Updating...It is expected to be available on April 15.
Course Description:
Consistently getting the right products and services to the right people, in the right place, at the right cost, and at the right time is the heart of competitiveness. If a company cannot do that, then its other strengths will not matter in the long run. This makes mathematical decision models and quantitative analysis a core activity of the firm and a prime determinant of the firm’s profitability. The cleverest strategy or business model means little without the use of proper mathematical models and analysis.
This course is designed to provide students with an introduction to some of the fundamental mathematical backgrounds for analytics and finance decision models and analyses. The course has four parts: single variable calculus, matrix algebra, multivariable calculus and optimization, and probability and statistics.
During the course, the emphasis will be on actual algebraic calculations and how they are used in solving applied business problems and decision models. The process of calculating with variables builds skill in mathematical modeling and paves the way for students to use calculus, rather than the more traditional “definition-theorem-proof” treatment common in traditional applied mathematics courses.
Learning Objectives:
Students will learn several tools and techniques in:
1. Single variable calculus
2. Matrix algebra
3. Multivariable calculus and optimization
4. Probability and statistics
That are required to understand various quantitative topics in analytics, finance, marketing and economics.
Course Description:
Overview of integrated circuits design, mainly focus on digital logic design. Including basic design process of integrated circuits, introduction of digital/analog design, design of combinational/sequential circuits, EDA tools, basic arithmetic units, and introduction to simulation and synthesis using Verilog.
Learning Objectives:
Upon completion of this course, students should be able to:
1. Know integrated circuit design process.
2. Gain sufficient understanding of basic logic design concepts to pursue more advanced study areas such as VLSI design, computer architecture, microprocessor systems, etc.
3. Perform basic combinational and sequential circuit designs.
4. Perform two-level logic minimization using Boolean algebra, Karnaugh maps, the Quine McCluskey method, Branch and Bound method, etc.
5. Acquire basic methodology of hardware description language Verilog for pratical digital design.
6. Learn how modern EDA tools can help design simple logic circuits.
7. Perform simple arithmetic logic circuit design.
8. Understand the basic concepts and key points of sequential circuit design.
9. Perform design of finite state machine.
10. Get an initial understanding of the design of larger scale digital systems.
Overview and Objectives:
Part I of this course is to give a solid foundation in probability, as well as the basics of statistical methods.
Part II of this course provides an overview of first-order and second-order differential equations, Laplace transforms, partial differential equations, and applications.
Part III of this course provides an overview of vector calculus, line and surface integrals together with integral theorems and Fourier series. A number of applications to actual problems will be discussed.
Overview and Objectives:
This course gives a foundation in design and analysis of data structure and algorithm, as well as coding skills. We will introduce some data structures, explain the characteristics and uses, and discuss their time and space complexity. At the same time, you will need to implement some data structures for better understanding and use. We will also cover some basic algorithms like sorting, and you will need to understand its principles and relevant questions.
Overview and Objectives
The goal of this course is to give you a working knowledge of how to use Python and R to extract knowledge and information from data for business analysis purpose. You will be competent in using R and Python libraries to work with and analyze offline as well as online data. We will learn how to get data from files (csv, html, json, xml) and relational databases (mysql), cover the rudiments of data cleaning, and examine data analysis, machine learning (regression, decision trees, clustering), deep learning (tensorflow) and data visualization packages (numpy, Pandas, Scikit-learn) available in Python. We will examine how we can use Python libraries for extracting value from text (text mining) and understanding the properties of networks (network analysis).
Overview and Objectives:
Optimization problems arise very often in Business, and the ability to solve them is a competitive advantage in most cases. However, recognize, understand, modeling an Optimization problem requires special tools and skills. A problem that is not understood or modeled correctly can lead to the wrong solution or wrong conclusions. The purpose of this course is to provide you with the knowledge necessary to model practical Optimization problems and solve them efficiently. We will see how to properly formulate a problem and how to solve practical applications.
Overview and Objectives:
Our goal is to develop an understanding of the basic tools of statistical analysis and to learn how to apply them to a wide variety of situations and data encountered in the areas of business and economics. By the end of this course you should be able to:
1. Compute and interpret basic descriptive statistical measures.
Course Description:
This is a course in the principles of economics. General topics covered include, but are not limited to: supply and demand; pricing and production decisions of firms; competition and efficiency; the role of government in the economy; basic macroeconomics concepts; the determination of national income; labor, capital and financial markets.
Learning Objectives:
Upon completion of this course, students should be able to:
1. Define scarcity, opportunity cost, elasticity of demand and supply, inflation, unemployment, and gross domestic product.
2. Illustrate the process by which supply and demand for a product converge to market equilibrium. Analyze price ceilings, price floors and tax burdens using the supply and demand model.
3. Apply the concepts of marginal analysis and opportunity costs to consumer theory and to the profit-maximizing behavior of firms in both competitive and monopolistic markets, and compares competition to monopoly on efficiency grounds.
4. Define and explain the following terms: perfect competition; comparative advantage; price discrimination; oligopoly; consumer surplus; producer surplus; deadweight loss; the principle and agent problem; adverse selection and moral hazard.
5. Understand the basic macroeconomic concepts.
6. Understand the necessary conditions for an economy to experience a sustained, long-term growth.
Course Description:
Fluid properties: fluid statics, stability of floating bodies, conservation of mass, differential relations for fluid flow, Euler and Bernoulli equations, impulse-momentum principle, laminar and turbulent flow, dimensional analysis and model testing, analysis of flow in pipes, boundary layer on flat plates, hydrodynamic drag. Practical civil engineering applications stressed.
Student Learning Outcomes:
Students who successfully complete this course will possess a basic understanding of fluid mechanics. They will be prepared to solve common design problems and have gained an understanding of the fundamentals of the properties of fluids and how they relate to design. Students will also understand the collection and analysis of experimental engineering data and could interpret and present experimental results.
After successful completion of this course, students will be able to:
1) Determine the total force and locate the center of pressure on flat and curved surfaces.
2) Utilize the principles of buoyance to analyze the stability of a floating body.
3) Explain the physical basis for each term in the mass, momentum, Bernoulli
and energy equations and all physical boundary conditions.
4) Estimate forces associated with a momentum change.
5) Determine friction and minor losses in pipes and fittings for different flow conditions.
6) Determine pump and turbine heads.
7) Determine skin friction and drag on flat plates.
8) Extrapolate model testing data to prototype by applying dimensional analysis
and similitude.
9) Validate experimental objectives through data analysis and be able to account
for minor losses within a pipe network.
10) Gain familiarity with basic analytical instrumentation and equipment required to
perform fluid experiments (this outcome will take on a virtual dimension).
11) Utilize tables to find fluid mechanics properties and design tools such as the
Moody
12) Chart to determine engineering parameters
13) Communicate engineering data and experimental results.
Course Overview:
This course will advance your technical communications skills for both academic and career purposes. You will be asked to write technical documents, edit your own work and your peers’ work, produce graphs and visual displays of data, and present technical information.
Learning Goals:
1. Master principles of effective technical writing.
2. Master presenting data effectively.
3. Master giving an effective technical presentation.
4. Be familiar with teamwork and collaborative development of communication.
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