#TomeletPicks · Picks from the Humble Bundle
Picks from the Humble Bundle: Data Science
Five books for taking a dataset from CSV to a clear result
View the bundle on Humble BundleThe short take
Start with Python for Data Analysis if you already know some Python and have a dataset to clean. If you’re choosing the entry tier, use Data Science from Scratch to understand the models or Fundamentals of Data Visualization to explain a result.
For analysts who can read some Python and want a practical route from a raw file to a result someone else can read or reproduce.
Read them in TomeletTwo strong starting points in the entry tier
Data Science from Scratch and Fundamentals of Data Visualization are both in the three-book tier. Move up to the middle tier if you have a recurring file task you want to automate. The full tier includes Python for Data Analysis and Software Engineering for Data Scientists, our choices for cleaning a dataset and making an analysis repeatable.
Read with one dataset open
Choose a dataset you know well enough to spot an odd result. Clean it, combine two tables and make one chart that answers a question. When you can repeat those steps, put the cleaning logic into a script or add a test for the input most likely to break.
Our 5 picks, ranked
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No. 1
Python for Data Analysis, Third Edition
Start here if you have a folder of CSVs and a question to answer. McKinney gives merging, reshaping and missing data the space they deserve. The case studies let you follow those steps through to a useful result.
Read it if you know some Python and want to turn a messy dataset into a repeatable analysis.
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No. 2
Fundamentals of Data Visualization
Keep this within reach when you write a report. The paired examples give you a way to check a chart before sharing it. Start with the chapters on your chart type and on the kind of uncertainty your reader needs to see.
Read it if you have results and want to make the evidence easier to read.
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No. 3
Data Science from Scratch, Second Edition
Try this when you can run a library function and want to understand what it does. Writing a small model yourself gives you a chance to notice which inputs and assumptions matter.
Read it if you know basic Python and want to work through the maths behind common models.
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No. 4
Data Science at the Command Line, Second Edition
Pick one recurring file task and turn it into a command you can rerun. That gives this book a job on day one, especially if you regularly download files, inspect fields or convert between CSV and JSON.
Read it if you use a terminal and want to repeat routine data work with less manual effort.
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No. 5
Software Engineering for Data Scientists
Open this when a notebook needs to become something your team can run. The chapters on code quality, tests and packaging give you concrete places to start with a project you already have.
Read it if you have an analysis that needs to run reliably or be shared with colleagues.
Make room for your own thinking
Read these picks in Tomelet.
Highlight a passage. Keep a note with the text. Come back to an idea when you need it.
Free reader. No account required. Books are obtained separately.
- Get the bundle from Humble Bundle before the offer ends, or buy one book on its own from its seller link above.
- Download the EPUB or PDF files to your device. A store’s own reading app is not the file download.
- Open Tomelet and import the files. Already own compatible copies? Start here.
The offer: O’Reilly Media · Data Science by O’Reilly, 25 ebooks · EPUB or PDF · DRM-free.
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After the offer ends, each pick above links its own DRM-free edition.
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Keep the conversation going.
Which would help your next project most: cleaning the data, making the chart or getting the analysis to run again?
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