CSE.0002x is an introduction to using computing to solve problems in engineering and science including simulation of time-dependent phenomena; optimization of systems; and quantification of uncertainty.
CSE.0002x is an advanced, hands-on course that teaches you how to leverage computational methods to solve complex problems in engineering and science. This course focuses on using Python programming to simulate time-dependent phenomena, optimize systems, and quantify uncertainty. By the end of this course, you'll be able to write sophisticated programs that tackle real-world challenges, such as simulating a Mars lander's descent, optimizing cellular tower placement, and assessing climate change scenarios.
This course is ideal for students, engineers, and scientists who have some programming experience and want to enhance their problem-solving skills using computational methods. It's particularly suited for those interested in applying mathematical and computational techniques to real-world engineering and scientific challenges.
The skills acquired in this course have wide-ranging applications in various fields:
These skills are highly valued in industries that rely on data-driven decision-making and complex system modeling, making graduates of this course attractive candidates for roles in tech companies, research institutions, and engineering firms.
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An in-depth introduction to the field of machine learning, from linear models to deep learning and reinforcement learning, through hands-on Python projects. -- Part of the MITx MicroMasters program in Statistics and Data Science.
If you want to learn to code with Python, this free course will give you an overview of the ins and outs of Python basics. Try our no-risk course today and see why Python has become one of the world’s most popular and in-demand programming languages. No previous coding experience required.
