Getting Started With Python For Data Science
Learn Python for practical data analysis with pandas
Getting Started With Python For Data Science is a beginner-friendly course from Codecademy that introduces Python programming and core data science concepts through hands-on work with real datasets using pandas and Jupyter Notebook.
The curriculum focuses on practical data analysis tasks—exploring, sorting, filtering, and transforming data—with short projects and quizzes to reinforce learning. You’ll practice end-to-end workflows in Jupyter Notebook and build confidence using pandas through guided, applied exercises.
At a Glance
Getting Started with Python for Data Science is a beginner-focused course taught by Ada Morse and the Codecademy data science team.
It teaches practical data-analysis workflows using Python and pandas, with hands-on work in Jupyter Notebook to explore, filter, and transform real datasets.
| Level | Beginner |
| Rating | 4.6 out of 5 |
| Duration | 5+ hours |
| Languages | English |
| Learners | 100K+ learners |
| Certificate | Certificate of completion (available with Plus or Pro) |
| Access | Free access to course content; additional features and certificate require a paid subscription |
| Course includes |
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| Price | Free with optional paid subscription for certificates and extra features |
What This Course Teaches
Outcomes are framed as measurable competencies so learners can evaluate progress by task completion rather than vague familiarity.
By the end of the course you will be able to analyze and summarize datasets, apply targeted sorting and filtering to find relevant records, and transform raw columns into analytics-ready tables using Python and pandas.
How the Course Is Structured
The course is organized into three core lessons plus a brief ‘Next Steps’ wrap-up, presenting practical activities and guided practice across a compact syllabus.
Overall, the material is arranged into four sections and is designed to be completed in roughly 7 hours of self-paced work.
Curriculum overview
01 Exploring Data with Python ▾
Get started exploring datasets using Python, pandas, and Jupyter Notebook.
02 Sorting and Filtering Rows ▾
Learn how to structure data by sorting rows and how to zoom in on important data using Boolean filters.
03 Cleaning and Transforming Columns ▾
Learn how to transform raw columns of data into analytics-ready tables using column-based calculations.
04 Next Steps ▾
You’ve completed Getting Started with Python for Data Science! What’s next?
Audience & Requirements
This course is aimed at beginners who want practical, task-focused Python skills specifically for data analysis rather than general software development.
Typical learners include career changers, early-career analysts, professionals who handle ad-hoc datasets, and students seeking hands-on practice with pandas and Jupyter Notebook.
- Beginners with no prior coding experience who want practical data-analysis skills.
- Career changers preparing for entry-level data roles.
- Professionals who need to analyze datasets for work (marketing, operations, product).
- Students seeking hands-on experience with pandas and Jupyter Notebook.
- No prerequisites required; the course is designed for beginners.
- Comfort following interactive exercises in Jupyter Notebook (the course uses this environment).
- Optional: Codecademy Plus or Pro subscription if you want a certificate or access to additional paid features.
Final Verdict
This compact course is a practical, hands-on introduction to Python for data analysis and is a good fit for beginners and career changers who want to build usable skills quickly. Given its strong rating and broad learner adoption, it presents a low-risk way to learn pandas and Jupyter Notebook through short projects and guided practice.
For learners seeking a fast, applied entry point into data science, this course is worth taking; the free core content lets you evaluate the format, and a paid subscription adds a certificate and extra features if you want formal recognition.
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