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Turn data into decisions with statistics and code
Online or on campus
Data Analytics at Haverton 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 Haverton'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 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 |
|---|---|---|
| DA305 | Capstone Analytics Project & Dissertation Capstone Analytics Project & Dissertation (DA305) is a course on BSc (Hons) Data Analytics. It carries 30 credits and 300 notional learning hours, at 10 hours per credit. The course is taught at bachelor's level. Students learn the method on a guided case, then use it on a longer task with the sources named in the chapters. 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 |
| Code | Course | Credits · hours |
|---|---|---|
| DA101 | Statistics I — Probability & Inference Statistics I — Probability & Inference (DA101) is a course on BSc (Hons) Data Analytics. It carries 30 credits and 300 notional learning hours, at 10 hours per credit. The course is taught at bachelor's level. Students learn the method on a guided case, then use it on a longer task with the sources named in the chapters. 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 |
| DA102 | Programming for Analytics (Python/R) Programming for Analytics (Python/R) (DA102) is a course on BSc (Hons) Data Analytics. It carries 30 credits and 300 notional learning hours, at 10 hours per credit. The course is taught at bachelor's level. Students learn the method on a guided case, then use it on a longer task with the sources named in the chapters. 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 |
| DA103 | Data Visualisation & Storytelling Data Visualisation & Storytelling (DA103) is a course on BSc (Hons) Data Analytics. It carries 30 credits and 300 notional learning hours, at 10 hours per credit. The course is taught at bachelor's level. Students learn the method on a guided case, then use it on a longer task with the sources named in the chapters. 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 |
| DA104 | Business Context for Data Business Context for Data (DA104) is a course on BSc (Hons) Data Analytics. It carries 30 credits and 300 notional learning hours, at 10 hours per credit. The course is taught at bachelor's level. Students learn the method on a guided case, then use it on a longer task with the sources named in the chapters. 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 |
| DA105 | Linear Algebra & Discrete Mathematics Linear Algebra & Discrete Mathematics (DA105) is a course on BSc (Hons) Data Analytics. It carries 30 credits and 300 notional learning hours, at 10 hours per credit. The course is taught at bachelor's level. Students learn the method on a guided case, then use it on a longer task with the sources named in the chapters. 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 |
| DA106 | Data Acquisition & Wrangling Data Acquisition & Wrangling (DA106) is a course on BSc (Hons) Data Analytics. It carries 30 credits and 300 notional learning hours, at 10 hours per credit. The course is taught at bachelor's level. Students learn the method on a guided case, then use it on a longer task with the sources named in the chapters. 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 |
| Code | Course | Credits · hours |
|---|---|---|
| DA201 | Regression & Statistical Inference Regression & Statistical Inference (DA201) is a course on BSc (Hons) Data Analytics. It carries 30 credits and 300 notional learning hours, at 10 hours per credit. The course is taught at bachelor's level. Students learn the method on a guided case, then use it on a longer task with the sources named in the chapters. 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 |
| DA202 | Database Systems & Advanced SQL Database Systems & Advanced SQL (DA202) is a course on BSc (Hons) Data Analytics. It carries 30 credits and 300 notional learning hours, at 10 hours per credit. The course is taught at bachelor's level. Students learn the method on a guided case, then use it on a longer task with the sources named in the chapters. 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 |
| DA203 | Machine Learning Foundations Machine Learning Foundations (DA203) is a course on BSc (Hons) Data Analytics. It carries 30 credits and 300 notional learning hours, at 10 hours per credit. The course is taught at bachelor's level. Students learn the method on a guided case, then use it on a longer task with the sources named in the chapters. 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 |
| DA204 | Analytics Ethics & Data Governance Analytics Ethics & Data Governance (DA204) is a course on BSc (Hons) Data Analytics. It carries 30 credits and 300 notional learning hours, at 10 hours per credit. The course is taught at bachelor's level. Students learn the method on a guided case, then use it on a longer task with the sources named in the chapters. 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 |
| DA205 | Time Series Analysis & Forecasting Time Series Analysis & Forecasting (DA205) is a course on BSc (Hons) Data Analytics. It carries 30 credits and 300 notional learning hours, at 10 hours per credit. The course is taught at bachelor's level. Students learn the method on a guided case, then use it on a longer task with the sources named in the chapters. 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 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 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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