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Turn data into decisions with statistics and code
Online or on campus
Data Analytics at Brigant combines statistical theory with hands-on coding in Python and SQL. You will clean real-world datasets, build dashboards, and interpret findings for business and policy audiences. Modules cover probability, regression, experimental design, and introductory machine learning. Campus seminars use anonymised datasets from public health, finance, and education partners. Communication skills are emphasised — analysts must explain uncertainty, not just produce charts. BSc (Hons) Data Analytics is taught as one award, not a list of unrelated modules. The published length is 3 years full-time, and the credit total is 360. Students move from Statistics I — Probability & Inference through Statistics I — Probability & Inference; Programming for Analytics (Python/R); Data Visualisation & Storytelling; Business Context for Data; Linear Algebra & Discrete Mathematics; Data Acquisition & Wrangling, and finish on Capstone Analytics Project & Dissertation. 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: Clean, analyse, and visualise structured datasets with reproducible notebooks Apply regression and hypothesis testing to business questions Build interactive dashboards for non-technical stakeholders 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.
Access to Brigant's analytics sandbox includes JupyterHub, Tableau licences, and faculty office hours in the Data Studio.
Faculty spotlight
Dr. Priya Sharma teaches Statistical Modelling; Professor Daniel Mensah leads the Causal Inference reading group.
Study online
Online tuition is free. Online examinations are free. There is no exam fee for online study.
On-campus study
Prefer to learn at our Edinburgh campus? Campus tuition and campus examinations are charged. The published campus price is on each programme page.
| Code | Course | Credits · hours |
|---|---|---|
| DA101 | Statistics I — Probability & Inference Statistics I — Probability & Inference (DA101) is a 20-credit course on BSc (Hons) Data Analytics, 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 Statistics I — Probability & Inference 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 · 200 h |
| DA102 | Programming for Analytics (Python/R) Programming for Analytics (Python/R) (DA102) is a 20-credit course on BSc (Hons) Data Analytics, 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 Programming for Analytics (Python/R) 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 · 200 h |
| DA103 | Data Visualisation & Storytelling Data Visualisation & Storytelling (DA103) is a 20-credit course on BSc (Hons) Data Analytics, 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 Visualisation & Storytelling 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 · 200 h |
| DA104 | Business Context for Data Business Context for Data (DA104) is a 20-credit course on BSc (Hons) Data Analytics, 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 Business Context for Data 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 · 200 h |
| DA105 | Linear Algebra & Discrete Mathematics Linear Algebra & Discrete Mathematics (DA105) is a 20-credit course on BSc (Hons) Data Analytics, 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 Linear Algebra & Discrete Mathematics 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 · 200 h |
| DA106 | Data Acquisition & Wrangling Data Acquisition & Wrangling (DA106) is a 20-credit course on BSc (Hons) Data Analytics, 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 Acquisition & Wrangling 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 · 200 h |
| Code | Course | Credits · hours |
|---|---|---|
| DA201 | Regression & Statistical Inference Regression & Statistical Inference (DA201) is a 20-credit course on BSc (Hons) Data Analytics, 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 Regression & Statistical Inference 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 · 200 h |
| DA202 | Database Systems & Advanced SQL Database Systems & Advanced SQL (DA202) is a 20-credit course on BSc (Hons) Data Analytics, 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 Database Systems & Advanced SQL 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 · 200 h |
| DA203 | Machine Learning Foundations Machine Learning Foundations (DA203) is a 20-credit course on BSc (Hons) Data Analytics, 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 Machine Learning Foundations 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 · 200 h |
| DA204 | Analytics Ethics & Data Governance Analytics Ethics & Data Governance (DA204) is a 20-credit course on BSc (Hons) Data Analytics, 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 Ethics & Data Governance 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 · 200 h |
| DA205 | Time Series Analysis & Forecasting Time Series Analysis & Forecasting (DA205) is a 20-credit course on BSc (Hons) Data Analytics, 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 Time Series Analysis & Forecasting 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 · 200 h |
| DA206 | Big Data Technologies & Cloud Pipelines Big Data Technologies & Cloud Pipelines (DA206) is a 20-credit course on BSc (Hons) Data Analytics, 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 Big Data Technologies & Cloud Pipelines 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 · 200 h |
| Code | Course | Credits · hours |
|---|---|---|
| DA301 | Advanced Predictive Analytics Advanced Predictive Analytics (DA301) is a 20-credit course on BSc (Hons) Data Analytics, 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 Advanced Predictive Analytics 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 · 200 h |
| DA302 | Natural Language Processing & Text Mining Natural Language Processing & Text Mining (DA302) is a 20-credit course on BSc (Hons) Data Analytics, 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 Natural Language Processing & Text Mining 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 · 200 h |
| DA303 | Deep Learning & Neural Networks Deep Learning & Neural Networks (DA303) is a 20-credit course on BSc (Hons) Data Analytics, 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 Deep Learning & Neural Networks 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 · 200 h |
| DA304 | Decision Analytics & Optimisation Decision Analytics & Optimisation (DA304) is a 20-credit course on BSc (Hons) Data Analytics, 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 Decision Analytics & Optimisation 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 · 200 h |
| DA305 | Capstone Analytics Project & Dissertation Capstone Analytics Project & Dissertation (DA305) is a 40-credit course on BSc (Hons) Data Analytics, 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 Capstone Analytics Project & Dissertation 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 · 400 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 3 years full-time. 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 Brigant. 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. 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. University of Brigant 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@brigant.uk.
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