MSc in Quantitative Risk Management with Machine Learning
- Public University
Department of Management
Talk to ExpertMSc in Quantitative Risk Management with Machine Learning at Birkbeck London Overview
Birkbeck, University of London is one of the finest universities in UK. Birkbeck, University of London provides MSc in Quantitative Risk Management with Machine Learning at PG level. The duration of this course is 1 year. This course attracts students from all over the world. To enrol in this course, international students must fulfil all the admission requirements. The admission requirement at the Birkbeck, University of London include standardized tests like IELTS, TOEFL and PTE. The first year tuition fees Birkbeck, University of London for MSc in Quantitative Risk Management with Machine Learning is Pound 19,380 (INR 2,505,834). Moreover, the students must be ready for other expenses such as rent, hostel fees, food, books, supplies, utilities, etc.
Duration | 1 year |
Tuition & Fees | ₹24.38 Lakh Get Fees details |
International Students Admission Website | Go to Website  |
Course Level | PG Degree |
Mode of Course | Full Time |
MSc in Quantitative Risk Management with Machine Learning at Birkbeck London Fees
Birkbeck, University of London MSc in Quantitative Risk Management with Machine Learning fee structure includes first-year tuition fee that all the students have to pay. The total expense may vary, depending on the additional charges provided by the Birkbeck, University of London. The total first-year tuition fees of MSc in Quantitative Risk Management with Machine Learning are Pound 19,380 (INR 2,505,834).
To know more about Birkbeck, University of London MSc in Quantitative Risk Management with Machine Learning fee breakup, check out the table below:
1st year tuition fees | Fees components | Amount (for 1 year) |
|---|---|
| Tuition & fees | INR 24,37,670 |
Calculated at the exchange rate of 1 Pound = INR 125.78276 | Tuition & expenses were last updated on 28ᵗʰ September 2024Download exam sample paper
MSc in Quantitative Risk Management with Machine Learning at Birkbeck London Entry Requirements
- No specific cutoff mentioned
- 55%
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- Applicants must have passed with second-class degree, 55% or equivalent or a 5.50-6.49 GPA on a scale of 10 in a quantitative subject, such as mathematics, physics, statistics, economics or engineering.
- Alternatively, a merit or higher in our MSc Finance.
- Applicant below this mark who have attended one of the top institutions may be considered at the discretion of the department
Calculate your score and check your eligibility at over 2000+ universities.
- No specific cutoff mentioned
Calculate your score and check your eligibility at over 2000+ universities.
- No specific cutoff mentioned
- Marks - 6.5/9
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- with not less than 6.0 in each of the sub-tests.
- Get a Full FREE IELTS Prep Course with Shiksha Study Abroad – Limited Seats for Weekend & Weekday Batches! Register now for IELTS
- Yes
- Conditional offer: Applicants will receive a conditional offer if they are still studying or need to obtain additional qualifications in order to meet the entry criteria (e.g. pass A-level exams or obtain an English Language qualification such as IELTS)
MSc in Quantitative Risk Management with Machine Learning at Birkbeck London Rankings
| Rank | Rank Publisher |
|---|---|
| #326 | |
| #218 | |
| #801 |
MSc in Quantitative Risk Management with Machine Learning at Birkbeck London Placements
| Particulars | Statistics (2021) |
|---|---|
| Internship Available | Yes |
MSc in Quantitative Risk Management with Machine Learning at Birkbeck London Highlights
- MSc in Quantitative Risk Management with Machine Learning is offered by Department of Management under Birkbeck, University of London, UK.
This a Masters level program of a course duration of 1 Years.
Download the brochure to read more details of this course.
MSc in Quantitative Risk Management with Machine Learning at Birkbeck London Scholarships
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MSc in Quantitative Risk Management with Machine Learning at Birkbeck London Contact Information
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