UK

Qualifi Ltd · RQF Level 3

Qualifi Level 3 Diploma in Data Science

You don’t just earn this. You learn to use it, one-to-one, on your own real work.

  • LevelRQF Level 3
  • Total credits60
  • Guided learning455 hours
  • Units in this qualification14

Awarded by Qualifi Ltd · on the Ofqual register

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What you’ll learn

What each unit actually teaches you to do

The Level 3 Diploma in Data Science is designed for individuals seeking to develop skills in the digital technology sector. It covers topics such as logistic regression, graph data science, linear regression, and Python for data science.

These are the units Qualifi Ltd registers against this qualification. It is a catalogue, not a syllabus. Which of them you take depends on the combination the qualification requires, and that is set by the awarding body rather than by us. Every unit here is regulated, and every one you earn is yours to keep.

Creating and Interpreting Visualisations in Data ScienceReal unit · Qualifi Ltd
You'll study the importance of data visualisation in analysis and communication. The unit covers basic plot types and charts, principles of effective visualisation, and develops skills to create and interpret visual data representations appropriately.

What you'll be able to do

  • Understand the role and importance of visualising data for analysis and communication
  • Understand the purpose and application of basic plots and charts in data visualisation
  • Create and interpret plots and charts to effectively represent and analyse data
Data Analytics with PythonReal unit · Qualifi Ltd
This unit equips learners with practical skills to load, save, wrangle, clean, and transform data using Python. You'll perform essential data manipulation tasks necessary for exploratory data analysis and preparation in data science projects.

What you'll be able to do

  • Load data from various sources and save data in different formats using Python.
  • Perform basic data wrangling techniques and conduct exploratory data analysis using Python.
  • Perform basic data cleaning tasks, such as handling missing values and outliers, using Python.
  • Perform basic data transformation tasks, such as scaling and normalisation, using Python.
Data and Descriptive Statistics in Data ScienceReal unit · Qualifi Ltd
You'll study different types of data and their characteristics alongside measures of central tendency, spread, symmetry, peakness, and relationships. The unit enables interpretation of descriptive statistics essential for data science analysis.

What you'll be able to do

  • Understand the different types of data (e.g., numerical, categorical) and their characteristics.
  • Understand various measures of central tendency (e.g., mean, median, mode) and their interpretation.
  • Understand various measures of spread (e.g., variance, standard deviation, range) and their interpretation.
  • Understand measures of symmetry (skewness) and peakness (kurtosis) in data distributions.
  • Understand measures of joint variability (covariance) and linear relationship (correlation) between variables.
Decision Trees in Data ScienceReal unit · Qualifi Ltd
This unit introduces learners to decision trees, covering their principles, construction, and applications in classification and regression. You'll perform calculations using decision tree metrics and build decision tree models for data science problems.

What you'll be able to do

  • Understand the fundamental principles of decision trees in the context of data science.
  • Understand the applications of decision trees in solving data science problems.
  • Understand the different components of a decision tree, such as nodes, branches, and leaves.
  • Understand how to construct a decision tree using various algorithms.
  • Understand the process of splitting nodes based on different criteria.
  • Perform calculations using decision tree metrics, such as entropy, information gain, and Gini impurity.
  • Interpret the results of these calculations.
  • Apply these metrics to optimise the structure of a decision tree.
  • Build a decision tree model using appropriate software tools and libraries.
  • Train and test the model using relevant datasets.
  • Evaluate the performance of the model using appropriate metrics.
  • Refine and improve the model based on the evaluation results.
Fundamentals of Data AnalyticsReal unit · Qualifi Ltd
You'll gain foundational knowledge of data analytics, covering types such as descriptive and predictive analytics, the data analytics ecosystem, and common data quality issues. The unit also addresses basic data transformation methods to support analytics processes.

What you'll be able to do

  • Understand the processes and different types of data analytics used in various contexts.
  • Understand the components and interactions within the data analytics ecosystem.
  • Understand the common issues that affect data quality and the methods used to address them.
  • Understand the issues involved in basic data transformations and the methods used to perform them.
Graphs and Graph Data ScienceReal unit · Qualifi Ltd
You'll gain knowledge of different types of graphs, graph data models, and the graph ecosystem. The unit covers graph properties, core data models, and graph algorithms relevant to graph data science applications.

What you'll be able to do

  • Understand different types of graphs and their properties.
  • Understand the core types of graph data models.
  • Understand the graph ecosystem.
  • Understand the types of graph data science and graph algorithms.
K-means Clustering in Data ScienceReal unit · Qualifi Ltd
You'll explore the theory and practical application of k-means clustering, including the concept of centroids and the iterative clustering process. The unit also covers evaluation methods and discusses assumptions and limitations of the technique.

What you'll be able to do

  • Understand the underlying theory of k-means clustering, including its assumptions and limitations.
  • Understand the concept of centroids and how they are used in k-means clustering.
  • Understand the iterative process of assigning data points to clusters and updating centroids.
  • Understand how to evaluate the quality of k-means clusters using metrics such as the silhouette score and the Davies-Bouldin index.
  • Understand the importance of choosing an appropriate number of clusters (k).
  • Create a k-means model using appropriate software tools and libraries.
  • Pre-process data to ensure it is suitable for k-means clustering.
  • Determine the optimal number of clusters for a given dataset.
  • Evaluate the performance of the k-means model using appropriate metrics.
  • Interpret the results of the clustering and draw meaningful conclusions.
Linear Regression in Data ScienceReal unit · Qualifi Ltd
This unit introduces learners to the theory and application of linear regression in data science. You'll understand key concepts such as independent and dependent variables, perform regression calculations, evaluate models using metrics like R-squared and Mean Squared Error, and create linear regression models.

What you'll be able to do

  • Understand the basic theory underpinning linear regression
  • Understand regression metrics and how to evaluate the performance of a regression model
  • Perform regression calculations and analysis using appropriate techniques
  • Create linear regression models using relevant software tools
Logistic Regression in Data ScienceReal unit · Qualifi Ltd
This unit provides learners with a comprehensive understanding of logistic regression. You'll grasp the theory, perform calculations, create models, interpret coefficients, and assess model fit using appropriate techniques.

What you'll be able to do

  • Understand the basic theory of logistic regression.
  • Perform logistic regression calculations.
  • Create logistic regression models.
Machine Learning Methods and Models in Data ScienceReal unit · Qualifi Ltd
You'll develop an understanding of basic machine learning models, including supervised, unsupervised, and reinforcement learning. The unit covers key characteristics, applications, and distinctions between classification and regression tasks.

What you'll be able to do

  • Understand the fundamental concepts of basic supervised machine learning models and their applications.
  • Understand the fundamental concepts of basic unsupervised machine learning models and their applications.
  • Understand the fundamental concepts of basic reinforcement learning and its applications.
Python for Data ScienceReal unit · Qualifi Ltd
It teaches you to understand Python's design philosophy and core features relevant to data science. You'll develop practical skills in manipulating basic data types, including lists, tuples, sets, and dictionaries, and writing functions and flow control statements.

What you'll be able to do

  • Understand the design philosophy and key features of the Python programming language
  • Understand Python’s basic data types and their characteristics
  • Create and manipulate lists and tuples using Python
  • Create and manipulate sets and dictionaries using Python
  • Write Python functions and flow statements to control program execution
Synthetic Data for Privacy and Security in Data ScienceReal unit · Qualifi Ltd
This unit covers data privacy and security issues in data science, focusing on synthetic data and differential privacy. You'll understand legal and ethical considerations and gain skills to create anonymised or synthetic datasets to protect sensitive information.

What you'll be able to do

  • Understand the core issues related to data privacy and security in data science.
  • Understand the basics of differential privacy and its role in protecting sensitive data.
  • Understand the core issues surrounding the use of synthetic data in data science.
  • Understand the components and interactions within the synthetic data ecosystem.
  • Create anonymised or fake data using appropriate techniques and tools.
The Field of Data ScienceReal unit · Qualifi Ltd
This unit introduces learners to the field of data science covering historical to contemporary issues.

What you'll be able to do

  • Understand the core issues of data science.
  • Understand the core issues of data and big data.
  • Understand the core issues of artificial intelligence.
  • Understand the core issues of machine learning.
  • Understand the core issues of deep learning.
The Machine Learning ProcessReal unit · Qualifi Ltd
This unit provides learners with an understanding of the machine learning process, including data preparation, model evaluation, and key stages such as classification and regression. You'll explore techniques to address bias and variance and evaluate classification models effectively.

What you'll be able to do

  • Understand the overall machine learning process, from data collection to model deployment
  • Understand the data preparation process required for building effective machine learning models
  • Understand how to evaluate the performance of machine learning models using appropriate metrics
  • Evaluate classification models using relevant techniques and metrics
  • Understand the issues of bias and variance in machine learning models and how to mitigate them

The honest bit

You’ve started things before

Most courses were built for a room full of people who aren’t you. A cohort moves on whether or not your week allowed it, and by the third week the thing you’re behind on becomes the reason you stop opening it.

There’s no cohort here, and no timetable to fall behind. Before anything starts, Zavmo asks when you’re sharpest and how long you can realistically sit down for, then builds the sessions around those answers. A bad fortnight changes your pace. It doesn’t put you behind.

And you only pay once you start learning. Searching and planning are free, and you can cancel any time — so the cost of finding out is an afternoon, not a year.

Your passport

Every credit is a stamp you keep

Level 3 credits are regulated. They don’t vanish when a subscription ends or a website closes. They travel to any employer, and Zavmo keeps the map of what you’ve earned and what’s next.

Creating and Interpreting Visualisations in Data ScienceData Analytics with PythonData and Descriptive Statistics in Data ScienceDecision Trees in Data ScienceFundamentals of Data AnalyticsGraphs and Graph Data ScienceK-means Clustering in Data ScienceLinear Regression in Data Science

Each stamp is a real unit registered against this qualification.

Who’d teach you this

The Evidence Evaluator

Assessor

Helps you show what you can actually do, gathering the evidence that proves it as you learn.

Meet all twelve tutors

Where this connects

Where these credits take you

A qualification is never a dead end here. See the jobs its credits open, the national occupational standards its units map to, and the future skills they quietly build.

See Your Progress GrowIllustration
Qualifi Level 3 Diploma in Data Science
  • Assist in developing and validating machine learning solutions
  • Assist in developing data visualisations
  • Carry out data manipulation
  • Develop and communicate data visualisations
  • Develop and implement machine learning algorithms
This is your Mind Palace on learn.zavmo.ai. Every skill above comes from this role's own record, not an example borrowed from another job. A node lights up when you evidence it, and what you build stays yours between jobs. That is the part a course cannot do.

Where this can lead

Senior Synthetic Data Engineer

Your journey here is about continuous growth, whether that's becoming a deeper technical expert, a leader of people, or a strategic visionary. We're committed to providing the opportunities and support for you to build a truly impactful and rewarding career in this fascinating and critical field.

See the whole journey →
Opens the roleSenior Synthetic Data EngineerOne of the jobs this qualification is a route intoSee the role →Opens the roleLearning Analytics AssociateOne of the jobs this qualification is a route into · 1 near-identical record folded inSee the role →Opens the roleData ScientistOne of the jobs this qualification is a route into · 2 near-identical records folded inSee the role →Opens the roleMachine Learning TraineeOne of the jobs this qualification is a route intoSee the role →Opens the roleJunior Data Visualisation AnalystOne of the jobs this qualification is a route intoSee the role →Opens the roleMachine Learning SpecialistOne of the jobs this qualification is a route intoSee the role →Future FluencyAll ten future skillsSee how every unit is contextualised by the tenExplore →

Showing 6 roles, drawn from 161 job records mapped to this qualification.

Skills it covers

What the job actually needs. These are the standards the units are built against, in the words employers and awarding bodies already use for the work itself.

Assist in developing and validating machine learning solutionsAssist in developing data visualisationsCarry out data manipulationDevelop and communicate data visualisationsDevelop and implement machine learning algorithms

How you’ll actually learn this

One-to-one, on your own real work

A qualification is usually something done to you: sit the class, sit the exam, hope it sticks. Here it’s the opposite. You learn it one-to-one with a companion, on your own real work, and you keep going until you can use it, not just recall it.

One-to-one, on your real workNo lectures, no past-papers. Every unit is practised on the actual tasks your job throws at you, a tutor beside you, not a video in front of you.
Taught to the top, not the testMost courses stop at remembering. Your companion keeps climbing: analysing, judging, creating. That’s the part a machine can’t do for you.
Credits you keep, a map that continuesEvery unit is regulated and yours for good. The day you finish, Zavmo already knows the next role your new credits open.
Creating and Interpreting Visualisations in Data ScienceLevel 3

Applied to your work in any of these jobs

Which job do you mean?
Senior Synthetic Data EngineerLearning Analytics AssociateData ScientistMachine Learning TraineeJunior Data Visualisation AnalystMachine Learning Specialist

Learners will study the importance of data visualisation in analysis and communication. The unit covers basic plot types and charts, principles of effective visualisation, and develops skills to create and interpret visual data representations appropriately.

How the thinking builds
  1. Remember
  2. Understand
  3. Apply
  4. Analyse
  5. Evaluate
  6. Create
An illustration of a Zavmo lesson, built from this role’s own route. The unit, its objective and every criterion above are the awarding body’s own words, not an example.
Your PlanIllustration

Built for Qualifi Level 3 Diploma in Data Science

14 units in this credential, in the order it lists them, awarded by Qualifi Ltd.

  1. Creating and Interpreting Visualisations in Data Science
  2. Data Analytics with Python
  3. Data and Descriptive Statistics in Data Science
  4. Decision Trees in Data Science
  5. Fundamentals of Data Analytics
  6. Graphs and Graph Data Science

and 8 more in the full unit list below.

These are the real units of this credential, in its own order. Nothing here is marked done, because this plan has not been started by anyone yet. Yours would fill in as you go.

One to one, not one to many

No two people run this the same way

A course is written once and handed to everyone. This is assembled around you, and keeps changing as it learns you. Five things it reads, and what each one changes.

  1. Your actual work Every unit is taught against a live piece of your own work, not a worked example from a textbook.
  2. What you already know The first conversation finds your starting point, so you skip what you can already do and spend the time on what you cannot.
  3. The conditions you learn under Not a learning-styles quiz. The evidence does not support those. The dimensions the research does back, read once and used to shape the plan.
  4. How far you got last time It picks up mid-thought. The tutor knows what you said, what you struggled with, and what it asked you to try.
  5. Which tutor suits the moment Twelve of them, each for a different kind of thinking. The one who walks you through a first idea is not the one who stress-tests it.

See how you learn, free. Eight questions, no sign-up. A directional taster; the diagnostic inside Zavmo goes deeper and keeps adapting.

Why it sticks

Most courses stop at remembering

There’s a well-known ladder of how deeply you learn something, called Bloom’s Taxonomy. Most courses get you up the first two rungs: you remember some facts, you pass a test, you forget it. Real skill lives at the top. Judging, deciding, creating. And this isn’t a generic ladder: every rung has a named Zavmo tutor who walks you up it.

  1. 6CreateThe Creative Catalyst
  2. 5EvaluateThe Evidence Evaluator
  3. 4AnalyseThe Analyst
  4. 3ApplyThe Coach
  5. 2UnderstandThe Connector
  6. 1RememberThe Builder

Where most courses leave you Where Zavmo takes you

Why this matters: AI can already remember and understand for you. What it can’t do is take your real problem and judge the right call. So the only learning worth paying for is the learning that takes you to the top. That’s exactly what a tutor doing it with you, on your real work, is for.

The value

Why the companion is worth £70 a month

You’re not paying for the units. They’re regulated, and the same wherever you earn them. You’re paying for the one thing that decides whether you actually get there: a companion that makes them stick, on your real work.

That is one-to-one on this qualification, every day, on the work you already do, for £70 a month. Your first module is free, so you can see the teaching before you pay for any of it. Billed monthly, cancel any time and billing stops.

Earned on your own work, taught to the top.

Everything you just read, learned one-to-one on the job you already do. Your first module is free, so you can see the teaching before you pay for any of it.

Build my plan, free

No card. See how this qualification maps onto your role and meet the tutors who’d teach it, free. The learning begins when you subscribe. It’s £70 a month, billed monthly. Cancel any time and billing stops.