We all know what makes both authors and editors happy: a smooth, streamlined publication process where the path from draft to published article is quick and painless.

We also know that many of our contributors—old-timers and new ones alike—might not have a lot of experience working with WordPress, the software powering our independent publication, and specifically with its block editor. We put together this guide to offer you concrete pointers, and to address some of the common questions authors have asked us. You can read this post from top to bottom (especially if you have zero block-editor experience), or jump ahead to the sections that are relevant to your current needs.

• What is the block editor?
• How do I start a new draft?
• What are the essential elements that all TDS articles must have?
• The most common blocks you’ll use (and how to use them)
  • Paragraph
  • Heading
  • Image
  • Quote
  • List
  • Separator
  • Prismatic (for code blocks)
  • Shortcode (for LaTeX notations)
  • Table
  • Embeds
    • GitHub Gists
    • Media embeds
• How can I ask for help with my draft?
• My draft is good to go! What’s next?

What is the block editor?

The publishing tool at the core of our website is the block editor. You can find ample resources on its various components and features on the WordPress documentation site; if you’ve never used it before, a good place to start is this page, which explains the basic actions you can take when using the editor and offers a clear legend for the many buttons and symbols you see around (and within) your content.

Keep in mind that if you’ve already used the block editor in the past as a site admin or editor, the view you’ll see as a TDS author is going to be a bit different. We wanted to provide you with a focused drafting environment with minimal distractions, so only the tools and options you’ll need to format your articles will be visible.

How do I start a new draft?

Each time you land in your author dashboard on our Contributor Portal, you can start a new draft by clicking on Posts → Add New Post:

If you already have a few drafts on our site in various states of completion, clicking on this link will take you directly to a list of your articles; just choose the one you’d like to work on.

What are the essential elements that all TDS articles must have?

As you probably know by now, TDS is very flexible in terms of article formats, perspectives, and writing tone. But every article must have the following components in place before we can publish it.

Title

An engaging, descriptive, concise title is essential. Type it into the top text field above your article’s content, and you’re all set:

Bonus points: make sure your title is written in title case — for example, “My Title Is Irresistible and Succinct, but Not Clickbait-y,” rather than “My title is irresistible and succinct, but not clickbait-y.” (The latter is sentence case, which we reserve for your subtitle.)

Subtitle

Speaking of subtitles: you need one! Brevity is great — the idea is to add just a bit more context to your title and improve the odds that potential readers decide to click on your article.

In the block editor, the subtitle is called a subheading, and you add it directly into — you guessed it — the subheading field, which you’ll find in the post settings sidebar, just to the right of the article’s content:

If you’re feeling a little bit more adventurous, copy and paste your subtitle (sorry, subheading) into the Post Excerpt field, which is near the top of the very same post settings sidebar. (If you’re not feeling that adventurous, no worries — our editors will take care of that one for you.)

Featured image

Every article needs one! Whether you find yours online (copyright-free, of course), create it yourself, or generate one with an AI tool, it’s the most important visual element in your article, so it’s worth spending a few minutes on this choice. (Here are some ideas and guidance if you’re not sure how to go about it.)

Once you’re ready, upload your image to its designated Featured image slot, not into the body of your article. Just click on the Set featured image button in the post settings sidebar.

To bring true tears of joy to your editors’ eyes, once the image is uploaded, don’t forget to add a caption with the necessary sourcing details:

Tags and Categories

Feel free to add one category and up to five tags to your articles. While this is an essential element, it’s one we’re happy to take care of for you — so if you’re not sure which ones to choose, just leave these to us.

Note: we truly believe in the top-notch quality of all the articles we publish on TDS, but we ask that you don’t add our featured tags (like Deep Dives or Editors’ Pick) yourself. Our team considers every article, and we then add these tags on our end to a handful of articles each day.

The most common blocks you’ll use (and how to use them)

Paragraph

This is where the text of your article goes — which is why the Paragraph block is likely going to be the one you use most frequently. Place your cursor anywhere in the paragraph to make the options menu visible, and highlight any words to which you’d like to add specific styles like bold, italic, and inline code, among others.

Heading

Creating a clear structural hierarchy within your article helps both readers and search engines navigate your content. Add a new heading whenever you start a new section, and choose the right one depending on its position within your article.

H1 headings should be reserved for your article’s title, so don’t add any more of those in the body of your post. H2 headings are for new sections, and if your larger sections contain shorter subsections, you can use H3 or H4 headings as needed.

Image

Use the Image block to add most visual media to your article —charts, screenshots, animations, etc. The most important thing to remember is that you shouldn’t copy-and-paste images into the block editor — you need to upload every media file directly to our site’s Media Library.

Once uploaded, however, you can re-use the same image multiple times within the same article or across several articles.

Don’t forget to add an image caption to your images — that’s where you provide readers (and your TDS editors) with the necessary information about your image source and licensing status.

If you could use a quick refresher on our image guidelines, we’ve got you covered.

Quote

Quote blocks are a visual way for highlighting specific points or ideas, or for quoting longer passages from external sources.

Using them sparingly can be very effective, but don’t overdo it — just like bold or italicized text, Quote blocks can quickly become distracting.

List

Whether for your…

• table of contents,
• key takeaways,
• or any other element in your article that can benefit from a neat, bulleted arrangement…

…the List block should be your go-to. The block editor will automatically detect if you’re creating numbered or unordered lists once you type in the first item.

To add a nested list within your list, just start a new item and click your tab button.

Separator

Sometimes, a heading might feel insufficient for creating a visual break between or within sections.

In those cases, use the Separator block — just make sure you select the Dotted style, which is the one we chose as our default on TDS:

Prismatic (for code blocks)

Many (many!) of our articles feature code, and lots of it, so we wanted to make sure it looks neat, clear, and professional. To do that, we’ve enabled the Prismatic block, which should be your default choice for embedding longer code blocks into your article. (One main exception: if you decide to use GitHub Gists instead, which we also welcome.)

Feel free to type in your code or to copy-and-paste it from an IDE. The one crucial thing you need to remember, however, is to choose the block’s programming language — otherwise, your code will not display properly on the TDS frontend. Fortunately, selecting the right language is a breeze, and you can do it straight from your block settings sidebar:

With dozens of programming languages to choose from, you’re almost certain to find the one you need. On the off chance you don’t, select one that’s close enough, syntax-wise, to the exotic, up-and-coming language you’re coding in. (If you ever just want to present plaintext within a code block, your best bet is to go with HTML.)

Shortcode (for LaTeX notations)

If your article includes a lot of formulas (or formulae, if you insist), equations, and other math notations, you can ensure they look great on TDS by inserting your LaTeX markup into a Shortcode block. There are a couple of different approaches for doing that, and we strongly recommend reading the documentation by MathJax-LaTeX, which is the WordPress plugin powering LaTeX on our site.

If your needs aren’t as elaborate, you can also…

• Directly add a smattering of ≠, π, or ∑ (to name a few examples) using handy keyboard shortcuts.
• Create your fancy equations on the external tool of your choice, and take a screenshot — which you’ll then upload into an Image block.

Table

Great for comparing experiment results, product features, and more, the Table block makes creating sleek tables very easy — just decide how many columns and rows you need and you’re pretty much ready to go.

You can further customize your table using the block settings sidebar (to choose fixed-width cells, add a header section, etc.).

Embeds

We’ve enabled several frequently used embed types for authors, allowing you to seamlessly integrate content from other platforms into your articles.

GitHub Gists

Sharing your code via Gists is common among data and ML professionals, and this block makes it possible to extend this practice into your TDS articles. It requires two simple steps:

1. On GitHub, copy the embed link from your GitHub Gist:

2. Add a GitHub Gist Embed block to your article, and, in the block settings sidebar, paste the embed link. You should immediately see a preview of your Gist.

You can add as many Gists as you’d like, and their formatting will stay the same on our site.

Media embeds

If you’d like to embed tweets, YouTube videos, and other embeddable media from external platforms, click the + symbol in the block editor, type embed, and you’ll see the various options at your disposal.

Then, paste in the URL of the media you’d like to embed into the block, and let the editor do the rest. Note that for some platforms, you might need a special embed link.

If you’d like to embed other media but are not sure if it’s possible or how to go about it, just reach out — we’ll do our best to find a solution for you.

How can I ask for help with my draft?

We’re glad you asked! If you ever run into any issue during the review process, while drafting your article, or even after your post is published, you can always reach out directly from the block editor. Just highlight any word, click on the new comment symbol, and type in your question, note, or concern.

Two important things to remember:

• For the fastest response time, tag one of our team members when you enter your comment — typing the @ symbol will show you a list of the editors you can ping.

• Always save your draft after you post a comment. This ensures your comment doesn’t disappear into thin air if you make changes to your draft, and sends a notification to the editor you pinged.

My draft is good to go! What’s next?

Congrats! You’re almost done. Here are two things you should do before submitting your article for review — and one thing you absolutely have to do for us to actually review it.

• Giving your article a final proofread is never a bad idea (we can’t overstate the benefits of sending us a clean first draft).

• We also recommend you check out your draft preview — it’s fun to see how your article would look once it’s published, and it also offers you a chance to double-check everything is formatted and displayed as you’d intended. To preview your article, click on the laptop symbol (we think it’s a laptop symbol?) near the top of the editor screen.

• Finally, please don’t forget to submit your article for review — which you can do by clicking on the (drumroll, please) Submit for Review button at the top-right corner of the block editor.

Once you click on the button, your article enters our review queue; you can still make changes to it, but it’s best to keep these to a minimum once you receive a notification that it’s in review.

If you still have any questions or run into any issues while drafting and formatting your article, feel to reach out via email or by pinging us directly from your article.

The post How to Format Your TDS Draft: A Quick(ish) Guide appeared first on Towards Data Science.

Over the years, we’ve grown into a publication with hundreds of active authors, a team of editors that ensure the quality of the articles we publish, and millions of readers and followers across our main site, newsletters, and social channels.

We’re thrilled to be a leading hub for learning about data science, ML, AI, and adjacent topics, especially as developments in these fields affect an increasing number of people, communities, and societies around the world. We’re equally proud to be a welcoming and inclusive community, where authors and readers from all over the world, representing a wide range of opinions and experience levels, come together with a shared sense of mutual respect and a focus on professional and intellectual growth.

In 2024, Insight Media Group acquired the Towards Data Science publication. The following year we launched our own self-hosted website and contributor platform, enabling us to host and pay contributors. 

We’ve evolved in many ways since our early days, and we’re sure that we’ll continue to grow alongside our community. What will never change is our mission to share the best writing we can find on the topics we cover. Thank you for joining us on this journey.

For those of you who’d like to become more actively involved with our publication, we’re always happy to consider work from new authors who have insight and expertise to share around our core focus areas: data science, machine learning, AI, and related programming topics.

You’ll find all the details and guidelines about submitting your work on the Write for Towards Data Science page.

If you have any questions, feel free to reach out at contributor@towardsdatascience.com. 

If you’re interested in advertising opportunities, learn more here.

For general questions and inquiries, you can contact us at publication@towardsdatascience.com.

Our Editorial Team

Ludovic Benistant

Publisher. Linkedin. ludo@towardsdatascience.com 

Ben Huberman 

Director of Content Operations. Linkedin. ben@towardsdatascience.com 

Anne Bonner 

Editor. Linkedin. anne@towardsdatascience.com

The post About Towards Data Science appeared first on Towards Data Science.

Historically, there was no direct monetization involved in contributing to TDS (unless authors chose to join the partner program at our former hosting platform). As we establish TDS as an independent, self-sustaining publication, we’ve decided to change course, as it was important for us to reward the articles that help us reach our business goals in proportion to their impact.

How it works

The TDS Author Payment Program is structured around a 30-day window. Articles are eligible for payment based on the number of readers who engage with them in the first 30 days after publication.

Authors are paid based on three earning tiers:

• 25,000+ Views: The article will earn $0.1 per view within 30 days of publication: a minimum of $2,500, and up to $7,500, which is the cap for earnings per article.
• 10,000-24,999 Views: The article will earn $0.05 per view within 30 days of publication: a minimum of $500, and up to $1,249.
• 5,000-9,999 Views: The article will earn $0.025 per view within 30 days of publication: a minimum of $125, and up to $249.
• 500-4,999 Views: The article will earn $100.

A few important points to keep in mind: 

• Views are counted only if a reader stays on the page for at least 30 seconds, ensuring that the payouts reflect real engagement, not clicks.
• Articles with fewer than 500 views in 30 days will not qualify for payment.
• During these 30 days, articles must remain exclusive to Towards Data Science. After that, authors are free to republish or remove their articles.

Who can participate?

This program is available to every current TDS contributor, and to any new author who becomes eligible once an article reaches the first earning tier.

Participation in the program is subject to approval to ensure authentic traffic. We reserve the right to pause or decline participation if we detect unusual spikes or fraudulent activity. Additionally, payments are only available to authors who live in countries supported by Stripe.

Authors can submit up to four articles per month for paid participation.

Why we’re doing this

We built this program to create a transparent and sustainable system that pays contributors for the time and effort required to write great articles that attract a wide audience of data science and machine learning professionals. By tracking genuine engagement, we ensure that the best work gets recognized and rewarded while keeping the system simple and transparent.

We’re excited to offer this opportunity and look forward to supporting our contributors who keep Towards Data Science the leading destination in the data science community. 

How to contribute

To become a writer, please have a look at our guidelines. These will help you to prepare your article for publication.

Once ready, please send your article through the Contributor Portal 

We aim to respond to authors as quickly as possible and to let them know whether or not we’ve accepted their articles. We look forward to reading your work!

The post Announcing the Towards Data Science Author Payment Program appeared first on Towards Data Science.

• Submission Guidelines
• How to get your article ready for publication!
• Adding and using images
• Longform posts, columns, and online books
• How to submit your work
• FAQ

Why become a contributor?

We are looking for writers to propose up-to-date content focused on data science, machine learning, artificial intelligence and programming. If you love to write about these topics, read on!

Reach a broader audience with your articles. We are one of the most popular data science sites in the world. TDS started as a publication on Medium, amassing more than 700k followers and becoming the most-read publication on the site. Now on a self-hosted platform, TDS is the leading destination in the data science community. 

Here are a few things we do to ensure your articles reach the largest audience possible:

• Our independent domain (towardsdatascience.com) provides better visibility and direct traffic to your work.
• We feature our best stories on our homepage, newsletter, and social media (LinkedIn, X, and more), and provide our authors with sophisticated publishing tools to better tell their stories. 
• We provide editorial support to help refine and amplify high-quality submissions.

Earn money with the Towards Data Science Author Payment Program. When publishing in TDS, our authors can decide to apply to our Payment Program, which enables them to earn from their work. You can read more about our Author Payment Program here.

Submission Guidelines

Before submitting your article, there are a few essential things you need to know. Make sure you read each point well, and that you understand them, as by submitting an article to TDS, you are agreeing to comply with all of them.

Please take a few minutes to familiarize yourself with our Author Terms and Conditions of Use — they govern the relationship between contributors and TDS.

Any article you share with us must be entirely your own original work; you can’t take other writers’ words and present them as your own, and we also don’t allow AI-generated text, even when you’re the one who prompted its creation.

Guidelines

How to get your article ready for publication!

We aim to strike a balance between innovating, informing and philosophizing. We want to hear from you! If you are not a professional writer, consider the following points when preparing your article. We want to publish high quality, professional articles that people want to read.

1. Is your story a story that needs to be told?

Before you start writing, ask yourself: is this story a story that needs to be told?

If you have read many articles addressing the same issue or explaining the same concept, think twice before writing another one. If you have a radical, new take on an old chestnut, we want to hear from you… but, we need you to persuade us that your article is something special that distinguishes itself from the pack and speaks to our audience.

Conversely, if your article addresses an underserved area or presents a new idea or method, that’s just what we are after!

2. What is your message?

Let us know what your main message is, right from the start. Give your piece a snappy introduction that tells us:

• What is your novel idea?
• Why should we care?
• How are you going to prove your point?

Once you’ve got that out of the way, you can be as conversational as you like, but keep calling back to the central message and give us a solid conclusion.

Remember though, Towards Data Science is not your personal blog, keep it sharp and on-topic!

3. On the internet, nobody knows you are a dog

You’ve got a new idea or a new way of doing things, you want to tell the community and start a discussion. Fantastic, that’s what we want too, but we’re not going to take for granted that you know what you are talking about or that we should uncritically believe what you say… you’ve got to persuade us (your audience) that:

• The subject matter is important
• There is a gap that needs to be filled
• You have the answer
• Your solution works
• Your idea is based on a logical progression of ideas and evidence
• If you are giving us a tutorial, tell us why people would need to use this tool and why your way is better than the methods already published.

You can do this by explaining the background, showing examples, providing an experiment or just laying out how data you have extracted from various sources allowed you to synthesise this new idea.

Are there arguments that counter your opinion or your findings? Explain why that interpretation conflicts with your idea and why your idea comes out on top.

4. Do you have a short title with an insightful subtitle?

If you scroll up to the top of this page, you will see an example of a title and subtitle. Your post needs to have a short title and a longer subtitle that tell readers what your article is about or why they should read it. Your header is useful for attracting potential readers and making your intentions clear. To remain consistent and give readers the best experience possible, we do not allow titles or subtitles written in all-caps. We also ask that you avoid profanity in both your title and subtitle.

When your subtitle is directly under the title and formatted correctly, it will show up in some post previews, which helps with your click-through rate. 

5. What makes your post valuable to readers?

A successful post has a clearly defined and well-scoped goal, and follows through on its promise. If your title tells us you’re going to unpack a complex algorithm, show the benefits of a new library, or walk us through your own data pipeline, make sure the rest of the post delivers.

Here are a few pointers to help you plan and execute a well-crafted post:

• 1. Decide what your topic is — and what it isn’t
  If you’re not sure what your post is going to be about, there’s very little chance your audience will when they read it. Define the problem or question your article will tackle, and stick to it: anything that doesn’t address the core of your post should stay out.

• 2. Create a clear plan
  With your topic in hand, sketch out a clear structure for your post, and keep in mind the overall structure it’ll follow. Remember that your main goal is to keep your reader engaged and well-oriented, so it’s never too early to think about formatting and how you’ll break down the topic into digestible sections. Consider adding section headings along the way to make your structure visible.

• 3. Use clear, action-driven language
  If you’re still finding your personal voice as a data-science author, a good place to start is keeping things clean, clear, and easy to follow.

If your article is full of neutral, generic verbs (like to be, have, go, become, make, etc.), try to mix in more precise action verbs. When it makes sense, use specific, lively descriptors instead of dull ones (for example, you could replace “easy” with “frictionless,” “accessible,” or “straightforward,” depending on the context).

There are few things editors appreciate more than a clean first draft, so don’t forget to proofread your post a couple of times before sharing it with TDS: look for spelling, punctuation, and grammar issues, and do your best to fix them. What we hope to offer to our readers are clear explanations, a smooth overall flow — pay attention to those transitions! — and a strong sense of what you’re aiming to achieve with your post.

If you’d like to expand your toolkit beyond the basics, the Internet is full of great writing resources. Here are a few ideas to help you get started:

• Writing for an academic journal: 10 tips. The Guardian
• 6 of the Best Pieces of Advice from Successful Writers
• Steven Pinker’s Sense of Style

• 4. Include your own images, graphs, and gifs
  One of the most effective ways to get your key points across to your readers is to illustrate them with your compelling visuals.

For example, if you’re talking about a data pipeline you built, text can only take you so far; adding a diagram or flowchart could make things even clearer. If you’re covering an algorithm or another abstract concept, make it more concrete with graphs, drawings, or gifs to complement your verbal descriptions. (If you’re using images someone else created, you’ll need to source and cite them carefully — read our image guidelines below for more details.)

A strong visual component will hook your readers’ attention and guide them along as they read your post. It will also help you develop a personal style as an author, grow your following, and draw more attention on social media.

6. Are your code and equations well displayed?

TDS readers love to tinker with the ideas and workflows you share with them, which means that including a code implementation and relevant equation(s) in your post is often a great idea.

To make code snippets more accessible and usable, avoid screenshots. Use WordPress’s code blocks & inline code

To share math equations with your readers, Embed.fun is a great option. Alternatively, you can use Unicode characters and upload an image of the resulting equation.

When you include code or an equation within your article, be sure to explain it and add some context around it so readers of all levels can follow along.

To learn more about using these embeds and others in your post, check out this resource.

7. Check your facts

Whenever you provide a fact, if it’s not self-evident, let us know where you learned it. Tell us who your sources are and where your data originated. If we want to have a conversation we all need to be on the same page. Maybe something you say will spark a discussion, but if we want to be sure we are not at cross purposes, we need to go back to the original and read for ourselves in case we are missing a vital piece of the puzzle that makes everything you say make sense.

8. Is your conclusion to the point and not promotional?

Please make sure that you include a conclusion at the end of your article. It’s a great way to help your readers review and remember the essential points or ideas you’ve covered. You can also use your conclusion to link an original post or a few relevant articles.

Adding an extra link to your author profile or to a social media account is fine, but please avoid call-to-action (CTA) buttons.

For your references, please respect this format:

[X] N. Name, Title (Year), Source

For example, your first reference should look like this:

[1] A. Pesah, A. Wehenkel and G. Louppe, Recurrent Machines for Likelihood-Free Inference (2018), NeurIPS 2018 Workshop on Meta-Learning

9. Are your tags precise enough?

The more specific your tags, the easier it is for readers to find your article and for us to classify and recommend your post to the relevant audience.

We may change one or two tags before publication. We would do this only to keep our different sections relevant to our readers. For instance, we would want to avoid tagging a post on linear regression as “Artificial Intelligence”.

10. Do you have an amazing image?

A great image attracts and excites readers. That’s why all the best newspapers always display incredible pictures.

This is what you can do to add a fantastic featured image to your post:

• Use Unsplash. Most of the content on Unsplash is fine to use without asking for permission. You can learn more about their license here.

• Take one yourself. Your phone is almost certainly good enough to capture a cool image of your surroundings. You might even already have an image on your phone that would make a great addition to your article.
• Make a great graph. If your post involves data analysis, spend some time making at least one graph truly unique. You can try R, Python, D3.js or Plotly.

If you decide to purchase a license for an image to be used in your article, please note that we only allow the use of images under a license that: (i) does not expire; and (ii) that can be used for commercial purposes on the TDS Publication. You are responsible for ensuring you comply with the license terms of use. You must also include a caption below the image, as follows, or as otherwise required by the license provider: “Image via [license provider’s name] under license to [your name].” Finally, please email us a copy of a receipt or other evidence of the purchased license, along with the corresponding license terms of use.

If you’ve chosen to create images for your article using an AI tool (like DALL·E 2, DALL·E, Midjourney, or Stable Diffusion, among others), it’s your responsibility to ensure that you’ve read, understood, and followed the tool’s terms. Any image you use on TDS must be licensed for commercial use, including AI-generated images. Not all AI tools permit images to be used for commercial purposes and some require payment to permit you to use the image.

The images you generate with AI tools cannot violate the copyright of other creators. If the AI generated image resembles or is identical to an existing copyrighted image or fictional character (like Harry Potter, Fred Flinstone etc.), you are not permitted to use it on TDS. Use your best judgment and avoid AI-generated images that copy or closely emulate another work. If in doubt, use an image search tool — like Google Lens, TinEye, or others — to check whether your images are too similar to an existing work. We may also ask that you provide details of the text prompts you used in the AI tool to confirm you did not use the names of copyrighted works.

Your text prompts cannot use the names of real people, nor can your images be used if they feature a real person (whether a celebrity, politician, or anyone else).

Please remember to cite the source of your images even if you aren’t legally obligated to do so. If you created an image yourself, you can add (Image by author) in the caption. Whichever way you decide to go, your image source should look like this:

Your image should both have the source and the link to that source. If you created an image yourself, you can add “Image by author”.

If you’ve created an image that was lightly inspired by an existing image, please add the caption “Image by Author, inspired by source[include the link].” If you’ve edited an existing image, please make sure you have the right to use and edit that image and include the caption “Image by source[include the link], edited with permission by the author.”

Danger zone: Do not use images (including logos and gifs) you found online without explicit permission from the owner. Adding the source to an image doesn’t grant you the right to use it.

11. Where did you get your data?

The Towards Data Science team is committed to the creation of a respectful community of data science authors, researchers, and readers. For our authors, this means respecting the work of others, taking care to honor copyrights associated with images, published material, and data. Please always ensure that you have the right to collect, analyze, and present the data you’re using in your article.

There are plenty of great sources of data that are freely available. Try searching university databases, government open data sites, and international institutions, such as the UCI Irvine Machine Learning Repository, U.S. Government, and World Bank Open Data. And don’t forget about sites that hold specific data relating to fields like physics, astrophysics, earth science, sports, and politics like CERN, NASA, and FiveThirtyEight.

TDS is a commercial publication. Before submitting your article to us, please verify your dataset is licensed for commercial use, or obtain written permission to use it. Please note that not all the datasets on the websites we’ve listed are fine to use. No matter where you obtain your data, we advise you to double-check that the dataset permits commercial use.

If you aren’t confident you have the right to use it for commercial purposes, consider contacting the owner. Many authors receive a quick, positive response to a well-constructed email. Explain how you intend to use the data, share your article or idea, and provide a link to TDS. When you receive permission, please forward a copy to us at publication@towardsdatascience.com.

This is especially important if you plan to use web scraping to create your own dataset. If the website does not explicitly allow data scraping for commercial purposes, we strongly recommend that you contact the website owner for permission. Without explicit permission, we won’t be able to publish your work, so please forward us a copy via email.

And sometimes, simple works best! If you just want a dataset to explain how an algorithm works, you can always create an artificial or simulated dataset. Here’s a quick tutorial, and an article that uses a simulated dataset you might find helpful.

Please remember to add a link to the site where the dataset is stored, and credit the owner/creator in your article. Ideally, this is done on first mention of the dataset, or in a resource list at the end of the article. Please carefully follow any instructions relating to attribution that you find on the site. If you have created your own artificial or simulated dataset, it is important to mention that too.

We know interpreting a license can be challenging. It is your responsibility to be certain that you can present your data and findings in an article published with TDS, but if you’re stuck, please reach out to our editorial team for assistance. We would rather work with you in the early stages of your project than to have to decline your completed article due to a dataset license issue.

12. Is your content original?

While we do accept content that has already been published (for example, on your personal blog or website), our focus is on promoting and sharing new and original content with our readers. That means that by publishing your article in TDS first (or exclusively), you have a greater chance to be featured on our publication, our social media channels, and in our newsletter.

We love original content because it’s something that our audience hasn’t seen before. We want to give as much exposure to new material as possible and keep TDS fresh and up-to-date.

Originality also means that you (and your coauthors, if any) are the sole creator of each and every element in your post. Any time you rely on someone else’s words, you have to cite and quote them properly, otherwise we consider it an instance of plagiarism. This applies to human authors, of course, but also to AI-generated text. We generally don’t allow any language created by tools like ChatGPT on TDS; if your article discusses these tools and you wish to include examples of text you generated, please keep them to a minimum, cite their source and the prompt you used, and make it very clear (for example, by using block quotes) where the AI-generated portions begin and end.

13. Did you get any feedback before submitting your post?

Get into the habit of always asking a friend for feedback before publishing your article. Having worked so hard on that article, you wouldn’t want to let a silly mistake push readers away.

14. Has your Author profile been completed correctly?

Please include your real name, a photo, and a bio. We don’t publish posts from anonymous writers — it’s easier to build trust with readers when they associate your words with an actual person.

Use your profile to introduce yourself, your expertise, your and achievements — optimizing it will help you develop a meaningful relationship with your audience beyond a single post. 

If you are a company and would like to publish with us, please note that we almost exclusively publish articles submitted directly from the author.

15. Are you getting better?

Take a minute to reflect on the work you have been doing so far, and the current article you wish to publish. What value are you bringing, and to whom? In which ways are this article better or worse than the ones you previously published?

Longform posts, columns, and online books

Have a lot to say? Good. We love to dive deep into complex topics, and so do our readers. Here’s how you can publish longform posts, columns, and online books on TDS.

Longform posts

We love long reads! If your article’s reading time is shorter than 25 minutes, we recommend that you don’t break it into multiple pieces — keep it as-is. A single post makes it easier for readers to search and find all the information they need, and less likely that they’ll miss an important part of your argument.

To create a smoother reading experience, you can add a table of contents to orient your audience around your post. Adding high-quality images and lots of white space is always a good idea, too — a long text doesn’t have to be a wall of text.

We regularly add the most engaging and thoughtful longform posts to our Deep Dives page.

Columns

If your post’s reading time exceeds 25 minutes, or if you plan to focus on the same topic over multiple articles and a longer stretch of time, you can create your own TDS column. All it takes are three steps:

1. Add a custom tag to your post. This tag needs to be unique and reflect the theme of your project. Every time you publish a post with that tag, it will be added to your column’s landing page: towardsdatascience.com/tagged/[your-tag].
2. Add a kicker to your post. It’s like adding a subtitle but above your title.
3. Link your kicker to your column’s landing page.

You can create a TDS column and invite multiple authors to contribute. Just let your colleague(s) know which tag you decided to use so that they can add the same one to their articles. Here are some examples from our team.

Online Books

A column is a great format to use if you have an open-ended topic that you plan to write about for a while. If, on the other hand, your idea has a finite, defined scope and a clear sense of progression from one post to the next, you may want to create a series of articles that feels more like an online book. Here is the format we recommend using.

Keep the reading time of each article — or “chapter” — between 12 to 25 minutes, and aim for a series that has at least 5 articles (but probably not more than, say, 16). You can add links to previous or subsequent items from within each article — for example, in the introduction and/or conclusion.

To publish your online book, you can submit all your articles to our editorial team in one go, or one by one as you finish working on each. We’ll review them and publish them as they come along. Let us know your post is part of a planned online book project.

Please ensure that each article or online book chapter follows the same guidelines and rules as any other post that TDS publishes. If you ever decide to sell or exclusively license your book to a third party publisher, you will have to make sure you have their consent to continue to publish the book with TDS. If you do not have such consent, it is your responsibility to remove your content from the TDS publication.

How To Submit Your Work

To become a writer, please send your article using our form.

We aim to respond to authors as quickly as possible and to let them know whether or not we’ve accepted their articles.

On rare occasions, the volume of submissions we receive makes it difficult to respond to everyone; as a general rule, if you haven’t heard from us within a week of submitting your post, it’s safe to assume we won’t move forward with publishing it.

Contribute to Towards Data Science

If you’re having an issue with our online form, please let us know via email (publication@towardsdatascience.com) so we can help you complete the process. Please do not email us an article that you have already sent via our form.

FAQ

Our FAQ can be found here.

The post Write for Towards Data Science appeared first on Towards Data Science.

Since founding Towards Data Science in 2016, we’ve built the largest publication on Medium with a dedicated community of readers and contributors focused on data science, machine learning, and AI. Medium built a fantastic platform, and we wouldn’t have been able to reach our audience without its help.

As of Monday, February 3, 2025, Towards Data Science will become an independent publication, and we believe this transition will help us better serve our contributors and readers.

By moving off Medium we can maintain complete control over our editorial vision, protect the domain authority we’ve built, and guide our future growth on our own terms. 

Today, we’re launching our new WordPress site, though some parts of the migration are still a work in progress. We’re in close communication with our contributors to ensure a smooth transition. Despite the move, our mission remains unchanged: We’ll continue to provide the same in-depth coverage of data science, machine learning, and AI – only now on a platform where we can further support authors by utilizing exciting new promotional tools, advanced analytics, and dynamic community features that open up fresh opportunities for engagement and growth. 

Why Independence Makes Sense for TDS

We’ve long recognized that Medium provides valuable hosting, distribution and monetization tools – resources that have helped many writers and publishers (like us!) reach a wide audience. However,we believe our long term-term goals – as well as those of our readers and contributors – are best supported on an independent platform.

Here’s why we believe an independent platform is the right move:

Improved Accessibility for Readers – We believe in open access to all articles, not in paywalls. Moving to our own platform ensures that all of our content is available without charge to readers.

Ongoing Support for Authors – Our independent platform gives authors greater flexibility to promote their work. This means authors can amplify their voices and engage with new audiences.

Ownership of our Domain Authority – Operating independently allows us to preserve and grow Towards Data Science by not only retaining our search visibility, but also allowing us to experiment with novel content formats and create deeper connections with readers.

For current TDS authors, expect an email soon with details on how to join the new platform.

For new contributors, we’re opening up submissions and would love to feature your work on our new site. More details will follow in the coming days.

Content Rights Note

We want to be clear that we will only migrate content to our new site if we have the appropriate rights to do so in accordance with Towards Data Science’s terms and conditions. Your work remains under your control, and we’re committed to honoring any prior agreements regarding its use. Only content for which we have the necessary permissions will be transferred, ensuring that your intellectual property is respected throughout this transition.

If you have any questions or concerns about how your content will be handled during this migration, or wish to remove it from our new website, please don’t hesitate to reach out to our editorial team – you can email us here: publication@towardsdatascience.com. We’re here to provide any additional clarification you might need and to address any issues that arise.

How To Submit Your Work

To become a writer, please send your article using our form.

You can also find our guidelines and FAQ here.

FAQ: Transition for Authors & Contributors

We understand many TDS contributors have questions about how our move off Medium affects your work. Thank you for sticking with us through this change.

In the next few days, current and past authors will receive an email with instructions to set up a new account on our platform. This will let you update existing articles and submit new work for consideration.

Below are answers to some of the most pressing questions we’ve received so far, along with details about new features coming to Towards Data Science.

1. Why did Towards Data Science leave Medium?

Medium has been instrumental for our growth, but recent changes in Medium policies showed us that our priorities have diverged. By moving to our own platform, we gain full control over our editorial direction and policies. This independence allows us to provide free access to our articles, and better support you, our contributors, with a full set of publishing tools–including advanced analytics, enhanced social media capabilities, and more flexible content management–to help you reach a wider audience.

2. Why now?

At the end of January 2025, Medium began redirecting content from our custom domain, which immediately impacted our site traffic and search rankings. To protect our long-term vision and maintain the site’s hard-earned visibility, we fast-tracked a plan to launch an independent WordPress site.

3. When is the new platform launching?

Our new site launched on Monday, February 3, 2025. While we’re still refining some features on the backend, the site is fully operational with the paywall removed from all content. We’re working closely with contributors to ensure the transition is as smooth as possible. 

4. What do I need to do as a contributor during this transition?

For now, simply email your article to us in a PDF or Google Doc. We’ll handle the rest by importing your work into the new CMS and publishing it on the site. You don’t need to set up a new account right away–though current and past contributors will soon receive an email with instructions to create an account for managing and updating your content.

5. How will my existing content be handled?

We want to be clear: We only migrated content to our new site if we had the appropriate rights to do so in accordance with Towards Data Science’s terms and conditions. Your work remains under your control, and we’re committed to honoring any prior agreements regarding its use. Only content for which we have the necessary permissions will be transferred, ensuring that your intellectual property is respected throughout this transition.

If you have any questions or concerns about how your content will be handled during this migration, please don’t hesitate to reach out to our editorial team – you can email us here: publication@towardsdatascience.com.

We’re here to provide any additional clarification you might need and to address any issues that arise.

6. Can I continue to publish on Medium during this transition?

Yes, absolutely. Towards Data Science leaving Medium doesn’t mean you have to leave Medium. 

Please keep in mind that to publish on TDS, you’ll need to send us your article directly. We will no longer accept submissions through, or published on, Medium.

7. What are the benefits of publishing on the new TDS?

Moving to our own platform opens up several exciting opportunities for our contributors, including

• Unlimited Reach: Your articles will be completely free to read, free from Medium’s paywall. This means your work can reach a wider, more diverse audience.
• Advanced Promotional Tools: Our new site comes with enhanced features to empower authors to better promote their work. This flexibility will make it easier to use TDS to build your personal brand and connect with new readers. 
• Greater Editorial Control: We’re excited to launch new article formats that will allow authors greater control over how their content is presented to the reader.
• Better Visibility & Control: Content on TowardsDataScience.com will maintain its search rankings without being dependent on Medium’s policies.

8. Who do I contact if I have questions?

We’re here to help. If you have any additional questions or need further clarification, please email our editorial team at publication@towardsdatascience.com.

Your feedback is important as we finalize our migration to the new platform.

We hope these answers clarify our transition process. We’re thrilled about the future of Towards Data Science, and we’re committed to making this transition a smooth and positive experience for our authors and readers. Thank you for being a vital part of Towards Data Science as we take the publication to the next level!

– TDS Editorial Team

The post Towards Data Science is Launching as an Independent Publication appeared first on Towards Data Science.

The Variable is moving soon—sign up here to ensure you receive all future newsletters.

Our prolific authors delivered some excellent work this past month, channeling all the renewed energy and excitement we’ve come to expect from January on TDS. From career advice to core programming and data-processing tasks, our most-read and -shared articles in the past month cover the topics that data professionals care about the most as they plan their next move and aim to expand their skill set.

We invite you to explore this month’s must-reads with an open mind: from the ever-shifting terrain of job descriptions to the rise of small language models (alongside large ones), they tackle well-covered areas in Data Science and machine learning from a fresh, actionable, and pragmatic perspective. Let’s get started.

• How to Pick Between Data Science, Data Analytics, Data Engineering, ML Engineering, and SW Engineering"When the job titles sound so similar and the roles have a good amount of overlap" it can be difficult to choose the right path for your own interests and priorities as a data practitioner. Marina Wyss – Gratitude Driven‘s clear and detailed overview will help you make an informed decision.
• Your Company Needs Small Language ModelsIs it time to reassess the axiom that in AI, bigger is always better? Sergei Savvov makes a compelling case for the growing footprint of small language models in industry contexts, outlining the ways "they can reduce costs, improve accuracy, and maintain control of your data," and urges us to stay mindful of these models’ current limitations.
• The Large Language Model CourseFor anyone whose new year’s resolutions included expanding their knowledge of (and practical experience with) LLMs, Maxime Labonne‘s comprehensive course is the one-stop resource you’ll need to get started—it offers a well-structured curriculum that assumes no advanced knowledge, and comes full of recommended articles, tutorials, and tools.

• 5 Simple Projects to Start Today: A Learning Roadmap for Data EngineeringFor all the aspiring data engineers out there, Sarah Lea outlines a realistic, four-month plan that covers all the essentials. Most importantly, it guides you through several project ideas so that you not only grow your theoretical knowledge, but also get to practice the concepts and workflows you’re learning about.
• How to Build a Knowledge Graph in Minutes (And Make It Enterprise-Ready)Looking to apply a similar hands-on approach to knowledge graphs? After facing major setbacks in the past, Thuwarakesh Murallie shows how you can build one on your own by leveraging the power of LLMs.
• Top 12 Skills Data Scientists Need to Succeed in 2025"Many things are changing, but other things are not. Understanding which changes require your attention is the key to success." Benjamin Bodner breaks down the skills that remain essential for data professionals amid the disruptive pressure of AI tools.
• Deep Dive into Multithreading, Multiprocessing, and AsyncioWhether you’re taking your first steps in Python or are already a seasoned programmer, there’s always more to learn; Clara Chong‘s recent post zooms in on concurrency models—approaches to handle multiple tasks simultaneously—and unpacks the stakes of choosing the right one, depending on your project’s needs.
• How to Run Jupyter Notebooks and Generate HTML Reports with Python ScriptsFor another Python-focused tutorial that foregrounds the ability to streamline tedious workflows with code, don’t miss Amanda Iglesias Moreno‘s guide to automating Jupyter Notebook execution and report generation (with a helpful detour through synthetic-data creation).
• My Experience Switching From Power BI to Looker (as a Senior Data Analyst)It’s often difficult to assess the tradeoffs and gains you’ll be making by adopting a new tool without first trying different options—or hearing firsthand accounts from professionals in a similar situation to yours. Tomas Jancovic (It’s AI Thomas) walks us through the journey of moving to Looker for his data analytics workflows, and presents a balanced, concrete account of its pros and cons.
• Three Important Pandas Functions You Need to KnowNew models and generative-AI apps come and go, but Pandas is still here with us, playing an important role in data scientists’ day-to-day work. Jiayan Yin recently shared a helpful guide for early-stage learners, zooming in on three essential functions that you’ll likely turn to again and again as you process and analyze datasets.

Our latest cohort of new authors

Every month, we’re thrilled to see a fresh group of authors join TDS, each sharing their own unique voice, knowledge, and experience with our community. If you’re looking for new writers to explore and follow, just browse the work of our latest additions from the past couple of months, including Ramsha Ali, Derick Ruiz, Dr. Marcel Müller, Rodrigo M Carrillo Larco, MD, PhD, Ilona Hetsevich, Federico Zabeo, Vladyslav Fliahin, Jérôme DIAZ, Mandeep Kular, Glenn Kong, Vladimir Kukushkin, Viktor Malyi, Ruben Broekx, Iqbal Hamdi, Richa Gadgil, Piotr Gruszecki, Jonathan Fürst, Sirine Bhouri, Kyoosik Kim, Sunghyun Ahn, Afjal Chowdhury, Tim Wibiral, Kunal Santosh Sawant, Aman Agrawal, Abdelkader HASSINE, Florian Trautweiler, Mohammed AbuSadeh, Loic Merckel, Lukasz Gatarek, Zombor Varnagy-Toth, Marc Matterson, Manelle Nouar, Paula LC, Shitanshu Bhushan, Matthew Senick, Lewis James | Data Science, Clara Chong, Bilal Ahmed, Pavel Krautsou, Erol Çıtak, Cristovao Cordeiro, Vladimir Zhyvov, Yuval Gorchover, Zach Flynn, Allon Korem | CEO, Bell Statistics, Tony Albanese, Sandra E.G., Miguel Cardona Polo, James Thorn, Vineet Upadhya, Kaushik Rajan, Mahmoud Abdelaziz, PhD, Benjamin Assel, Shirley Li, Marina Wyss – Gratitude Driven, Michal Davidson, Rémy Garnier, Uladzimir Yancharuk, David Lindelöf, Ricardo Ribas, Hunjae Timothy Lee, Ashley Peacock, Rohit Ramaprasad, Alejandro Alvarez Pérez, David Martin, Ben Tengelsen, César Ortega Quintero, Jaemin Han, Max Surkiz, Massimo Capobianco, Tobias Cabanski, Jimin Kang, Felix Schmidt, Paolo Molignini, PhD, Sayali Kulkarni, Alan Nekhom, and Chris Lettieri, among others.

Thank you for supporting the work of our authors! We love publishing articles from new authors, so if you’ve recently written an interesting project walkthrough, tutorial, or theoretical reflection on any of our core topics, don’t hesitate to share it with us.

Until the next Variable,

TDS Team

The post Data Roles, Small Language Models, Knowledge Graphs, and More: Our January Must-Reads appeared first on Towards Data Science.

Whether you’re an independent practitioner or part of a larger team trying to make sense of this emerging technology, you’ll find concrete and actionable insights in the lineup of articles we’ve selected this week. They each tackle the nuts and bolts of building AI apps and leveraging their potential for well-defined goals, while avoiding common pain points.

While these posts zoom in on specific topics and business problems, they all offer a pragmatic, accessible approach, making them useful for readers across a wide spectrum of backgrounds and experience levels. Let’s dive in.

• What Did I Learn from Building LLM Applications in 2024? – Part 2So you’ve built a prototype of an LLM-based app that looks promising. What’s next? Satwiki De offers a comprehensive roadmap based on the takeaways she’s accumulated over a year of experimentation—the main thrust being to "develop your AI-enabled application keeping the business objectives in mind."
• Why Generative-AI Apps’ Quality Often Sucks and What to Do About itHaving observed countless enterprise AI initiatives deliver underwhelming results (if not fail altogether) Dr. Marcel Müller focuses on the importance of robust assessment, and shows "how we can qualitatively and quantitatively evaluate generative AI applications in the context of concrete business processes."
• Designing, Building & Deploying an AI Chat App from Scratch (Part 1)If you’re ready to roll up your sleeves (real or proverbial) and start implementing an AI chat app, Joris Baan‘s patient guide is a great resource to help you stay on the right track. Part 1 outlines the microservices architecture and local development needs you’ll want to think about, while part 2 moves on to a detailed discussion of cloud deployment and scaling.

• Learn to Build Advanced AI Image ApplicationsMoving beyond chatbots, Ida Silfverskiöld explores the possibilities that visual generative-AI tools open up for real-world businesses and professionals—in this case, an interior design app based entirely on open-source models and frameworks.
• How to Build an AI Agent for Data Analytics Without Writing SQLWhy not harness the power of AI to streamline common data-analytics workflows that would typically require numerous SQL queries? Chengzhi Zhao aims to accomplish just that with the aid of an AI agent, and shows how you can build one yourself with LangChain and DuckDB.
• AI-Powered Information Extraction and MatchmakingFor another promising use case where an AI app can help you and your team save time and become more efficient, don’t miss Umair Ali Khan‘s patient tutorial on building AI-based match-making tools (in this case, helping job seekers find positions that fit their skills and interests).

Branching out into the world beyond AI apps, we’ve selected a few more recommended reads we thought you’d enjoy—from a beginner-friendly intro to LLMs to an in-depth analysis of data strategies.

• If your less tech-savvy colleagues could use a clear and accessible primer on what LLMs are and how they work, just send them Carolina Bento‘s top-notch introduction.
• For a more advanced exploration of LLMs’ inner workings, head right over to Jaemin Han‘s fascinating look at models’ shortcomings in generating and interpreting ASCII art—and the surprising security risks the latter might suggest.
• Quick and focused, Clara Chong‘s guide to variable scoping explains why this particular aspect of your code can have far-reaching consequences for your Data Science workflows.
• What rules should we have in place to protect humans from unknowingly interacting with AI profiles on social platforms? James Barney unpacks the fallout from the recent controversy around Meta’s (now-suspended) experiment.
• The rise of code-generating chatbots leads Murtaza Ali to wonder if the age of human-written programming tutorials is coming to an end. (Spoiler alert / sigh of relief: not just yet!)
• If you’d like to make sense of the gap you observe between between predictions and real-world outcomes, Hennie de Harder‘s new post walks us through the basics of prediction probabilities, calibration, and how to interpret these numbers in a practical context.
• In the mood for a deep dive? Jens Linden, PhD just shared the latest installment in his Demystify Data Strategy series, focusing this time on the common pitfalls of data and AI strategies—and how organizations can avoid them.
• To round out this week’s lineup, we invite you to learn about MicroPython and its potential relevance for data scientists; Sarah Lea‘s concise guide will get you up to speed on all the essential details.

Thank you for supporting the work of our authors! As we mentioned above, we love publishing articles from new authors, so if you’ve recently written an interesting project walkthrough, tutorial, or theoretical reflection on any of our core topics, don’t hesitate to share it with us.

Until the next Variable,

TDS Team

The post Building Successful AI Apps: The Dos and Don’ts appeared first on Towards Data Science.

Feeling inspired to write your first TDS post? We’re always open to contributions from new authors.

Buzzwords and trends come and go, but the core task of telling compelling stories with data remains one of the main pillars in data scientists’ daily workflow. For practitioners who’d like to up their visualization game, this week we’re highlighting some of our best recent articles on creating powerful, effective, and sleek deliverables.

Our selection tackles the topic from multiple angles, so whether you’re interested in chart optimization, geospatial aids, or interactive dashboards, we’re sure you’ll find something here to inspire you and help you expand your current skill set. Happy tinkering!

• How to Make Extremely Beautiful Charts with PythonWhy settle for "meh…" when "wow!" is within reach? Ari Joury, PhD challenges us to stop settling for mediocre visualizations, shows how a few smart and focused tweaks can transform your average chart into something you can truly be proud of, and offers more than a few concrete examples (and code snippets) to make these suggestions both concrete and actionable.
• 5 Essential Tips to Build Business Dashboards Stakeholders Love"You might be wondering, isn’t dashboarding just creating a bunch of charts? Well, Yes and No." Yu Dong zooms in on an often-undervalued aspect of data professionals’ work, and explains why it’s time to reevaluate and streamline your approach to dashboard creation.
• Awesome Plotly with Code Series (Part 8): How to Balance Dominant Bar Chart CategoriesFor the past few months, Jose Parreño‘s excellent series has addressed some of the main pain points of designing functional, easy-to-digest charts. His latest installment takes a close, pragmatic look at a common scenario where one category threatens to throw an entire bar chart off-balance.

• Step-by-Step Guide for Building Bump Charts in PlotlyReady to move on from bar charts into more advanced and custom formats? Don’t miss Amanda Iglesias Moreno‘s Plotly-based tutorial, which introduces a complete workflow for creating a bump chart, a more specialized visualization that is "designed to explore changes in a ranking over time" and allows us to "quickly identify trends and detect elements at the top or bottom of the ranking."
• Easy Map Boundary Extraction with GeoPandasWorking with geospatial data can be very rewarding—not to mention essential in many industries—but it can also get tricky and occasionally unwieldy. Lee Vaughan‘s latest Python guide brings clarity and practicality to a very common use case: extracting, measuring, and plotting country borders.

A new year often brings with it a rush of excellent new writing, and so far 2025 has not disappointed on that front. Here are several recent standouts on a wide range of topics, from hands-on AI projects to the history of GPT models.

• What would it take for a supervised-learning model to solve a Rubik’s Cube? Daniel Warfield‘s latest deep dive walks us through a fun and enlightening project.
• As more and more AI-generated content floods contemporary media and online spaces, Stephanie Kirmer wonders how we should assess its cultural impact—and navigate its (many) pitfalls.
• For a solid understanding of cutting-edge LLMs, a trip down memory lane (all the way back to the prehistoric era of 2019–2020) might be a good idea—and Shirley Li‘s thorough and accessible retrospective of GPT-2 and GPT-3 offers us precisely that.
• If you’re in the mood for something more hands-on this week, we highly recommend Maeda Hanafi, PhD‘s new guide to customizing your fine-tuning code with Hugging Face’s Transformers library.
• Are we approaching the point where AI might be able to reliably determine if something is funny? Vineet Upadhya‘s latest article reviews recent research into this fascinating topic, including a new framework for computational humor detection.
• Establishing causality is a common goal for data practitioners, but that doesn’t make it any less difficult to pull off. For his debut TDS post, Rémy Garnier presents a versatile method that you can apply across a wide range of use cases.
• After sharing a well-received list of AI projects you can experiment with over a weekend, Shaw Talebi fleshes out the details of one of his proposed ideas: an AI-powered resume optimizer.

Thank you for supporting the work of our authors! As we mentioned above, we love publishing articles from new authors, so if you’ve recently written an interesting project walkthrough, tutorial, or theoretical reflection on any of our core topics, don’t hesitate to share it with us.

Until the next Variable,

TDS Team

The post Charts, Dashboards, Maps, and More: Data Visualization in the Spotlight appeared first on Towards Data Science.

Feeling inspired to write your first TDS post? We’re always open to contributions from new authors.

As we all settle into the sometimes hectic rhythm of a new year, we hope you’ve been enjoying the excitement of kicking off projects, learning about new topics, and exploring your next career moves. We’re definitely seeing a flurry of activity among our authors—both longstanding contributors and recent additions—and are thrilled to share all the great work they’ve been cooking up over the holidays.

Our lineup of top-notch reads this week has a distinctly actionable, hands-on flavor to it—after all, what better way to harness all this energy than by tinkering with some datasets, models, and code? Whether you’re interested in learning more about cutting-edge evaluation methods or building agentic-AI tools, we’ve got you covered with a diverse selection of tutorials and practical overviews. Ready to dive in?

• Paradigm Shifts of Eval in the Age of LLMs Is it time to reevaluate the way we approach evaluations? Lili Jiang believes it is: "I’ve come to recognize that LLMs requires some subtle, conceptually simple, yet important changes in the way we think about evaluation." Her latest article offers high-level insights into what a new paradigm might look like.

• The Next Frontier in LLM Accuracy Staying thematically close to LLM optimization, Mariya Mansurova‘s new deep dive unpacks in great detail several methods we can use to increase models’ accuracy, and zooms in on advanced fine-tuning techniques.

• How to Build a Graph RAG App Ready to roll up your sleeves and dig deep into some code? Steve Hedden‘s thorough tutorial on creating your first graph RAG app is a great option for anyone who’s interested in this trending topic but needs guidance and context to ensure they’re starting off on the right foot.

• Multi-Agentic RAG with Hugging Face Code Agents Agent-based systems gained enormous steam (and buzz) last year, and it doesn’t seem like that’s about to change in 2025. Curious to learn more about them? Gabriele Sgroi, PhD‘s patient, step-by-step guide may be long, but it remains accessible and clear as it outlines the process of leveraging a "small" LLM to power a multi-agentic system—and produce good results, even on consumer-grade hardware.

• Demand Forecasting with Darts: A TutorialLLMs may be grabbing much of our collective attention these days, but business-focused workflows remain the bread and butter of industry data scientists. Sandra E.G.‘s debut TDS article provides a robust, hands-on introduction to one such essential task: demand forecasting in the context of retail sales.

• Distributed Parallel Computing Made Easy with Ray It’s crucial for data and ML practitioners to experiment with new tools and frameworks, as seemingly small improvements can accumulate into major cost and efficiency benefits. Betty LD walks us through her recent foray into the AI-focused Ray library for distributed data processing, and demonstrates its power through the use case of scalable offline batch inference.

If you’re ready to branch out into other topics this week, we’re here to help—whether your interests lie at the intersection of music and AI, quantum computing, or linear algebra (among others), we hope you explore some of these excellent articles:

• Presenting her team’s cutting-edge research, Tula Masterman explains how we can leverage the hidden state from an intermediate Transformer layer for efficient and robust content safety and prompt injection classification.
• Music-centered AI tools continue to make strides; Max Hilsdorf devotes his latest exploration of the topic to mono-to-stereo upmixing, a technique for enriching or improving our music-listening experience.
• With seven first-author publications under her belt, Malak Sadek is well-positioned to offer concrete insights for other researchers who’d like to grow their publishing footprint.
• Continuing his ongoing series on key linear algebra concepts, Rohit Pandey recently shared a new, comprehensive explainer that looks at the inner workings of orthonormal matrices.
• Quantum computing has been a hot topic for some time now, though discussions of its promise can sometimes feel almost sci-fi-adjacent. Here to help us make sense of the field and where things stand at the moment is Sara A. Metwalli, whose primer comes right as the UN has declared 2025 the international year of quantum science and technology.
• While we’re on the topic, let’s round out this week’s selection with Benjamin Assel‘s nuanced discussion of error-rate measurement in IBM quantum processors, which also includes code examples, using Qiskit.

Thank you for supporting the work of our authors! As we mentioned above, we love publishing articles from new authors, so if you’ve recently written an interesting project walkthrough, tutorial, or theoretical reflection on any of our core topics, don’t hesitate to share it with us.

Until the next Variable,

TDS Team

The post LLM Evaluation, Parallel Computing, Demand Forecasting, and Other Hands-On Data Science Approaches appeared first on Towards Data Science.

Feeling inspired to write your first TDS post? We’re always open to contributions from new authors.

Happy new year! Welcome back to the Variable!

The ink has barely dried on our 2024 highlights roundup (it’s never too late to browse it, of course), and here we are, ready to dive headfirst into a fresh year of learning, growth, and exploration.

We have a cherished tradition of devoting the first edition of the year to our most inspiring—and accessible—resources for early-stage Data Science and machine learning professionals (we really do!). We continue it this year with a selection of top-notch recent articles geared at beginner-level learners and job seekers. For the rest of our readers, we’re thrilled to kick things off with a trio of excellent posts from industry veterans who reflect on the current state of data science and AI, and share their opinionated, bold predictions for what the year ahead might look like. Let’s get started!

2025: Ready, Set, Go!

• Top 10 Data & AI Trends for 2025As the co-founder and CEO of Monte Carlo, few people are as well-positioned as Barr Moses to observe industry shifts and emerging trends—so her annual forecast should definitely be on your to-read list.
• 2024 in Review: What I Got Right, Where I Was Wrong, and Bolder Predictions for 2025After a year dominated by RAG, AI agents, and knowledge graphs, what tools and workflows should we keep track of in the coming year? Leonie Monigatti shares a thorough recap of 2024—and a sneak peek at her well-tuned crystal ball for the year to come.
• Ten Predictions for Data Science and AI in 2025From Jason Tamara Widjaja, here’s another set of insightful reflections: "My predictions for 2025 attempt to provide a view of the tensions of AI, taking an unpopular but balanced view as someone whose work depends not on selling AI, but on implementing AI well – and living through the consequences of our decisions."

Data science and machine learning, step by step by step

• The Essential Guide to R and Python Libraries for Data VisualizationWith or without AI, charts and plots aren’t going anywhere anytime soon. Sarah Lea maps out the key libraries in which current and aspiring data scientists should gain fluency.
• Roadmap to Becoming a Data Scientist, Part 2: Software EngineeringProgramming isn’t going anywhere in 2025, either. Vyacheslav Efimov‘s guide outlines the coding essentials that will lead you to data science success.
• Missing Data in Time-Series: Machine Learning TechniquesOne constant trait of real-world data: it’s messy! Learn how to navigate the chaos by following along Sara Nóbrega‘s primer on handling missing data.
• Causality – Mental Hygiene for Data ScienceTaking a few steps back from the more nitty-gritty aspects of data science work, Eyal Kazin‘s recent deep dive constitutes a "gentle intro" to the intricate art of detecting, interpreting, and applying causality.
• Master Machine Learning: 4 Classification Models Made SimpleFor anyone who enjoys structure and clarity above all else, Leo Anello‘s (extremely) thorough, 15-step tutorial on classification models would be a perfect starting point from which to expand your ML know-how.
• 2024 Survival Guide for Machine Learning Engineer InterviewsWhether you’re already applying for your first MLE job or contemplating it as one of your goals for the year, don’t miss Mengliu Zhao‘s "survival guide," aimed specifically at junior-level practitioners.
• Machine Learning Basics I Look for in Data Scientist InterviewsTackling the occasionally opaque hiring process from the other end of the table, Farzad Nobar created a helpful resource to help job applicants zoom in on the topics that really matter to employers.
• 100 Years of (eXplainable) AIBeyond the whats and hows of day-to-day work, there are also the whys: why did this model produce these outputs? Sofya Lipnitskaya‘s explainer unpacks the history of AI explainability in the context of the recent rise of LLMs.
• How to Build a General-Purpose LLM AgentTo end on a more hands-on note—and to satisfy the curiosity of all of you who’ve heard the buzz around AI agents—we highly recommend Maya Murad‘s step-by-step guide, which can form "the groundwork for designing your own custom agentic architecture" down the line.

Thank you for supporting the work of our authors! As we mentioned above, we love publishing articles from new authors; if contributing to TDS in 2025 is one of your new year’s resolutions—or even if you’ve just recently written an interesting project walkthrough, tutorial, or theoretical reflection on any of our core topics—don’t hesitate to share it with us.

Until the next Variable,

TDS Team

The post Start a New Year of Learning on the Right Foot appeared first on Towards Data Science.