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Read the original DataScienceCentral roundup by Vincent Granville for the page as it appeared in 2016.
What the roundup covers
The selections span programming, visualization, machine learning, data quality, Hadoop, retail and big-data concepts. Granville describes most items as tutorials aimed at beginners, while some are cheat sheets or condensed references for experienced practitioners. Several entries use a periodic-table format, organizing concepts for quick lookup rather than presenting a linear lesson.
Why the title and link count differ
The source headline promises 13 infographics, but its displayed groups add up differently:
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| Source section | Visible links | Best fit |
|---|---|---|
| For Geeks | 6 | Technical learners and data practitioners |
| For Business People | 7 | Readers seeking business and organizational context |
| Infographics Repositories | 3 | People looking for larger collections |
| Total visible links | 16 | Not 13 |
This is a discrepancy in the 2016 page, not evidence that three items should be removed. The safest description is a 16-link roundup published under a “13” title.
Technical infographics (“For Geeks”)
These six entries focus on tools, methods and technical vocabulary. They are useful as orientation material or quick references, but the 2016 publication date matters for software advice.
Data Science Wars: R versus Python
A side-by-side introduction to two major data-science languages. Beginners can use it to identify differences in ecosystems and typical workflows; experienced readers should treat any version-specific comparison as historical.
Rank #2
- Wiley
- Language: english
- Book - storytelling with data: a data visualization guide for business professionals
Three periodic tables for data scientists
Periodic-table layouts turn a broad field into a visual index. They are suited to browsing concepts and terminology, not to learning an end-to-end project workflow.
Cheat Sheet: Data Visualization with R
A compact reference for people working with R visualization. It is most useful after a learner knows the basic syntax and needs a visual reminder of chart-related options.
Cheat sheet: data visualization in Python
A parallel quick-reference format for Python visualization. Because libraries and APIs change, verify commands against current documentation before using them in production.
Comparing Data Science and Analytics
This comparison helps clarify overlapping terms and roles. It is a conceptual primer rather than a measurement of modern job titles or organizational practice.
Great Machine Learning Infographics
A visual introduction to machine-learning ideas for readers who need a high-level mental model before tackling algorithms, code or mathematics.
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Business-oriented infographics (“For Business People”)
These seven links shift from programming details to information management, infrastructure and industry use cases.
Rank #4
Infographics on data quality
Useful for discussing why reliable decisions depend on accurate, complete and consistent data. Treat the graphics as communication aids, then define quality rules for the specific organization or dataset.
Unstructured Data: InfoGraphics
An introduction to data that does not fit neatly into conventional tabular fields. It can help nontechnical stakeholders understand why storage, search and analysis requirements differ from structured data.
The Data Science Ecosystem in One Tidy Infographic
A broad map of the components surrounding data science. Use it to identify unfamiliar categories, not as a definitive or current inventory of every tool.
Big data and the retail industry: infographics
Connects big-data ideas with retail scenarios. Industry examples can make abstract concepts accessible, but readers should not assume that a 2016 retail practice reflects current systems or regulations.
Infographics: The Half Life of Data
Frames data value as something that can change over time. The concept is useful for prioritizing freshness and retention, while any specific half-life claim in the graphic should be checked in its original context.
What is Hadoop? Great Infographics Explains How it Works
A visual explanation of Hadoop and its architecture. Hadoop’s role in modern platforms has evolved since 2016, so use this as historical orientation rather than current platform guidance.
What is big data – Infographics by Bernard Marr
A plain-language overview of big-data terminology for business readers. It is a starting point for discussion, not a current standard or formal definition.
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The page also points to three collections rather than single graphics:
- 24 Data Science, R, Python, Excel, and Machine Learning Cheat Sheets
- 72 Infographics about big data
- A pletora of big data infographics
Repositories are useful when you want several visual references, but they require more filtering. Check each item’s publisher, date, software version and accessibility before relying on it.
Quick Recap
How to use the list today
- Choose your audience. Start with the “For Geeks” group for technical vocabulary and tool-oriented references; choose “For Business People” for data concepts, infrastructure and industry context.
- Choose the format you need. Tutorials support a first pass, cheat sheets support recall, periodic tables support browsing, and repositories support discovery.
- Check the date. The roundup is dated May 28, 2016. Confirm current library syntax, platform status and industry practices elsewhere before implementation.
- Trace important claims. The roundup verifies that these links appeared on its page; it does not establish the present accuracy, accessibility or currency of each outbound resource.
- Turn visuals into action. After reading a graphic, write down one concept to study, one term to define and one small exercise to complete. An infographic is a supplement, not a substitute for documentation, code practice or domain review.
Who should start where?
| Reader goal | Recommended starting group | Reason |
|---|---|---|
| Learn the vocabulary of data science | Technical infographics or the ecosystem map | They provide broad conceptual orientation. |
| Compare R and Python | “Data Science Wars: R versus Python” | It addresses the language choice directly. |
| Refresh visualization syntax | R or Python visualization cheat sheet | Cheat sheets are designed for quick lookup. |
| Explain data projects to stakeholders | Business-oriented group | These entries emphasize quality, infrastructure and industry context. |
| Browse many examples | Repository section | Collections offer breadth, but require individual vetting. |
What this roundup does—and does not—establish
- It establishes which resources DataScienceCentral listed on its page in 2016.
- It does not provide a current quality score, ranking methodology or test results.
- It does not verify that every outbound graphic remains online.
- It does not make software-version, platform-lifecycle or industry claims current through 2026.
- No named statistic or authoritative quotation from the linked graphics is supplied on the roundup page itself, so none should be treated as independently verified here.
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
