serves as an essential foundation — whether you are a product manager developing AI-driven products, a decision-maker leading enterprise AI transformation, a professional seeking career advancement, or a parent planning for your child’s future. We do not aim to turn you into an algorithm engineer. Instead, we help you genuinely understand the underlying logic of AI:

• The technical evolution from machine learning to large language models.

• Practical skills from prompt engineering to context engineering.

• Clear judgment on current applications and future trends.

This knowledge system will equip you with the ability to engage confidently with technical experts, make well-informed AI decisions, and build the foundation to continuously evolve in the age of intelligence. AI is not an elective — it is a required course for everyone in this era. Systematic learning is the only path to avoiding the pitfalls of fragmented information.

Course Overview

This course focuses on the application of Python and R for business analysis. The goal is to develop a working knowledge of how to extract knowledge and insights from data. Topics include basic programming syntax, web scraping, relational databases, data analysis, text mining, machine learning, neural networks, and artificial intelligence.

Learning Objectives

• Set up and navigate the integrated development environment (IDE) for Python and R.

• Understand fundamental programming concepts and logical thinking.

• Load and store data from files (CSV, HTML, JSON, XML) and manage data using relational databases (MySQL).

• Apply data standardization and cleaning techniques to reduce redundancy and anomalies.

• Use statistical packages for data analysis and visualization.

• Apply text mining techniques including tokenization, word frequency analysis, named entity recognition, sentiment analysis, and topic modeling; understand the role of large language models (e.g., ChatGPT) in business analytics.

• Distinguish between supervised and unsupervised machine learning approaches.

• Understand common machine learning algorithms: linear regression, polynomial regression, decision trees, random forest, KNN, and K-Means clustering.

• Understand the fundamentals of network analysis and neural network algorithms.

• Apply business analytics techniques to real-world problems through a Capstone Project.

Course Overview

This course develops a solid understanding of core statistical tools and their application across a wide range of business and economics contexts. By the end of this course, students will be able to:

• Compute and interpret basic descriptive statistics.

• Understand foundational concepts in probability and apply elementary probability techniques.

• Apply statistical inference methods, including estimation and hypothesis testing.

• Work with measures of statistical association, including correlation and regression analysis.

• Use Excel as a tool for statistical analysis.

Course Description

This course equips non-accounting students with essential financial and managerial accounting skills. It covers the preparation and interpretation of the three key financial statements — Income Statement, Balance Sheet, and Cash Flow Statement — under both GAAP and IFRS. Students will learn to analyze financial performance using ratio analysis and apply managerial tools such as financial budgeting and variance analysis to support internal decision-making and strategic planning

Course Overview

AI tools are reshaping how businesses approach supply chain management and marketing decisions. This course explores both domains and examines how AI can complement professional expertise to drive better, faster decision-making. The first half of the course covers supply chain management, focusing on demand forecasting, inventory optimization, and supply chain risk management. Students will compare traditional statistical methods with AI-driven approaches, assess their business impact, and explore the practical requirements for successful AI implementation. The second half focuses on marketing. Through hands-on projects, students will use AI tools to conduct market research, analyze consumer data, extract actionable insights, and develop marketing plans and content. The course also features a guest lecture by a finance professor on machine learning in investment management, offering students a broader perspective on AI applications across business functions. Assignments are grounded in real business cases, requiring students to apply AI tools to solve practical problems. No programming experience is required, though a basic understanding of business fundamentals is expected.

Course Overview

This course develops students' technical communication skills for both academic and professional contexts. Students will write and edit technical documents, create graphs and visual data displays, and present technical information clearly and effectively.

Learning Objectives

• Apply the principles of effective technical writing to produce clear, well-structured documents.

• Design and present data visualizations that communicate information accurately and concisely.

• Deliver effective technical presentations to professional and academic audiences.

• Develop collaborative communication skills through teamwork and peer review.

Course Overview

This course introduces three foundational areas of applied physics essential to engineering: Fluid Mechanics, Electromagnetism, and Thermodynamics. Through a combination of theoretical instruction and practical application, students will develop a deep understanding of core physical principles and their relevance to engineering systems.

Fluid Mechanics

• Understand fundamental fluid properties including viscosity, compressibility, thermal expansion, surface tension, and capillary phenomena.

• Analyze fluid statics: pressure distribution, force balance, and interactions between static fluids and solid boundaries.

• Apply fluid kinematics concepts: flow fields, material derivatives, and system derivatives.

• Analyze pipe flow and hydraulic systems, including laminar and turbulent flow and resistance characteristics.

Electromagnetism

• Understand static electric fields: charge, Coulomb’s law, electric field strength, Gauss’s theorem, and electric potential.

• Analyze conductors and dielectrics in electrostatic fields, including capacitors and polarization phenomena.

• Apply principles of steady currents, magnetic fields, and electromagnetic induction.

• Understand mutual and self-induction and transient circuit processes.

Thermodynamics

• Apply the first law of thermodynamics: system states, thermodynamic energy, enthalpy, entropy, work, and heat.

• Analyze gas properties including state equations, specific heat capacities, and ideal gas behavior.

• Apply the second law of thermodynamics: Carnot cycle, entropy, and the entropy increase principle.

Course Overview

This course provides an overview of integrated circuit design with a primary focus on digital logic design. Topics include the basic design process for integrated circuits, an introduction to digital and analog design, combinational and sequential circuit design, EDA tools, basic arithmetic units, and an introduction to simulation and synthesis using Verilog.

Learning Objectives

• Understand the integrated circuit design process.

• Develop a solid foundation in digital logic design as a basis for advanced study in VLSI design, computer architecture, and microprocessor systems.

• Design and analyze combinational and sequential circuits.

• Apply two-level logic minimization techniques using Boolean algebra, Karnaugh maps, the Quine-McCluskey method, and the Branch and Bound method.

• Use the Verilog hardware description language for practical digital design.

• Utilize modern EDA tools to design and verify logic circuits.

• Design basic arithmetic logic circuits and finite state machines.

Gain an initial understanding of the design of larger-scale digital systems.

Course Description

This course offers a comprehensive introduction to core computer science concepts, designed for students with little or no prior programming experience. Students will develop a solid understanding of computer science principles and programming practices, including modern computer architectures, algorithmic thinking, and hands-on problem-solving. Key topics include computer hardware, operating systems, databases, networking, algorithms, and data structures. By the end of the course, students will have a strong foundation in computer science and be well-prepared for more advanced study in the field.

Course Overview

Data structures and algorithms are foundational to all areas of computer science. This course introduces the design and analysis of core data structures and algorithms, and develops practical coding skills. Students will explore the characteristics, applications, and complexity of key data structures, and study fundamental algorithms including sorting and searching.

Learning Objectives

• Understand time and space complexity and perform analysis using Big-O notation.

• Understand and apply fundamental data structures: arrays, stacks, queues, trees, and graphs.

• Apply general algorithm design paradigms including recursion, divide-and-conquer, greedy algorithms, and randomized algorithms to classical computational problems.

• Analyze algorithms for correctness and computational efficiency.

Implement data structures and algorithms in a programming language (e.g., Python or C) and apply them to real-world problems.

Course Overview

This course provides a solid foundation in the C programming language — one of the most widely used and influential languages in domains such as embedded systems, operating systems, and high-performance computing. Students will learn the fundamental concepts of C, including data types, operators, expressions, and functions, and gain experience with software development practices including memory management, data structure design, and system-level programming.