Technology & security
Python for Data & Automation
Build an evidence-led Python for Data & Automation foundation you can explain in a real workplace or interview.
Learn programming logic for data tasks and workplace automation through safe, explainable projects.
About this course
A beginner-friendly programme for data-minded learners who want to understand Python concepts, data structures, files, analysis workflows and automation planning. It focuses on readable thinking, testing and responsible handling of data.
Career relevance
Where this can take you
Designed for learners exploring technology careers and building a practical, honest portfolio.
Portfolio project: Create a Python for Data & Automation capstone pack that documents your purpose, method, evidence, quality check and next action.
Skills you will gain
Practical skills, not just theory
What you will learn
Break an automation problem into clear steps and data inputs.
Use core Python concepts and readable data structures.
Plan safe file, data and error-handling workflows.
Explain testing, limitations and responsible automation boundaries.
Objective map
A numbered, measurable syllabus
Every lesson, knowledge check and practical activity maps to the objectives below. Domain weights show how much of the programme each area represents.
Domain 1
Programming mindset and setup to Conditions, loops and functions
- 1.1Demonstrate programming mindset and setup in a realistic python for data & automation context: in this Python for Data & Automation module, learners work with programming mindset and setup through a guided workplace brief, a practical activity and a reviewed output.
- 1.2Demonstrate variables, types and expressions in a realistic python for data & automation context: in this Python for Data & Automation module, learners work with variables, types and expressions through a guided workplace brief, a practical activity and a reviewed output.
- 1.3Demonstrate conditions, loops and functions in a realistic python for data & automation context: in this Python for Data & Automation module, learners work with conditions, loops and functions through a guided workplace brief, a practical activity and a reviewed output.
Domain 2
Lists, dictionaries and structured data to Data cleaning and analysis thinking
- 2.1Demonstrate lists, dictionaries and structured data in a realistic python for data & automation context: in this Python for Data & Automation module, learners work with lists, dictionaries and structured data through a guided workplace brief, a practical activity and a reviewed output.
- 2.2Demonstrate files, inputs and careful outputs in a realistic python for data & automation context: in this Python for Data & Automation module, learners work with files, inputs and careful outputs through a guided workplace brief, a practical activity and a reviewed output.
- 2.3Analyse and evaluate data cleaning and analysis thinking in a realistic python for data & automation context: in this Python for Data & Automation module, learners work with data cleaning and analysis thinking through a guided workplace brief, a practical activity and a reviewed output.
Domain 3
APIs and automation boundaries to Privacy, secrets and responsible automation
- 3.1Demonstrate apis and automation boundaries in a realistic python for data & automation context: in this Python for Data & Automation module, learners work with apis and automation boundaries through a guided workplace brief, a practical activity and a reviewed output.
- 3.2Demonstrate error handling, testing and debugging in a realistic python for data & automation context: in this Python for Data & Automation module, learners work with error handling, testing and debugging through a guided workplace brief, a practical activity and a reviewed output.
- 3.3Demonstrate privacy, secrets and responsible automation in a realistic python for data & automation context: in this Python for Data & Automation module, learners work with privacy, secrets and responsible automation through a guided workplace brief, a practical activity and a reviewed output.
Domain 4
Version control and project communication to Final Python and automation capstone
- 4.1Apply and control version control and project communication in a realistic python for data & automation context: in this Python for Data & Automation module, learners work with version control and project communication through a guided workplace brief, a practical activity and a reviewed output.
- 4.2Complete and defend portfolio lab: data automation proposal in a realistic python for data & automation context: in this Python for Data & Automation module, learners work with portfolio lab: data automation proposal through a guided workplace brief, a practical activity and a reviewed output.
- 4.3Complete and defend final python and automation capstone in a realistic python for data & automation context: in this Python for Data & Automation module, learners work with final python and automation capstone through a guided workplace brief, a practical activity and a reviewed output.
How the programme works
Learn, practise, explain and prove it
This is a long-form, eight-week guided programme rather than a stack of short articles. Each module contains six substantial lessons that move from concepts and methods into a worked case, a quality clinic, a workplace lab and assessed reflection.
1. Guided learning
Study the purpose, process, risks and professional standard through structured readings and active notes.
2. Worked cases
Compare plausible options, expose missing evidence and defend a decision under realistic constraints.
3. Quality clinics and labs
Diagnose weak work, revise it against a rubric and create portfolio evidence with a full audit trail.
4. Hard assessment
Pass applied checks and the final assessment at 85%, with no unresolved critical-error questions.
Curriculum
A complete programme, module by module
The curriculum below shows the actual learning pathway, objective and evidence for every module. Full lessons remain locked until enrolment.
Build the base
Standards, vocabulary and the core method you will use throughout the programme.
01Programming mindset and setupIn this Python for Data & Automation module, learners work with programming mindset and setup through a guided workplace brief, a practical activity and a reviewed output.+
Measured objectiveDemonstrate programming mindset and setup in a realistic python for data & automation context: in this Python for Data & Automation module, learners work with programming mindset and setup through a guided workplace brief, a practical activity and a reviewed output.
Evidence you will produceA checked working paper, analysis or report for programming mindset and setup, including source data, assumptions, calculations, findings and limitations.
Six guided lessons + worked case + quality clinic + workplace lab + hard knowledge check
02Variables, types and expressionsIn this Python for Data & Automation module, learners work with variables, types and expressions through a guided workplace brief, a practical activity and a reviewed output.+
Measured objectiveDemonstrate variables, types and expressions in a realistic python for data & automation context: in this Python for Data & Automation module, learners work with variables, types and expressions through a guided workplace brief, a practical activity and a reviewed output.
Evidence you will produceA checked working paper, analysis or report for variables, types and expressions, including source data, assumptions, calculations, findings and limitations.
Six guided lessons + worked case + quality clinic + workplace lab + hard knowledge check
03Conditions, loops and functionsIn this Python for Data & Automation module, learners work with conditions, loops and functions through a guided workplace brief, a practical activity and a reviewed output.+
Measured objectiveDemonstrate conditions, loops and functions in a realistic python for data & automation context: in this Python for Data & Automation module, learners work with conditions, loops and functions through a guided workplace brief, a practical activity and a reviewed output.
Evidence you will produceA checked working paper, analysis or report for conditions, loops and functions, including source data, assumptions, calculations, findings and limitations.
Six guided lessons + worked case + quality clinic + workplace lab + hard knowledge check
Milestone: You can explain the fundamentals and complete a first guided task.
Develop core skills
The working skills and tools of the role, practised one at a time.
04Lists, dictionaries and structured dataIn this Python for Data & Automation module, learners work with lists, dictionaries and structured data through a guided workplace brief, a practical activity and a reviewed output.+
Measured objectiveDemonstrate lists, dictionaries and structured data in a realistic python for data & automation context: in this Python for Data & Automation module, learners work with lists, dictionaries and structured data through a guided workplace brief, a practical activity and a reviewed output.
Evidence you will produceA checked working paper, analysis or report for lists, dictionaries and structured data, including source data, assumptions, calculations, findings and limitations.
Six guided lessons + worked case + quality clinic + workplace lab + hard knowledge check
05Files, inputs and careful outputsIn this Python for Data & Automation module, learners work with files, inputs and careful outputs through a guided workplace brief, a practical activity and a reviewed output.+
Measured objectiveDemonstrate files, inputs and careful outputs in a realistic python for data & automation context: in this Python for Data & Automation module, learners work with files, inputs and careful outputs through a guided workplace brief, a practical activity and a reviewed output.
Evidence you will produceA checked working paper, analysis or report for files, inputs and careful outputs, including source data, assumptions, calculations, findings and limitations.
Six guided lessons + worked case + quality clinic + workplace lab + hard knowledge check
06Data cleaning and analysis thinkingIn this Python for Data & Automation module, learners work with data cleaning and analysis thinking through a guided workplace brief, a practical activity and a reviewed output.+
Measured objectiveAnalyse and evaluate data cleaning and analysis thinking in a realistic python for data & automation context: in this Python for Data & Automation module, learners work with data cleaning and analysis thinking through a guided workplace brief, a practical activity and a reviewed output.
Evidence you will produceA checked working paper, analysis or report for data cleaning and analysis thinking, including source data, assumptions, calculations, findings and limitations.
Six guided lessons + worked case + quality clinic + workplace lab + hard knowledge check
Milestone: You can carry out routine work independently and check it properly.
Apply it in realistic scenarios
Case studies and simulations with competing priorities and incomplete information.
07APIs and automation boundariesIn this Python for Data & Automation module, learners work with apis and automation boundaries through a guided workplace brief, a practical activity and a reviewed output.+
Measured objectiveDemonstrate apis and automation boundaries in a realistic python for data & automation context: in this Python for Data & Automation module, learners work with apis and automation boundaries through a guided workplace brief, a practical activity and a reviewed output.
Evidence you will produceA checked working paper, analysis or report for apis and automation boundaries, including source data, assumptions, calculations, findings and limitations.
Six guided lessons + worked case + quality clinic + workplace lab + hard knowledge check
08Error handling, testing and debuggingIn this Python for Data & Automation module, learners work with error handling, testing and debugging through a guided workplace brief, a practical activity and a reviewed output.+
Measured objectiveDemonstrate error handling, testing and debugging in a realistic python for data & automation context: in this Python for Data & Automation module, learners work with error handling, testing and debugging through a guided workplace brief, a practical activity and a reviewed output.
Evidence you will produceA checked working paper, analysis or report for error handling, testing and debugging, including source data, assumptions, calculations, findings and limitations.
Six guided lessons + worked case + quality clinic + workplace lab + hard knowledge check
09Privacy, secrets and responsible automationIn this Python for Data & Automation module, learners work with privacy, secrets and responsible automation through a guided workplace brief, a practical activity and a reviewed output.+
Measured objectiveDemonstrate privacy, secrets and responsible automation in a realistic python for data & automation context: in this Python for Data & Automation module, learners work with privacy, secrets and responsible automation through a guided workplace brief, a practical activity and a reviewed output.
Evidence you will produceA checked working paper, analysis or report for privacy, secrets and responsible automation, including source data, assumptions, calculations, findings and limitations.
Six guided lessons + worked case + quality clinic + workplace lab + hard knowledge check
Milestone: You can make and justify decisions in realistic situations.
Prove it and plan your next step
Portfolio evidence, final review and a development plan you can show an employer.
10Version control and project communicationIn this Python for Data & Automation module, learners work with version control and project communication through a guided workplace brief, a practical activity and a reviewed output.+
Measured objectiveApply and control version control and project communication in a realistic python for data & automation context: in this Python for Data & Automation module, learners work with version control and project communication through a guided workplace brief, a practical activity and a reviewed output.
Evidence you will produceA checked working paper, analysis or report for version control and project communication, including source data, assumptions, calculations, findings and limitations.
Six guided lessons + worked case + quality clinic + workplace lab + hard knowledge check
11Portfolio lab: data automation proposalIn this Python for Data & Automation module, learners work with portfolio lab: data automation proposal through a guided workplace brief, a practical activity and a reviewed output.+
Measured objectiveComplete and defend portfolio lab: data automation proposal in a realistic python for data & automation context: in this Python for Data & Automation module, learners work with portfolio lab: data automation proposal through a guided workplace brief, a practical activity and a reviewed output.
Evidence you will produceA checked working paper, analysis or report for portfolio lab: data automation proposal, including source data, assumptions, calculations, findings and limitations.
Six guided lessons + worked case + quality clinic + workplace lab + hard knowledge check
12Final Python and automation capstoneIn this Python for Data & Automation module, learners work with final python and automation capstone through a guided workplace brief, a practical activity and a reviewed output.+
Measured objectiveComplete and defend final python and automation capstone in a realistic python for data & automation context: in this Python for Data & Automation module, learners work with final python and automation capstone through a guided workplace brief, a practical activity and a reviewed output.
Evidence you will produceA checked working paper, analysis or report for final python and automation capstone, including source data, assumptions, calculations, findings and limitations.
Six guided lessons + worked case + quality clinic + workplace lab + hard knowledge check
Milestone: You have portfolio-ready evidence and a clear next step.
Suggested pace
Eight-week study plan
Plan for roughly 5–9 hours each week across readings, worked cases, practice, portfolio production and assessment remediation. Repeat a week when the knowledge checks show a gap.
Week 1
Programming mindset and setup to Variables, types and expressions
- •Complete the six guided lessons for Programming mindset and setup and Variables, types and expressions and build structured notes against the objective codes.
- •Analyse the worked case, compare two defensible options and submit a reasoned decision record.
- •Complete the quality clinic, correct the weak draft and document every revision against the rubric.
- •Produce the programming mindset and setup and variables, types and expressions portfolio evidence and complete the hard knowledge checks.
- •Review every incorrect answer, explain why each distractor fails and schedule targeted retrieval practice.
Checkpoint: Reach at least 85% on the week’s checks, resolve every critical error and save the reviewed portfolio evidence before continuing.
Week 2
Conditions, loops and functions
- •Complete the six guided lessons for Conditions, loops and functions and build structured notes against the objective codes.
- •Analyse the worked case, compare two defensible options and submit a reasoned decision record.
- •Complete the quality clinic, correct the weak draft and document every revision against the rubric.
- •Produce the conditions, loops and functions portfolio evidence and complete the hard knowledge checks.
- •Review every incorrect answer, explain why each distractor fails and schedule targeted retrieval practice.
Checkpoint: Reach at least 85% on the week’s checks, resolve every critical error and save the reviewed portfolio evidence before continuing.
Week 3
Lists, dictionaries and structured data to Files, inputs and careful outputs
- •Complete the six guided lessons for Lists, dictionaries and structured data and Files, inputs and careful outputs and build structured notes against the objective codes.
- •Analyse the worked case, compare two defensible options and submit a reasoned decision record.
- •Complete the quality clinic, correct the weak draft and document every revision against the rubric.
- •Produce the lists, dictionaries and structured data and files, inputs and careful outputs portfolio evidence and complete the hard knowledge checks.
- •Review every incorrect answer, explain why each distractor fails and schedule targeted retrieval practice.
Checkpoint: Reach at least 85% on the week’s checks, resolve every critical error and save the reviewed portfolio evidence before continuing.
Week 4
Data cleaning and analysis thinking
- •Complete the six guided lessons for Data cleaning and analysis thinking and build structured notes against the objective codes.
- •Analyse the worked case, compare two defensible options and submit a reasoned decision record.
- •Complete the quality clinic, correct the weak draft and document every revision against the rubric.
- •Produce the data cleaning and analysis thinking portfolio evidence and complete the hard knowledge checks.
- •Review every incorrect answer, explain why each distractor fails and schedule targeted retrieval practice.
Checkpoint: Reach at least 85% on the week’s checks, resolve every critical error and save the reviewed portfolio evidence before continuing.
Week 5
APIs and automation boundaries to Error handling, testing and debugging
- •Complete the six guided lessons for APIs and automation boundaries and Error handling, testing and debugging and build structured notes against the objective codes.
- •Analyse the worked case, compare two defensible options and submit a reasoned decision record.
- •Complete the quality clinic, correct the weak draft and document every revision against the rubric.
- •Produce the apis and automation boundaries and error handling, testing and debugging portfolio evidence and complete the hard knowledge checks.
- •Review every incorrect answer, explain why each distractor fails and schedule targeted retrieval practice.
Checkpoint: Reach at least 85% on the week’s checks, resolve every critical error and save the reviewed portfolio evidence before continuing.
Week 6
Privacy, secrets and responsible automation
- •Complete the six guided lessons for Privacy, secrets and responsible automation and build structured notes against the objective codes.
- •Analyse the worked case, compare two defensible options and submit a reasoned decision record.
- •Complete the quality clinic, correct the weak draft and document every revision against the rubric.
- •Produce the privacy, secrets and responsible automation portfolio evidence and complete the hard knowledge checks.
- •Review every incorrect answer, explain why each distractor fails and schedule targeted retrieval practice.
Checkpoint: Reach at least 85% on the week’s checks, resolve every critical error and save the reviewed portfolio evidence before continuing.
Week 7
Version control and project communication to Portfolio lab: data automation proposal
- •Complete the six guided lessons for Version control and project communication and Portfolio lab: data automation proposal and build structured notes against the objective codes.
- •Analyse the worked case, compare two defensible options and submit a reasoned decision record.
- •Complete the quality clinic, correct the weak draft and document every revision against the rubric.
- •Produce the version control and project communication and portfolio lab: data automation proposal portfolio evidence and complete the hard knowledge checks.
- •Review every incorrect answer, explain why each distractor fails and schedule targeted retrieval practice.
Checkpoint: Reach at least 85% on the week’s checks, resolve every critical error and save the reviewed portfolio evidence before continuing.
Week 8
Final Python and automation capstone
- •Complete the six guided lessons for Final Python and automation capstone and build structured notes against the objective codes.
- •Analyse the worked case, compare two defensible options and submit a reasoned decision record.
- •Complete the quality clinic, correct the weak draft and document every revision against the rubric.
- •Produce the final python and automation capstone portfolio evidence and complete the hard knowledge checks.
- •Review every incorrect answer, explain why each distractor fails and schedule targeted retrieval practice.
Checkpoint: Submit the capstone evidence pack, defend the key decision, remediate every weak objective and pass the final assessment at 85% with no critical misses.
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Assessment and certificate
A simulated automation case and final knowledge assessment. It does not provide access to external systems or authorise use of employer data.
Who this is for
- ✓Aspiring data analysts and junior automation learners.
- ✓Professionals who want to understand programming workflows.
- ✓Job seekers building entry-level technical project evidence.