Data Analysis with Python: Free Course

About this course

Apply Python to data cleaning, exploration and visualisation with this free Learn Skills Daily course. The complete beginner, intermediate and advanced classes introduce a practical analysis workflow using a notebook environment and commonly used data libraries. Basic Python knowledge is recommended before starting.

Course at a glance

What you’ll learn

• Work with a dataset in a Python notebook and identify the structure of the information being analysed.
• Use the course’s data-handling and cleaning demonstrations to prepare a more consistent dataset.
• Explore and visualise information while relating the output to a specific analytical question.
• Document transformations and assumptions so that an analysis can be checked and reproduced.

Skills you’ll gain

• Python data analysis
• JupyterLab
• pandas
• Data cleaning
• Exploratory analysis
• Data visualisation

Course Overview

The publisher identifies JupyterLab, NumPy, pandas and seaborn as part of the course context. The training moves from working with a dataset to cleaning, manipulating, exploring and presenting information that can support a decision. Follow the introductory analysis class first, then continue through the intermediate and advanced sessions. Keep the original data available and make each transformation understandable. A useful analysis records how a result was produced and what limitations remain, rather than relying on an attractive chart or a summary whose assumptions are hidden.

How to study the course

• Begin with the notebook and data-handling foundations, then work through the later classes. Inspect a small sample after each transformation.
• Use visualisation to answer a stated question. Check the underlying values and labels before interpreting a pattern or presenting a conclusion.

Use the searchable classroom lesson list to revisit a specific explanation. Pause after a useful demonstration, reproduce a small example and explain the result in your own words. Keep a short note of what worked, what changed and what you still need to understand before moving on.

Who is this for?

This course is for analysts, operational professionals and technical learners who already know basic Python and want to apply it to data. It is useful for moving from a script to a more explainable analytical workflow.

Before you start

Familiarity with Python variables, collections, functions and control flow is helpful. A suitable notebook environment and libraries are needed for practice. The public videos are free; any separate provider resources or service subscriptions are not included.

Put the lessons into practice

Use a small public or invented dataset about workshop attendance. Preserve the original file, inspect its fields and identify an inconsistency that needs cleaning. Create a summary answering one clear question, then make a visualisation that supports that answer. Check the numbers independently for a few records and record how missing values are treated. Keep the notebook readable enough for another learner to follow the transformations. Finish with a short conclusion that states the evidence and its limits.

This optional activity is provided by WikiFree for independent practice. It is not a graded assignment from the instructor. Keep your working notes, check the result against the task you defined and return to the relevant lesson when an assumption or technique is unclear.

Certificate / Assessment

Watching the public videos and using WikiFree progress tracking does not award a certificate, exam voucher, academic credit or instructor assessment.

Use your practice output to review what you can now explain and repeat independently. Completion tracking records your study progress; it does not replace an assessed qualification or professional experience.

Frequently asked questions

Provider: Learn Skills Daily. Videos play from the Learn Skills Daily channel on YouTube.