Search result for Master of science in computational science and engineering Online Courses & Certifications
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Introduction to Genomic Technologies
by Steven Salzberg, PhD , Jeff Leek, PhD- 4.6
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Approx. 6 hours to complete
You'll also get an introduction to the key concepts in computing and data science that you'll need to understand how data from next-generation sequencing experiments are generated and analyzed. This is the first course in the Genomic Data Science Specialization. Quiz 1: Overview and Molecular Biology Data Science Technology Quiz 4: Data Science Technology...
Convex Optimization
by Stephen Boyd , Henryk Blasinski- 0.0
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8 Weeks
Candidate in Computer Science at Stanford University. , summa cum laude, in Mathematics and Computer Science from the University of Pennsylvania and an M. Ernest Ryu is a PhD candidate in Computational and Mathematical Engineering at Stanford University. Madeleine Udell is a PhD candidate in Computational and Mathematical Engineering at Stanford University....
Convex Optimization
by Stephen Boyd , Henryk Blasinski- 0.0
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8 Weeks
Candidate in Computer Science at Stanford University. , summa cum laude, in Mathematics and Computer Science from the University of Pennsylvania and an M. Ernest Ryu is a PhD candidate in Computational and Mathematical Engineering at Stanford University. Madeleine Udell is a PhD candidate in Computational and Mathematical Engineering at Stanford University....
Industrial Biotechnology
by Prof. Nicholas Turner , Prof. Nigel Scrutton- 4.7
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Approx. 11 hours to complete
Explain the diversity of synthetic biology application and discuss the different ethical and regulatory/governance challenges involved in this research. Understand the principles and role of bioprocessing and biochemical engineering in industrial biotechnology. Give examples of industrial biotechnology products and processes and their application in healthcare, agriculture, fine chemicals, energy and the environment....
Communicating Data Science Results
by Bill Howe- 3.6
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Approx. 8 hours to complete
You will also learn the importance of reproducibility in data science and how the commercial cloud can help support reproducible research even for experiments involving massive datasets, complex computational infrastructures, or both. Design and critique visualizations Explain the state-of-the-art in privacy, ethics, governance around big data and data science Reproducibility and Data Science...
Introduction to Logic
by Michael Genesereth- 4.4
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Approx. 54 hours to complete
This course is an introduction to Logic from a computational perspective. It shows how to encode information in the form of logical sentences; it shows how to reason with information in this form; and it provides an overview of logic technology and its applications - in mathematics, science, engineering, business, law, and so forth....
Financial Engineering and Risk Management Part II
by Martin Haugh , Garud Iyengar- 4.7
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Approx. 17 hours to complete
The emphasis of FE & RM Part II will be on the use of simple stochastic models to (i) solve portfolio optimization problems (ii) price derivative securities in various asset classes including equities and credit and (iii) consider some advanced applications of financial engineering including algorithmic trading and the pricing of real options....
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Computational Thinking for Problem Solving
by Susan Davidson- 4.7
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Approx. 18 hours to complete
Computational thinking is the process of approaching a problem in a systematic manner and creating and expressing a solution such that it can be carried out by a computer. Many quantitative and data-centric problems can be solved using computational thinking and an understanding of computational thinking will give you a foundation for solving problems that have real-world, social impact....
Introduction to High-Performance and Parallel Computing
by Shelley Knuth , Thomas Hauser- 3.4
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Approx. 18 hours to complete
This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics....
Financial Engineering and Risk Management Part I
by Martin Haugh , Garud Iyengar- 4.6
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Approx. 18 hours to complete
We hope that students who complete the course will begin to understand the "rocket science" behind financial engineering but perhaps more importantly, we hope they will also understand the limitations of this theory in practice and why financial models should always be treated with a healthy degree of skepticism. Floating Rate Bonds and Term Structure of Interest Rates...
