LoadingLoadingLoadingLoadingDual British & Global Bachelor of Science (2 Diplomas) — Conferred jointly with FollowLap University (FollowLap) & University of London Worldwide (UK)
University of London (Worldwide) (United Kingdom)
Online only
Acquire world-class mathematical, computational, and statistical expertise to engineer production-grade machine learning pipelines and predictive models. Students earn 2 independent accredited diplomas with a 60% / 40% international credit split.
Study online
Every programme can be completed fully online with zero tuition fees — study from anywhere in the world.
| Code | Course | Credits |
|---|---|---|
| BSC-101 | Data and measurement Data and measurement (BSC-101) is a 20-credit course on BSc Data Science & Machine Learning (Dual Award), with 200 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Data and measurement as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.
| 20 |
| BSC-102 | Models that can be checked Models that can be checked (BSC-102) is a 20-credit course on BSc Data Science & Machine Learning (Dual Award), with 200 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Models that can be checked as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.
| 20 |
| BSC-103 | Deployment and care Deployment and care (BSC-103) is a 20-credit course on BSc Data Science & Machine Learning (Dual Award), with 200 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Deployment and care as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.
| 20 |
| BSC-104 | Analytics project Analytics project (BSC-104) is a 20-credit course on BSc Data Science & Machine Learning (Dual Award), with 200 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Analytics project as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.
| 20 |
| BSC-105 | Data and measurement (BSC-105) Data and measurement (BSC-105) (BSC-105) is a 20-credit course on BSc Data Science & Machine Learning (Dual Award), with 200 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Data and measurement (BSC-105) as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.
| 20 |
| BSC-106 | Models that can be checked (BSC-106) Models that can be checked (BSC-106) (BSC-106) is a 20-credit course on BSc Data Science & Machine Learning (Dual Award), with 200 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Models that can be checked (BSC-106) as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.
| 20 |
| Code | Course | Credits |
|---|---|---|
| BSC-201 | Deployment and care (BSC-201) Deployment and care (BSC-201) (BSC-201) is a 20-credit course on BSc Data Science & Machine Learning (Dual Award), with 200 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Deployment and care (BSC-201) as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.
| 20 |
| BSC-202 | Analytics project (BSC-202) Analytics project (BSC-202) (BSC-202) is a 20-credit course on BSc Data Science & Machine Learning (Dual Award), with 200 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Analytics project (BSC-202) as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.
| 20 |
| BSC-203 | Data and measurement (BSC-203) Data and measurement (BSC-203) (BSC-203) is a 20-credit course on BSc Data Science & Machine Learning (Dual Award), with 200 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Data and measurement (BSC-203) as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.
| 20 |
| BSC-204 | Models that can be checked (BSC-204) Models that can be checked (BSC-204) (BSC-204) is a 20-credit course on BSc Data Science & Machine Learning (Dual Award), with 200 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Models that can be checked (BSC-204) as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.
| 20 |
| BSC-205 | Deployment and care (BSC-205) Deployment and care (BSC-205) (BSC-205) is a 20-credit course on BSc Data Science & Machine Learning (Dual Award), with 200 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Deployment and care (BSC-205) as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.
| 20 |
| BSC-206 | Analytics project (BSC-206) Analytics project (BSC-206) (BSC-206) is a 20-credit course on BSc Data Science & Machine Learning (Dual Award), with 200 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Analytics project (BSC-206) as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.
| 20 |
| Code | Course | Credits |
|---|---|---|
| BSC-301 | Data and measurement (BSC-301) Data and measurement (BSC-301) (BSC-301) is a 20-credit course on BSc Data Science & Machine Learning (Dual Award), with 200 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Data and measurement (BSC-301) as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.
| 20 |
| BSC-302 | Models that can be checked (BSC-302) Models that can be checked (BSC-302) (BSC-302) is a 20-credit course on BSc Data Science & Machine Learning (Dual Award), with 200 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Models that can be checked (BSC-302) as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.
| 20 |
| BSC-303 | Deployment and care (BSC-303) Deployment and care (BSC-303) (BSC-303) is a 20-credit course on BSc Data Science & Machine Learning (Dual Award), with 200 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Deployment and care (BSC-303) as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.
| 20 |
| BSC-304 | Analytics project (BSC-304) Analytics project (BSC-304) (BSC-304) is a 20-credit course on BSc Data Science & Machine Learning (Dual Award), with 200 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Analytics project (BSC-304) as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.
| 20 |
| BSC-305 | Data and measurement (BSC-305) Data and measurement (BSC-305) (BSC-305) is a 40-credit course on BSc Data Science & Machine Learning (Dual Award), with 400 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Data and measurement (BSC-305) as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.
| 40 |
Modules are assessed through a published mix of coursework, applied projects, and examinations. Exam windows are announced in advance so students in other time zones are not forced into overnight sittings. Alternative arrangements are available where documented.
The published duration is 36 months. Teaching language: English. Actual time-to-complete depends on mode and any recognised prior learning.
This is a fully online award. You study from your country. No student visa and no campus relocation are required.
Degree tuition for this award is published as £0 / tuition-free on the online pathway. Examination or administrative fees may apply at checkout — never an annual tuition invoice. Check the Fees page for any extras.
Requirements are grouped on this page (academic, English, documents). Equivalent qualifications are considered. English may be waived after prior English-medium study.
Assessment is typically a mix of coursework, projects, and examinations. Doctoral awards include a thesis or dissertation and an oral examination. Details sit in the programme specification and module outlines.
Recognition of the award for local employment, professional licence, or ministry attestation is decided by your employer or regulator. FollowLap University publishes verification pages for certificates. We do not claim automatic equivalence in every country.
Start an application on this website. Progress is saved from the first step. Admissions: admissions@followlap.com.
Recognised & Accredited
