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Learning
Set Up a Python Data Analysis Environment → Publish the Analysis as a Portfolio Piece
This path takes career switchers with no programming background to a working command of Python for data analysis. It starts with environment setup and core syntax, builds through NumPy, pandas, data cleaning, visualization, and SQL integration, and finishes with a reproducible end-to-end analysis project that can be shown to employers. Each node includes concrete, well-known learning resources such as the official Python tutorial, Automate the Boring Stuff, Python for Data Analysis, pandas and NumPy documentation, Kaggle Learn micro-courses, and freeCodeCamp's data analysis course. The final milestone is a published notebook that answers a real question with data, which serves as the portfolio piece for a career transition.
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16 steps · 4 stages. Click any step to inspect it and see it on the Path Map.
Curated materials referenced by this learning path.
The official, authoritative introduction to Python covering syntax, data structures, control flow, functions, modules, and errors. Use it as the primary reference while working through the early nodes.
Open resource →Al Sweigart's beginner-friendly book teaches Python through practical tasks such as reading files and handling errors. Chapters on functions, lists, dictionaries, and file I/O map directly onto the early nodes of this path.
Open resource →The definitive book on pandas and NumPy by the creator of pandas, freely readable online. Chapters on NumPy arrays, DataFrames, data cleaning, aggregation, and time series align with the core of this path.
Open resource →Official NumPy introduction covering array creation, indexing, shape, dtype, and vectorized operations. A concise companion to the NumPy node.
Open resource →The official pandas guide with sections on data structures, indexing, merging, groupby, reshaping, and time series. The reference to return to whenever a DataFrame operation is unclear.
Open resource →A short, free, exercise-driven micro-course on pandas covering DataFrames, indexing, summary functions, and groupby. Good for deliberate practice between reading the book and doing the project.
Open resource →Free hands-on course on plotting with seaborn and matplotlib, including line charts, bar charts, heatmaps, scatter plots, and distributions. Exercises use real datasets.
Open resource →Official matplotlib tutorial on creating figures, axes, labels, legends, and saving output. Use alongside the visualization node to move beyond default chart styling.
Open resource →A free, example-driven SQL tutorial covering SELECT, WHERE, GROUP BY, JOIN, and subqueries against real datasets. Suitable for learners who need SQL to pull analysis-ready data.
Open resource →Official documentation for Jupyter Notebook and JupyterLab, including how notebooks execute, how to manage kernels, and how to export notebooks to other formats.
Open resource →A free certification course covering NumPy, pandas, reading data from files and databases, and data cleaning, with five required projects that provide end-to-end practice.
Open resource →Official guidance on writing a repository README, including structure, formatting, and what readers expect. Useful when publishing the final analysis notebook as a portfolio piece.
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