Course: Math & Optimizations
duration: 8 hours |
Language: English (US) |
access duration: 90 days |
Details
In this course you will explore several math and optimization problems. First, you’ll learn more about the use and importance of discrete mathematics in the world of computer science and practise this with Python. Next, you’ll discover graphs and graph operations to model a network. Learn also about linear programming, which is an optimization model that can be used when our objective function and constraints can be represented using linear terms. Finally, you’ll examine how to solve optimizations problems using integer programming.
Result
After completing this course, you’ll have the skills to use and work with sets in the real world using Python. You’ll have the skills and knowledge to create and work with graphs using NetworkX in Python, to apply linear programming to solve optimization problems and to apply integer programming to solve optimization problems.
Prerequisites
No prerequisites.
Target audience
Software Developer, Web Developer
Content
Math & Optimizations
Math & Optimizations: Introducing Sets & Set Operations
Discrete mathematics is the study of objects that take on
- distinct, separated values. The study of discrete mathematics is
- important in the field of Computer Science as computers can only
- understand discrete binary numbers. Use this course to learn more
- about the use and importance of discrete mathematics in the world
- of computer science. Examine the use of sets and perform common
- operations on them in Python. These operations include union,
- intersection, difference, and symmetric difference. When you are
- finished with this course, you will have the skills to use and work
- with sets in the real world using Python.
Math & Optimizations: Introducing Graphs & Graph Operations
The graph data structure plays a significant role in modeling
- entities in the real world. A graph comprises nodes and edges that
- are used to represent entities and relationships, respectively. A
- graph can be used to model a social network or a professional
- network, roads and rail infrastructure, and telecommunication and
- telephone networks. Through this course, you'll explore graph data
- structure, graph components, and different types of graphs and
- their use cases. Start by discovering how to represent directed,
- undirected, weighted, and unweighted graphs in NetworkX. You'll
- then learn more about visualizing nodes and connections in graphs
- using Matplotlib. This course will also help you examine how to
- implement graph algorithms on all graph types using NetworkX. Upon
- completing this course, you will have the skills and knowledge to
- create and work with graphs using NetworkX in Python.
Math & Optimizations: Solving Optimization Problems Using Linear Programming
Mathematical optimization models allow us to represent our
- objectives, decision variables, and constraints in mathematical
- terms, and solving these models gives us the optimal solution to
- our problems. Linear programming is an optimization model that can
- be used when our objective function and constraints can be
- represented using linear terms. Use this course to learn how
- decision-making can be represented using mathematical optimization
- models. Begin by examining how optimization problems can be
- formulated using objective functions, decision variables, and
- constraints. You'll then recognize how to find an optimal solution
- to a problem from amongst feasible solutions through a case study.
- This course will also help you investigate the pros and cons of the
- assumptions made by linear programming and the steps involved in
- solving linear programming problems graphically as well as by using
- the Simplex method. When you are done with this course, you will
- have the skills and knowledge to apply linear programming to solve
- optimization problems.
Math & Optimizations: Solving Optimization Problems Using Integer Programming
- Integer programming is a mathematical optimization model that helps find optimal solutions to our problems. Integer programming problems find more applications than linear programming and are an important tool in a developer's toolkit. Examine how to solve optimizations problems using integer programming through this course.
- Start by comparing the integer programming optimization model and linear programming. You'll then move on to the LP relaxation technique and how it can be used to obtain the starting point of an integer programming solution.
- You'll also explore the Pulp Python library through different case studies consisting of integer programming problems.
- Upon completing this course, you'll be able to apply integer programming to solve optimization problems.
Course options
We offer several optional training products to enhance your learning experience. If you are planning to use our training course in preperation for an official exam then whe highly recommend using these optional training products to ensure an optimal learning experience. Sometimes there is only a practice exam or/and practice lab available.
Optional practice exam (trial exam)
To supplement this training course you may add a special practice exam. This practice exam comprises a number of trial exams which are very similar to the real exam, both in terms of form and content. This is the ultimate way to test whether you are ready for the exam.
Optional practice lab
To supplement this training course you may add a special practice lab. You perform the tasks on real hardware and/or software applicable to your Lab. The labs are fully hosted in our cloud. The only thing you need to use our practice labs is a web browser. In the LiveLab environment you will find exercises which you can start immediately. The lab enviromentconsist of complete networks containing for example, clients, servers,etc. This is the ultimate way to gain extensive hands-on experience.
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