DataCamp learning tracks
Learning tracks for analysts looking to upskill using DataCamp
What is DataCamp?
Our analytical L&D offer includes full enterprise access to Datacamp, an online learning platform focused on data and analytics skills. It offers hundreds of interactive courses across Python, R, SQL, Power BI, Tableau, machine learning, data engineering, and more. The platform is designed to be hands-on: less theory, more doing. You code directly in the browser, get instant feedback, and can apply your learning through real-world projects and assessments. More information on this can be found on the DfE intranet.
How do I get access to DataCamp?
If you’d like a license, please email datacamp.requests@education.gov.uk copying your line manager. We expect everyone requesting access to have a development conversation with their manager, outlining how they plan to use - and are using - the platform to support their learning and development, making sure this is captured in their personal L&D plan.
What are learning tracks?
Learning tracks are combinations of courses designed to focus on particular skills or areas. Within DataCamp, DfE analysts can find organisation-specific learning tracks that have been designed to support their learning & development. They cover a range of skills, including AI & machine learning, using R, SQL and Python, presenting & visualising data, and using Databricks.
Below is a summary of the available tracks to help you choose the most appropriate ones for you. The tracks are tailored to skill and experience levels and you’ll find estimated duration per course and track to help you plan your time.
Summary of learning tracks
| Track Name | Target Persona | Estimated Duration | Link |
|---|---|---|---|
| GSS Track 1a: One Big Thing – AI for all: a headstart! | Anyone, for introductory AI awareness | ~7 hours | link |
| GSS Track 1b: Intermediate AI and Machine Learning | For those that have completed 1a and want more (no coding) | ~10 hours | link |
| GSS Track 2a: new to role – intro level (R Focus) | New to role, low technical skill | ~13 hours | link |
| GSS Track 2b: new to role – intro level (Python Focus) | New to role, low technical skill | ~13 hours | link |
| GSS Track 3a: new to role – intermediate level (R Focus) | New to role, intermediate technical skill | ~18 hours | link |
| GSS Track 3b: new to role – intermediate level (Python Focus) | New to role, intermediate technical skill | ~16 hours | link |
| GSS Track 4a: new to role – high technical skill (R Focus) | New to role, high technical skill | ~36 hours | link |
| GSS Track 4b: new to role – high technical skill (Python Focus) | New to role, high technical skill | ~28 hours | link |
| GSS Track 5a: experienced – low technical skill (R Focus) | Experienced, low technical skill | ~16 hours | link |
| GSS Track 5b: experienced – low technical skill (Python Focus) | Experienced, low technical skill | ~16 hours | link |
| GSS Track 6a: experienced – intermediate technical skill (R Focus) | Experienced, intermediate technical skill | ~18 hours | link |
| GSS Track 6b: experienced – intermediate technical skill (Python Focus) | Experienced, intermediate technical skill | ~16 hours | link |
| GSS Track 7a: experienced – high technical skill (R Focus) | Experienced, high technical skill | ~36 hours | link |
| GSS Track 7b: experienced – high technical skill (Python Focus) | Experienced, high technical skill | ~28 hours | link |
| GSS Track 8: Familiarising with R | Anyone, for R skills development | ~22 hours | link |
| GSS Track 9a: Databricks Foundations (Beginner) | beginner, low technical skill | ~11 hours | link |
| GSS Track 9b: Databricks for Intermediate Practitioners | intermediate, some technical skill | ~11 hours | link |
DataCamp learning tracks
GSS Track 1a: One Big Thing – AI for all: a headstart!
This track gives a general introduction to AI, and some basic machine learning concepts such as supervised vs. unsupervised learning and model evaluation. It then moves onto prompt engineering and ChatGPT, the ethics around AI use, and its use in decision making.
GSS Track 1b: Intermediate AI and Machine Learning
This track gives a general introduction to LLMs, goes over some key concepts and ethical considerations building on GSS Track 1a: One Big Thing – AI for all: a headstart!. In particular it focuses on LLMOps.
GSS Track 2-7 a (R Focus)
These tracks take you from using basic objects in R, such as vectors and matrices, through to functions and more complex uses & concepts such as machine learning & AI, all while getting you used to other necessary tools for data analysis, SQL and Git. These tracks also cover data science, data ethics and how to portray data effectively.
GSS Track 2-7 b (Python Focus)
These tracks mirror the R focus tracks above, but the basic & intermediate coding, data analysis, machine learning, feature engineering and dashboard aspects are taught using Python rather than R.
GSS Track 8: Familiarising with R
You can think of this as speedrunning general concepts in R, or a quick refresher course.
It covers the following:
- Basic data types
- Loops and functions
- Statistics in R
- Data wrangling & visualisation with tidyverse (a suite of data science tools)
You can think of this as speedrunning general concepts in R, or a quick refresher course.
GSS Track 9a: Databricks Foundations (Beginner)
This track is for anyone new to Databricks who needs to understand the Lakehouse platform, manage data reliably, learn how to use Genie, and visualize results. It covers Databricks Lakehouse architecture (medallion architecture, clusters, workspace administration, governance and sharing); how to manage data reliably with Delta Lake; how to use Databricks Genie (refining prompts, creating Genie Spaces); and how to build charts and dashboards to communicate insights.
It covers:
- Lakehouses
- Medallion architecture
- Clusters
- Administration
- Genie
GSS Track 9b: Databricks for Intermediate Practitioners
This track is for learners who already know Databricks basics and want to build production pipelines, run SQL analytics at scale, and automate Databricks with Python/AI tooling. It covers using Databricks SQL for warehousing, BI, and real-time workflows; how to build and orchestrate end-to-end ETL/ELT pipelines with Spark SQL; and how to automate Databricks operations and query AI/foundation models programmatically via the Python SDK.
It covers:
- SQL
- python
- Jobs and Pipelines