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Turn data into decisions that matter
Online or on campus
Data science sits at the intersection of statistics, computing, and domain expertise. This programme teaches you to frame business questions as analytical problems, wrangle messy real-world datasets, and communicate findings to non-technical audiences. Modules cover Bayesian inference, deep learning, NLP, and MLOps. You will work with public datasets from healthcare, finance, and climate science, building a portfolio that demonstrates end-to-end analytical capability. MSc Data Science is taught as one award, not a list of unrelated modules. The published length is 24 months, and the credit total is 36. Students move from Statistics & Probability for Data Science through Statistics & Probability for Data Science; Programming for Analytics (Python/R); Data Wrangling & SQL; Data Visualisation & Storytelling; Machine Learning Fundamentals; Deep Learning & Neural Networks, and finish on MSc Data Science Capstone. Each course has a question, a method, and a submission. In this field, students define a measurable question, fit a method that can be checked, and report where it fails. Reading is a dataset description, a methods note, and one published evaluation. The artefact a marker expects is a project note with the question, the method, the result, and the limitation. The award is built so that a graduate can do the following in practice: Apply statistical inference and experimental design to business problems Build and validate predictive models using scikit-learn, PyTorch, and R Design dashboards and narratives that drive executive action Teaching assumes the student can read a source, attempt a problem before the seminar, and revise after feedback. Attendance at live seminars is part of the design. The capstone or final course must use the methods of the earlier courses; a project that ignores them does not pass. The pages for each course name the topics that are examined. Those topics are the syllabus. A brochure line is not a substitute for them.
Every module uses real datasets — never toy CSVs alone. You will document reproducible notebooks and present to a mock executive panel in the capstone. Haverton partners with public-health NGOs for anonymised analytics projects.
Faculty spotlight
Dr. Priya Sharma, ex-lead data scientist at a global bank, teaches MLOps; Professor Daniel Mensah specialises in causal inference for policy evaluation.
Study online
Online tuition is free. Online examinations and certificates are 100% free and sponsored.
On-campus study
Prefer to learn at our London campus? Campus tuition is published on each programme page. All examinations are 100% free of charge.
| Code | Course | Credits · hours |
|---|---|---|
| DS501 | Statistics & Probability for Data Science Statistics & Probability for Data Science (DS501) is a course on MSc Data Science. It carries 15 credits and 150 notional learning hours, at 10 hours per credit. The course is taught at master's level. Students compare a weaker method, judge the evidence, and state what the result does not show. The chapters are the syllabus. Each chapter is a class of work this course assesses. Preparation is the reading, dataset, or case for that chapter. Seminar time is spent on the decision, the calculation, or the draft. The student revises after feedback. A pass requires the method named in the chapters, evidence a marker can check, and a stated limit on what the result does not show. Haverton University teaches this course on the published route for this degree.
| 15 · 150 h |
| DS502 | Programming for Analytics (Python/R) Programming for Analytics (Python/R) (DS502) is a course on MSc Data Science. It carries 15 credits and 150 notional learning hours, at 10 hours per credit. The course is taught at master's level. Students compare a weaker method, judge the evidence, and state what the result does not show. The chapters are the syllabus. Each chapter is a class of work this course assesses. Preparation is the reading, dataset, or case for that chapter. Seminar time is spent on the decision, the calculation, or the draft. The student revises after feedback. A pass requires the method named in the chapters, evidence a marker can check, and a stated limit on what the result does not show. Haverton University teaches this course on the published route for this degree.
| 15 · 150 h |
| DS503 | Data Wrangling & SQL Data Wrangling & SQL (DS503) is a course on MSc Data Science. It carries 15 credits and 150 notional learning hours, at 10 hours per credit. The course is taught at master's level. Students compare a weaker method, judge the evidence, and state what the result does not show. The chapters are the syllabus. Each chapter is a class of work this course assesses. Preparation is the reading, dataset, or case for that chapter. Seminar time is spent on the decision, the calculation, or the draft. The student revises after feedback. A pass requires the method named in the chapters, evidence a marker can check, and a stated limit on what the result does not show. Haverton University teaches this course on the published route for this degree.
| 15 · 150 h |
| DS504 | Data Visualisation & Storytelling Data Visualisation & Storytelling (DS504) is a course on MSc Data Science. It carries 15 credits and 150 notional learning hours, at 10 hours per credit. The course is taught at master's level. Students compare a weaker method, judge the evidence, and state what the result does not show. The chapters are the syllabus. Each chapter is a class of work this course assesses. Preparation is the reading, dataset, or case for that chapter. Seminar time is spent on the decision, the calculation, or the draft. The student revises after feedback. A pass requires the method named in the chapters, evidence a marker can check, and a stated limit on what the result does not show. Haverton University teaches this course on the published route for this degree.
| 15 · 150 h |
| Code | Course | Credits · hours |
|---|---|---|
| DS505 | Machine Learning Fundamentals Machine Learning Fundamentals (DS505) is a course on MSc Data Science. It carries 15 credits and 150 notional learning hours, at 10 hours per credit. The course is taught at master's level. Students compare a weaker method, judge the evidence, and state what the result does not show. The chapters are the syllabus. Each chapter is a class of work this course assesses. Preparation is the reading, dataset, or case for that chapter. Seminar time is spent on the decision, the calculation, or the draft. The student revises after feedback. A pass requires the method named in the chapters, evidence a marker can check, and a stated limit on what the result does not show. Haverton University teaches this course on the published route for this degree.
| 15 · 150 h |
| DS506 | Deep Learning & Neural Networks Deep Learning & Neural Networks (DS506) is a course on MSc Data Science. It carries 15 credits and 150 notional learning hours, at 10 hours per credit. The course is taught at master's level. Students compare a weaker method, judge the evidence, and state what the result does not show. The chapters are the syllabus. Each chapter is a class of work this course assesses. Preparation is the reading, dataset, or case for that chapter. Seminar time is spent on the decision, the calculation, or the draft. The student revises after feedback. A pass requires the method named in the chapters, evidence a marker can check, and a stated limit on what the result does not show. Haverton University teaches this course on the published route for this degree.
| 15 · 150 h |
| DS507 | Natural Language Processing Natural Language Processing (DS507) is a course on MSc Data Science. It carries 15 credits and 150 notional learning hours, at 10 hours per credit. The course is taught at master's level. Students compare a weaker method, judge the evidence, and state what the result does not show. The chapters are the syllabus. Each chapter is a class of work this course assesses. Preparation is the reading, dataset, or case for that chapter. Seminar time is spent on the decision, the calculation, or the draft. The student revises after feedback. A pass requires the method named in the chapters, evidence a marker can check, and a stated limit on what the result does not show. Haverton University teaches this course on the published route for this degree.
| 15 · 150 h |
| DS508 | Time Series & Forecasting Time Series & Forecasting (DS508) is a course on MSc Data Science. It carries 15 credits and 150 notional learning hours, at 10 hours per credit. The course is taught at master's level. Students compare a weaker method, judge the evidence, and state what the result does not show. The chapters are the syllabus. Each chapter is a class of work this course assesses. Preparation is the reading, dataset, or case for that chapter. Seminar time is spent on the decision, the calculation, or the draft. The student revises after feedback. A pass requires the method named in the chapters, evidence a marker can check, and a stated limit on what the result does not show. Haverton University teaches this course on the published route for this degree.
| 15 · 150 h |
| Code | Course | Credits · hours |
|---|---|---|
| DS601 | MLOps & Production Systems MLOps & Production Systems (DS601) is a course on MSc Data Science. It carries 15 credits and 150 notional learning hours, at 10 hours per credit. The course is taught at master's level. Students compare a weaker method, judge the evidence, and state what the result does not show. The chapters are the syllabus. Each chapter is a class of work this course assesses. Preparation is the reading, dataset, or case for that chapter. Seminar time is spent on the decision, the calculation, or the draft. The student revises after feedback. A pass requires the method named in the chapters, evidence a marker can check, and a stated limit on what the result does not show. Haverton University teaches this course on the published route for this degree.
| 15 · 150 h |
| DS602 | Big Data Technologies (Spark) Big Data Technologies (Spark) (DS602) is a course on MSc Data Science. It carries 15 credits and 150 notional learning hours, at 10 hours per credit. The course is taught at master's level. Students compare a weaker method, judge the evidence, and state what the result does not show. The chapters are the syllabus. Each chapter is a class of work this course assesses. Preparation is the reading, dataset, or case for that chapter. Seminar time is spent on the decision, the calculation, or the draft. The student revises after feedback. A pass requires the method named in the chapters, evidence a marker can check, and a stated limit on what the result does not show. Haverton University teaches this course on the published route for this degree.
| 15 · 150 h |
| DS603 | MSc Data Science Capstone MSc Data Science Capstone (DS603) is a course on MSc Data Science. It carries 30 credits and 300 notional learning hours, at 10 hours per credit. The course is taught at master's level. Students compare a weaker method, judge the evidence, and state what the result does not show. The chapters are the syllabus. Each chapter is a class of work this course assesses. Preparation is the reading, dataset, or case for that chapter. Seminar time is spent on the decision, the calculation, or the draft. The student revises after feedback. A pass requires the method named in the chapters, evidence a marker can check, and a stated limit on what the result does not show. Haverton University teaches this course on the published route for this degree.
| 30 · 300 h |
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 24 months. Teaching language: English. Actual time-to-complete depends on mode and any recognised prior learning.
You may study this award fully online from your country, or — where published — on campus at Haverton. Online study does not require a student visa. Campus study may.
Degree tuition for this award is published as £0 / tuition-free on the online pathway. All online degree examinations and certificates are 100% free and sponsored — the only cost for online study is the application fee. Campus tuition and campus examinations are charged separately. Check the Fees page for details.
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. Haverton 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@haverton.org.
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