Essentials
8 guided hoursModules 1-2. An introduction with practical exercises and a small reviewed artifact.
AUTOMATION · BEGINNER TO INTERMEDIATE
Automate with Python
Make repetitive work a script away.
Learn Python by building useful tools for files, spreadsheets, reports and everyday tasks. Start with the basics, then make your scripts dependable.
COURSE OVERVIEW
What will I learn in the Python automation course?
Start with Python basics, then work on file organisation, CSV reporting and authorised API requests. The final project is a weekly report assistant with validation, a run log and setup instructions.
OPTIONAL AI PRACTICE
Apply AI to a relevant task.
Selected modules include an optional prompt-and-review exercise. Verify the result using the underlying skills taught in this course.
Explore the Prompting FoundationA STARTING POINT THAT FITS
Three learning levels.
Practitioner
24 guided hours totalModules 1-6, including Essentials. Complete the core labs and an assessed practical project.
Advanced
12 additional guided hoursTwo extension modules and a four-hour project clinic after practitioner readiness.
These are curriculum planning options. We confirm the timetable, delivery format, fees and required accounts before enrolment. Practitioner includes Essentials; the two durations are not added together.
Download the Curriculum PDFCheck your starting point
- Create and run a one-line Python file. Essentials if running scripts is new.
- Explain a function and a loop over dictionaries. Practitioner when basic syntax is understandable.
- Write a malformed-input test and explain an exception policy. Advanced after independent practitioner work.
WHAT YOU’LL LEARN
Skills to put into practice.
- Write readable Python using functions, loops and collections.
- Organise files and transform CSV data without changing the originals.
- Connect to an authorised API and handle missing or invalid responses.
- Test, document and safely rerun an automation.
Who this is for
Students, office professionals and new developers who want to replace repetitive computer tasks with reusable code.
Before you begin
Comfort using a computer and managing files. No prior Python experience is needed; basic programming knowledge helps.
THE COURSE OUTLINE
A clear path, module by module.
Each module connects a concept to a practical task. Open a module to see its topics and exercise.
01Your first useful script
- Set up Python and a code editor; run a script.
- Work with variables, strings, numbers and input.
- Read errors and inspect values while debugging.
What you will understand
A value has a type that controls which operations make sense. The string "12" is text, while the integer 12 supports arithmetic. Keyboard input starts as text, so convert it deliberately and handle an invalid conversion. Print small intermediate values while learning instead of hiding every step in one expression. Python runs a script in the interpreter you selected. Read an error message from its final line, then use the traceback to find the relevant file and line. A syntax error prevents parsing, while a runtime exception arises during execution. Save your file and rerun it after each small change. A function gives a named operation explicit inputs and an output. Separating calculation from input prompts makes the calculation easier to test. A function that adds costs should return a number rather than depend on the terminal. The calling code decides how to display the result.
PUT IT INTO PRACTICE
Build a small calculator for a task you do regularly.
02Logic you can reuse
- Use conditions, loops, lists and dictionaries.
- Break a task into functions with clear inputs and outputs.
- Validate input and handle exceptions.
What you will understand
A list keeps an ordered series of items, while a dictionary connects keys to values. Expense rows fit a list of dictionaries because every row uses the same named fields. Choose a data shape that makes the task visible. Clear field names improve readability more than clever positional indexing. A loop processes each item and a condition decides how to handle it. Validate an expense before adding it to a total. Place the update in the correct branch so rejected rows do not enter the calculation. When debugging, trace one row through the condition and update before running the full list. Give a summary function the rows it needs and return the result. Avoid relying on changing global variables. Separate parsing, validation and aggregation so each can be tested independently. A missing category or invalid amount needs an explicit policy, not a silent assumption inside the loop.
PUT IT INTO PRACTICE
Turn a list of expenses into a category summary.
03Files, folders and safe changes
- Read and write text with pathlib and context managers.
- Filter, rename and organise files using explicit rules.
- Add a dry-run preview and keep backups before changing files.
What you will understand
A Path represents a filesystem location, not the content of a file. Use an explicit practice root and join paths deliberately. Relative paths depend on the working directory, so show the resolved practice location before changing files. Keep the source folder and output folder separate. Build a list of intended moves first. A dry run prints the source and destination without moving anything. Check naming collisions and skip unrecognised files. The learner can review the plan and decide whether to apply it. Separate planning from execution so a test can inspect the plan without causing changes. Two source files can target the same destination name. Decide whether to reject, rename or ask for review before executing. Silently overwriting loses information. An exercise organiser should reject an existing destination and keep a clear log of moved and skipped files. Restrict its operation to disposable copies.
PUT IT INTO PRACTICE
Create a file organiser that previews every move before applying it.
04Spreadsheet and report workflows
- Read CSV data and check column names and types.
- Handle blank cells, duplicates and inconsistent values.
- Produce a clean output file and a readable summary.
What you will understand
A CSV is a text table whose headers define field names. Use a CSV parser rather than splitting lines on commas because quoted fields can contain commas. Validate required columns before reading values. Agree on the row identifier, date format and amount representation so the report has a consistent meaning. Binary floating-point values can produce small representation differences. Use Decimal when exact decimal money arithmetic matters. Validate missing, nonnumeric and non-finite values before summing. A missing amount differs from a legitimate zero. Preserve rejected rows and explain why they did not contribute to the total. Decide what one row represents before removing duplicates. In the fixture, order_id identifies one order. A duplicate order must not count twice, while two different orders with the same amount remain valid. Reconcile input rows, accepted rows and excluded rows to make the final total auditable.
PUT IT INTO PRACTICE
Convert a sample sales CSV into a validated weekly report.
05APIs and repeatable workflows
- Understand HTTP requests, status codes and JSON.
- Use an authorised sample API with timeouts and error handling.
- Keep credentials outside source code; plan retries and rate limits.
What you will understand
A request can fail before receiving a response, receive a non-success status or return invalid data despite a success status. Check each stage explicitly. Set a timeout so a classroom script does not wait indefinitely. Validate the response shape before using a field, and record a useful diagnostic message. Begin with saved sample JSON to learn parsing and validation without accounts or network access. A live extension requires an authorised training endpoint. Keep a credential in an environment variable or approved secret store, outside source files and student screenshots. A placeholder string is not a working credential. Retry only failures that the service permits and that may be transient. Keep retries bounded and respect rate-limit guidance. Repeating a read differs from repeating a write that might create a record. Preserve one run identifier and explain whether the requested operation is safe to repeat.
PUT IT INTO PRACTICE
Fetch sample records and save a summary with a run log.
06Test, package and hand over
- Test normal, empty and malformed inputs.
- Use configuration, logging and repeat-run checks.
- Write setup steps and explain when human review is needed.
What you will understand
A test compares a result with an independently chosen expectation. Check normal, empty, malformed and duplicate inputs. Avoid calculating the expected value with the same code under test, because both can share an error. A regression test should fail before a bug fix and pass after it. Use explicit input and output paths, deterministic category ordering and predictable exception reporting. A second run should not count records twice or change the source. Record the Python version and commands needed to reproduce the result. Configuration should name the task inputs without hiding important assumptions. A README should explain setup, a sample command, expected output and limitations. Include the input contract and the duplicate policy. Show how to diagnose a failed validation. Ask a peer to follow the instructions from a clean practice folder and record where the instructions need improvement.
PUT IT INTO PRACTICE
Demonstrate the capstone with a test checklist and a short README.
OPTIONAL AI PRACTICE
Try this brief
Review this sample Python report script against the requirements. Suggest edge cases and explain any proposed changes before editing.
Check the result
Run the script and its checks yourself. Confirm input files are unchanged and totals match the sample data.
AFTER THE CORE COURSE
Advanced extension.
07Advanced robust pipelines and data contracts
- Pipeline stages
- Atomic output
- Schema evolution
ADVANCED PRACTICE
Versioned report pipeline
08Advanced scheduling, observability and recovery
- Scheduling boundaries
- Observability
- Recovery
ADVANCED PRACTICE
Recovery drill
Complete both four-hour extension modules and a four-hour project clinic. Entry uses the core project and readiness evidence.
BRING THE SKILLS TOGETHER
A reusable weekly report assistant
Build a script that checks a folder of sample CSVs, validates the data and creates a summary report. Include a preview mode, error log and clear instructions.
Your project deliverables
- Python source and sample input files
- Validated output and test cases
- README with setup, limits and rerun instructions
A learning project using sample or permitted material. The final scope is agreed for your learning plan.
KEEP EXPLORING
Useful official resources.
Reference material to complement the Hawa Enterprises curriculum. Account features may vary; basic-feature or manual alternatives support the core exercises.
Course outline published by Hawa Enterprises. Updated . Ask about the learning plan.
GOOD TO KNOW
Before you take the next step.
Will I build real scripts?
The outline is centred on small working scripts and a final report automation. Bring a repetitive task you would like to explore; its suitability is discussed before the course.
Will scripts change my work files?
Exercises should start with sample data or copies. Preview modes, backups and explicit approval for file changes are part of the curriculum.
What are the fees, schedule and delivery format?
These details are confirmed by email before you commit. The curriculum gives guided-hour options for Essentials, Practitioner and Advanced. Actual schedules, fees and delivery arrangements are confirmed before enrolment. Ask about the course you want and your preferred timeline.
How do I enrol?
Choose a course and complete the enrollment wizard with your name, email and WhatsApp number. Review your details before sending the enquiry. Hawa Enterprises will discuss your starting level, goals and course arrangements before enrolment is confirmed.
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