Free Data Wrangling with Python Course
Learn how raw data is cleaned, organised and reshaped with Oxford Home Study Centre. This free beginner course introduces practical data-wrangling concepts in Python, helping you understand how datasets are prepared before analysis or machine-learning work begins.
You can study online at your own pace and access the learning materials without an enrolment fee. You can also browse our complete online course catalogue, explore the wider artificial intelligence course collection or compare other free AI courses.
Data wrangling is the process of turning raw, inconsistent or awkwardly structured information into a form that is easier to work with. In practical analytics, this can include checking missing values, correcting inconsistencies, removing duplicates, changing data types, reshaping tables and preparing data for later analysis.
This course focuses on three core stages of that workflow: understanding the role of data wrangling, cleaning and preprocessing data, and transforming or reshaping datasets. It is designed to help beginners build a clear foundation without overwhelming them with advanced automation or complex data-engineering topics.
Python is widely used for this kind of work because libraries such as Pandas and NumPy make it easier to inspect, clean and transform structured data. The course introduces these ideas at a conceptual and practical level, while keeping the main emphasis on understanding the workflow rather than memorising syntax.
If you later want greater depth, you can progress to a longer programme covering dataset merging, visualisation, automation, complex data formats and applied project work.
This course may suit students, beginner Python learners, analysts, researchers, business professionals and anyone who regularly works with raw datasets and wants to understand how data is prepared before analysis.
If you want a more extensive Python data-preparation workflow, compare our advanced Python for Data Cleaning and Wrangling programme. It extends into dataset integration, visualisation, automation, complex data types and applied project work.
You can also explore our related free AI Data Preprocessing course if you want to focus specifically on missing data, cleaning and transformation before model training.
Yes. You can study the course online without an enrolment fee. Optional certificates are available separately after successful completion.
Data wrangling is the process of cleaning, transforming and reorganising raw data so that it is more suitable for analysis or machine learning.
Yes. Python-based cleaning and preprocessing concepts are central to the course.
The course introduces the role of Python libraries such as Pandas and NumPy in data manipulation and preprocessing.
Yes. These are among the common data-quality issues addressed in the cleaning and preprocessing module.
The foundation course introduces restructuring concepts, while the longer paid programme gives merging and combining datasets a dedicated module.
No. Study is free, while certificates are optional paid products.
No regulated qualification status is established for this course. CPD accreditation and QLS endorsement should not be treated as equivalent to an Ofqual-regulated qualification.
The advanced Python for Data Cleaning and Wrangling programme is the natural progression route if you want broader coverage of merging, visualisation, automation and complex data.
Who Should Take This Course?
What You Will Learn
Related Learning and Next Steps
Frequently Asked Questions
Is the Data Wrangling with Python course free?
What is data wrangling?
Does the course teach Python for data cleaning?
Will I learn Pandas and NumPy?
Does the course cover missing values and duplicates?
Does the course cover dataset merging?
Is the certificate free?
Is this a regulated qualification?
What can I study next?
By the end of this course the learner will be able to:
- Understand the definition, scope, and applications of data wrangling in data science and artificial intelligence.
- Explore the evolution of data wrangling techniques and their role in preparing data for analysis and model training.
- Learn about core data wrangling methods such as handling missing values, removing duplicates, data type conversions, and feature transformations using Python.
- Understand the differences between traditional manual data cleaning approaches and Python-based automated wrangling techniques.
- Explore the basic principles behind using Python libraries such as Pandas and NumPy for effective data manipulation and preprocessing.
- Discover how data wrangling enhances the quality, reliability, and usability of datasets for machine learning, AI applications, and business decision-making.
Free Study and Optional Certificates
You can study this course free of charge. We provide the online learning materials without a course enrolment fee, while certification remains optional.
CPD Accredited Certificate
After successful completion, you can choose to purchase a CPD Accredited Certificate. This can provide evidence of continuing professional development and may be useful for a personal learning record.
Quality Licence Scheme Endorsed Certificate
You can also choose a Quality Licence Scheme Endorsed Certificate. QLS endorsement relates to the course endorsement arrangement and is separate from regulated qualification status.
Neither certificate should be presented as an Ofqual-regulated qualification, professional licence or guarantee of employment. If you need a specific credential for work or further study, check the receiving organisation's requirements before ordering.
Certificate prices and formats can change. Review our certificate information and claiming guidance when you are ready to order.
COURSE CONTENT
Course Syllabus
Module 1: Introduction to Data Wrangling
Understand what data wrangling involves, why raw datasets often require preparation and how organised data supports more reliable analysis and decision-making.
Module 2: Data Cleaning and Preprocessing
Explore techniques for addressing errors, inconsistencies, duplicates and missing information before data is used for analysis or modelling.
Module 3: Data Transformation and Reshaping
Learn why datasets may need to be reformatted, merged or reshaped and how transformation helps make data more suitable for analytical and machine-learning tasks.
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Student Feedback
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Course Info
| Course Level | 3 |
| Awarding Body | OHSC |
| Course Duration | 200 Hours |
| Entry Requirements | Open to All |
| Start Date | Ongoing |