Computational and Mathematical Engineering - Data Science Track, M.Sc.

Master

In Stanford (USA)

Price on request

Description

  • Type

    Master

  • Location

    Stanford (USA)

The Data Science track develops strong mathematical, statistical, and computational and programming skills through the general master's core and programming requirements. In addition, it provides a fundamental data science education through general and focused electives requirement from courses in data sciences and related areas. The course work follows the requirements of the general master's degree in the core course requirement. the general and focused elective requirements are limited to predefined courses from the data sciences and related courses group. Programming requirement is extended to 6 units and includes course work in advanced scientific programming and high performance computing. The final requirement is a practical component for 6 units to be completed through capstone project, data science clinic, or other courses that have strong hands-on or practical component such as statistical consulting. Recommended background: strong foundation in mathematics with courses in linear algebra, numerical methods, probabilities, stochastics, statistical theory, and programming proficiency in C++ and r.

Facilities

Location

Start date

Stanford (USA)
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Start date

On request

About this course

English Language Requirements This programme may require students to demonstrate proficiency in English. Schedule a TOEFL® test Schedule an IELTS test

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Reviews

This centre's achievements

2019

All courses are up to date

The average rating is higher than 3.7

More than 50 reviews in the last 12 months

This centre has featured on Emagister for 5 years

Subjects

  • GCSE Mathematics
  • Computational
  • Programming
  • Engineering
  • Mathematics
  • Statistics

Course programme

Programme Structure

Courses included:

  • Numerical Linear Algebra
  • Numerical Optimization
  • Convex Optimization I
  • Discrete Mathematics and Algorithms
  • Stochastic Methods in Engineering
  • Introduction to Statistical Inference
  • Introduction to Regression Models and Analysis of Variance
  • Introduction to Statistical Modeling
  • Modern Applied Statistics: Learning
  • Modern Applied Statistics: Data Mining
  • Representations and Algorithms for Computational Molecular Biology
  • Data Driven Medicine
  • Modern Statistics for Modern Biology

Computational and Mathematical Engineering - Data Science Track, M.Sc.

Price on request