How you’d get this certificate
- Zavmo teaches you. You learn the units one-to-one with an AI tutor, on your own work.
- You register with an approved centre. That is a college, training provider or employer Qualifi Ltd has approved to offer this qualification. Zavmo is not an approved centre, so you register with them, not with us.
- The centre assesses, and Qualifi Ltd awards. A qualified assessor at the centre judges your evidence, and the certificate comes from Qualifi Ltd.
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. Zavmo teaches towards them on your own work, and work done here is recognised as prior learning under the agreements we hold.
Zavmo is not an awarding body, is not an approved centre, and is not affiliated with Qualifi Ltd. It does the teaching and the mapping. Qualifi Ltd sets this qualification and awards it, and a qualified human assessor makes the assessment decision.
Free first step
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What each unit actually teaches you to do
Creating and Interpreting Visualisations in Data Science Real unit · Qualifi Ltd
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 Python Real unit · Qualifi Ltd
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 Science Real unit · Qualifi Ltd
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 Science Real unit · Qualifi Ltd
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 Analytics Real unit · Qualifi Ltd
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 Science Real unit · Qualifi Ltd
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 Science Real unit · Qualifi Ltd
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 Science Real unit · Qualifi Ltd
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 Science Real unit · Qualifi Ltd
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 Science Real unit · Qualifi Ltd
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 Science Real unit · Qualifi Ltd
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 Science Real unit · Qualifi Ltd
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 Science Real unit · Qualifi Ltd
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 Process Real unit · Qualifi Ltd
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 national standards these units are built against
What the job actually needs, in the words employers and awarding bodies already use for the work itself.
- Standard
Assist in developing and validating machine learning solutions
- Standard
Assist in developing data visualisations
- Standard
Carry out data manipulation
- Standard
Develop and communicate data visualisations
- Standard
Develop and implement machine learning algorithms
The jobs these credits are mapped to
A qualification is never a dead end here. Each of these is a job record this qualification is a mapped route into.
- 1 record
- 2 records
- 1 record
- 4 records
- 1 record
- 1 record
Showing 6 roles, drawn from 98 job records mapped to this qualification. Near-identical records are folded together, and each row says how many it stands for.
Where this can lead
Senior Synthetic Data Engineer
This work can lead into deeper technical specialism, into managing people, or into shaping data strategy at a senior level.
See the whole journeyThe credits, and who awards them
Level 3 credits are regulated, and they are awarded by Qualifi Ltd rather than by Zavmo. 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 have covered and what is next.
What you would be collecting
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 one is a real unit registered against this qualification.
How you’d practise it
You don’t watch this. You do it.
You are not assessed on remembering this. You are assessed on applying it, analysing it, evaluating it and producing something. So that is what the practice is: projects, role play and your own live cases, with a tutor in the room.
The Analyst · your map-it-properly tutor Illustration - Map a live decision Analyse
“Creating and Interpreting Visualisations in Data Science” runs on a decision you are facing now. Zavmo maps it with you and shows what the second-order effect looks like before you find out the slow way.
What you leave holding
The standards this credential builds, in the shape the product keeps them
Nothing here is marked learned. There is no learner on a public page, so the map shows what the qualification covers rather than what anybody has done.
- 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
How you would learn this
One to one, on the work you already do
A tutor sets the task against your own job and marks the reasoning, at your pace. There is no cohort to fall behind.
A lesson from Creating and Interpreting Visualisations in Data Science, one of this qualification's own units.
Applied to your work in any of these jobs
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.
- 1 Remember
- 2 Understand
- 3 Apply
- 4 Analyse
- 5 Evaluate
- 6 Create
It is £70 a month, billed monthly. Your free first module is Future Fluency, and you can cancel any time. Teaching towards this qualification begins when you subscribe.
Built for Qualifi Level 3 Diploma in Data Science
14 units in this credential, in the order it lists them, awarded by Qualifi Ltd.
- Creating and Interpreting Visualisations in Data Science
- Data Analytics with Python
- Data and Descriptive Statistics in Data Science
- Decision Trees in Data Science
- Fundamentals of Data Analytics
- Graphs and Graph Data Science
and 8 more in the full unit list below.
Free tools
Want a read on where you stand?
Three free tools, no account needed. Each one ends with a role, a level and a way in.
Learned on your own work, taught one-to-one.
Everything you just read, learned one-to-one on the job you already do. Your free first module is Future Fluency, so you can see how the teaching works before you pay for anything.
Build my plan, freeNo 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.
The title, level, credits, guided learning hours and unit learning outcomes on this page are taken from the Ofqual Register of Regulated Qualifications. Contains public sector information licensed under the Open Government Licence v3.0. Where this data comes from.