Data Analysis with Python: Free Course

About this course

Study Data Analysis with Python through this free English video course from freeCodeCamp. The classroom brings together all 6 public recordings from the selected source curriculum, totaling approximately 12h 6m. You can watch the teaching sequence, search the lesson list and keep track of the recordings you have completed. The course focuses on the subject named in its title. Its value comes from following the explanations and making your own small practice attempts, with enough time to inspect the result and explain the decisions involved.

Course at a glance

What you’ll learn

• Use Python functions and files in an analysis workflow
• Perform numerical operations with NumPy
• Inspect and transform tabular data with pandas
• Build explanatory charts with Matplotlib and Seaborn
• Conduct exploratory analysis of a case study
• Connect analysis code with a written interpretation

Skills you’ll gain

• Python
• NumPy
• pandas
• Data visualization

Course Overview

Explicit Parts 1–6 of 6 cover Python, files, NumPy, pandas, visualization and a final exploratory analysis.

The source lesson list includes “Data Analysis with Python: Part 1 of 6 (Live Course)”, “Data Analysis with Python: Part 4 of 6 – Analyzing tabular data with Pandas”, “Data Analysis with Python: Part 6 of 6 – Exploratory Data Analysis – A Case Study [Live Course]”. The classroom retains their original titles and positions. Introductions, supplementary explanations and complete-course recordings remain present whenever they are part of the source. A repeated recording is retained at each original position, allowing the course to be checked against the published teaching sequence.

Python preparation

Follow Parts 1 and 2 on programming foundations, functions and files. Reproduce a small transformation and keep its input, intermediate values and output easy to inspect.

Numerical computing

Work through the NumPy session. Compare array operations with a simple hand-checkable calculation and explain how shape, type and indexing affect a result.

Tabular data and visualization

Study the pandas and plotting sessions. Record how a table changes after an operation, and choose a display that makes the intended comparison visible without hiding missing information.

Exploratory case study

Follow the final case study as a connected analysis process. Translate a question into an operation, check the resulting table or figure and write down which conclusions the observations support.

Who is this for?

This course suits learners who want the specific subject described above and who can work at the stated level. It is also useful as a recorded reference when revisiting a concept or comparing a practice attempt with a demonstrated example. Beginners should give the foundational explanations time before moving to more elaborate applications. Experienced learners can use the lesson names to locate a topic, then inspect the surrounding context before applying a technique.

Before you start

Basic computer use is sufficient to start the introductory material. A Python notebook environment with the demonstrated analysis libraries is useful for optional practice. Use a fictional or public dataset you can describe.

Put the lessons into practice

Create a small fictional dataset of book loans. Load it into a notebook, inspect missing values, calculate a summary and create two useful visual comparisons. Write a short interpretation that distinguishes a descriptive pattern from a causal claim. Change one value and explain its effect on the result. Keep the analysis reproducible by recording the preparation steps and the assumptions behind each calculation.

Treat this suggested exercise as independent practice provided by WikiFree. Keep a short working note containing your inputs, assumptions, result and one point you still need to check. Revisit the relevant recording when a step is unclear, then attempt a small variation without following the demonstration line by line. A result you can explain and reproduce is a more useful study outcome than simply recognizing a familiar term. The exercise is not submitted to an instructor for grading.

Certificate / Assessment

Watching these public recordings and using WikiFree completion tracking does not award a certificate, academic credit, an examination voucher or instructor assessment. The video curriculum is the material included here. Separate platform assignments, credentials and paid services are not part of video viewing. Use the practice output to review which concepts you can explain, which steps you can repeat independently and which questions need another look at the teaching.

Frequently asked questions

Provider: freeCodeCamp. Videos play from the freeCodeCamp channel on YouTube.