IT, Data and Cyber Security · Awarded by OTHM
Level 7 Diploma in Data Science
A postgraduate-level route into data science for analysts and technical professionals who would rather learn alongside work than stop for a campus year.
- LevelRQF Level 7Same level as a master's
- Credits120six units
- Qualification time1,200 hours480 guided learning hours
- Awarding bodyOTHMRegulated by Ofqual
- Ofqual number610/2153/2Search the Ofqual register
- Duration9 months (standard) or 6 months (fast track)Set by LSPM, confirmed on enquiry
- Study modesonline or blendedLive seminars, evenings and Saturdays
- FeesFrom £999 (9 months, standard) or £1,299 (6 months, fast track)Written quotation on enquiry
- Next intakeRolling intake — course starts when the fee is paidSet by LSPM
- AssessmentAssignmentsNo examinations
Who it is for
Analysts and engineers who want to work at the modelling end
Six units of 20 credits take you from foundations to advanced methods: Data Science Foundations, Probability and Statistics for Data Analysis, Advanced Predictive Modelling, Data Analysis and Visualisation, Data Mining, Machine Learning and Artificial Intelligence, and Advanced Computing Research Methods.
It suits business and data analysts who want to move into modelling and machine learning, software engineers adding data science to their range, and managers of analytics teams who need to understand what their teams are doing well enough to direct it.
It is a demanding technical programme. You will write code, most commonly in Python or R, and you will need to be comfortable with mathematics at least to first-year undergraduate level. It is not a management diploma with a data flavour; the assignments expect working models and honest evaluation of them.
Structure
Six units, all mandatory
All six units are mandatory and are taken in a sequence LSPM sets. Unit titles and credit values are from the awarding body's specification.
Mandatory units All six required
| Unit | Credits |
|---|---|
| Data Science Foundations | 20 |
| Probability and statistics for data analysis | 20 |
| Advanced Predictive Modelling | 20 |
| Data Analysis and Visualisation | 20 |
| Data Mining, Machine Learning and Artificial Intelligence | 20 |
| Advanced Computing Research Methods | 20 |
| 120 credits from mandatory units. | |
Unit titles and credit values: OTHM Level 7 Diploma in Data Science (OTHM), official specification, Ofqual 610/2153/2.
How LSPM sequences them
Data Science Foundations and Probability and Statistics for Data Analysis run first, in that order, and LSPM spends longer on them than their credit weighting suggests because everything else depends on them. Data Analysis and Visualisation follows. Advanced Predictive Modelling and then Data Mining, Machine Learning and Artificial Intelligence form the technical core of the second half. Advanced Computing Research Methods is delivered alongside the last two units so that your final project is methodologically sound. The guided learning hours are 480, lower than most diplomas on the site, so a larger share of the 1,200 hours is your own practical work.
Entry requirements
Who can enrol
OTHM sets the entry requirements and London School of Planning and Management, the approved centre, applies them. As published in the specification:
- An honours degree in related subject or UK level 6 diploma or an equivalent overseas qualification
- Mature learners with management experience (learners must check with the delivery centre regarding this experience prior to registering for the programme)
- Learner must be 21 years old or older at the beginning of the course
How LSPM applies this in practice. The awarding body's published entry route is a degree or Level 6 qualification, or management experience. In practice LSPM also looks for programming ability and mathematical comfort, and we will ask you about both in the advice call. We would rather point you to a preparatory course than introduce you to a first unit you cannot follow.
Entry requirements from the official specification. If you are not sure whether your qualification counts as equivalent, send us the details and we will ask LSPM to check with the awarding body.
Assessment
Written assignments, no examinations
Assignments combine code, analysis and a written report: a statistical analysis with interpretation, a predictive model with evaluation, a visualisation portfolio with commentary, and a machine learning project. Notebooks and code are submitted alongside the report and are marked for method as well as result. No examinations.
Your tutor at LSPM marks the work and gives written feedback. OTHM then externally verifies a sample of marked work to confirm the standard is being applied consistently. A unit is passed when all of its criteria are met.
Progression
From diploma to top-up with advanced standing
Some universities will count the diploma's 120 credits towards an MSc in Data Science or a related master's and admit holders to the final stage, usually after reviewing the technical content unit by unit. Neither LSPM nor this site guarantees that any will.
Within a career, the diploma most commonly supports a move from analyst to data scientist, or from engineer to machine learning engineer, and gives managers of analytics teams the grounding to lead them credibly.
Progression note for employers. If you are sponsoring a manager with a master's as the eventual goal, budget the top-up separately and check the university's terms before you commit. The diploma stage is where LSPM's fees and timetable apply; the top-up stage is the university's. How the pathway works.
Outcomes
What you will be able to do
- Work the data pipelineAcquire, clean, structure and document data ready for analysis.
- Reason statisticallyApply probability and statistical inference correctly and know when a result means nothing.
- Build predictive modelsDevelop, tune and evaluate predictive models and explain their limits to non-specialists.
- Visualise for decisionsDesign analyses and visualisations that lead to a decision rather than decorate a slide.
- Apply machine learningSelect and implement data mining and machine learning methods, including an appreciation of AI's limits.
- Research computationallyDesign and carry out a computing research project to postgraduate standard.
Fit
Likely to suit, and better served elsewhere
Likely to get a lot from it
- Business and data analysts moving into modelling and machine learning.
- Software engineers adding data science to their range.
- Managers of analytics teams who need to direct the work credibly.
Better served by something else
- Anyone without programming ability or mathematical comfort; a preparatory course first.
- Managers wanting a non-technical overview of data; a strategic management diploma with a data flavour suits better.
- Learners seeking a deep specialism in one method such as computer vision; a master's is the place for that.
Real learner quotation to be supplied. This slot must not be published until a named graduate has given written consent.
Programme questions
Frequently asked
Which programming language is used?
Python is the default and what LSPM's tutors mainly use in seminars. R is accepted for statistical work if you are already fluent in it. You will not be taught a language from scratch; the advice call checks that you can already write working code.
How much mathematics is needed?
Enough to follow probability, linear algebra and calculus at the level of a first-year STEM undergraduate course. The statistics unit rebuilds the probability you need, but it moves quickly.
Why are the guided learning hours 480 rather than 600?
The specification allocates 480 of the 1,200 hours to guided learning, a lower share than some OTHM diplomas, because so much of the learning is practical work you do at a keyboard. LSPM uses the seminar time for concepts and code review.
Is this an alternative to an MSc in Data Science?
It is a Level 7 diploma, at the same level as an MSc but with 120 credits rather than 180. Some universities accept it for advanced standing into the final project stage of an MSc. It is not a substitute for one.
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