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Make a deal with Towards Data Science
🔒 Paid proposals held safely in escrow — released only when the work's approved.
Followers
251K
Posts
64K
Following
1.9K
Account age
9 yrs
💰 Suggested sponsorship rate
$1.4K – $2.3K / sponsored post
Estimated from audience size & typical reach — every deal is negotiable and held in SocialDB escrow.
🎁 Free analysisfor Towards Data Science
📊 Post performance
12K
Avg views / post
4.8%
Reach vs followers
Oct 2016
On X since
🔥 Top post: "Logistic regression is perhaps the most popular and well-known · 72.2K views
📊 Activity & format
Posting cadence
0.09 / week
A lower-frequency account — each post lands with more weight.
Content mix
Mostly text
Recent: 0 video · 0 image · 18 text.
Follower / following
131×
Follows 1.9K back. A strong ratio — an audience that follows them, not a follow-for-follow network.
Verification
✓ X Premium
A paid X Premium (Blue) verified account.
Recent posts
View on X ↗
"If all machine learning engineers want one thing, it’s faster model training — maybe after good test metrics" by @alexdremov_me
Interested in the intersection of physics and machine learning? Don't miss Shuyang Xiang's primer on building physics-informed neural networks to formulate shock waves.
Building scalable Kubeflow ML pipelines on Vertex AI and ‘jailbreaking’ Google prebuilt containers by Kabeer Akande
Lessons From My ML Journey: Data Splitting and Data Leakage - Common mistakes to avoid when you transition from statistical modelling to Machine Learning by Khin Yadanar Lin
If you're a machine learning engineer in need of an accessible introduction to log loss — including the math and theory behind it — don't miss @jrobvision's thorough explainer.
"While a managed training service might be the ideal solution for many ML developers [..], there are some occasions that warrant running directly on 'unmanaged' machine instances [...]."
Chaim Rand expands in his latest post on ML enginee…
Courage to Learn ML: An In-Depth Guide to the Most Common Loss Functions - MSE, Log Loss, Cross Entropy, RMSE, and the Foundational Principles of Popular Loss Functions by Amy Ma
Dealing with MRI and Deep Learning with Python - In this post, Carla Pitarch Abaigar delves into how to align our data with the model’s requirements and how to prepare the model to process our data effectively:
In some instances, machine learning shouldn't, in fact, be your go-to solution. Toon Beerten explains the tradeoffs of various approaches using a signature-detection case study.
"With the announcement of MLX, it seems that Apple wants to make a significant leap into open source deep learning."
Tristan Bilot provides a helpful benchmark to assess the performance of Apple's new machine learning framework.
How to design an MLOps architecture in AWS? A guide for developers and architects especially those who are not specialized in machine learning to design an MLOps architecture for their organization by @harry1230
Courage to Learn ML: Decoding Likelihood, MLE, and MAP by Amy Ma
💡 Facts
🗓️Joined X in 2016 — 9 years ago.
👁️Averages 12K views per post.
📤Posts about 0.1× per week.
💬0.14% engagement rate.
🏅Earned the 100K Followers for passing 100K followers.
🕵️ Fake follower check
Estimated46/100
Fair
Credibility score
69%
Mixed
Real audience
Medium
Fake-follower risk
High
Data confidence
- Est. 69% real, active audience · Medium fake-follower risk.
- Low engagement (~4.8% of followers engage each post) — a sign of an inflated or inactive audience.
- Organic base — far more followers than accounts it follows.
- Verified account.
- Established account (9+ years old).
- 0.14% engagement — below the ~14.4% typical for this size.
Heuristic estimate from engagement, follower ratios, account age & growth — a screening signal, not a guarantee.
About
The world's leading publication for data science and artificial intelligence professionals.
Submit an Article ✍️ https://contributor.insightmediagroup.io
🔀 Audience overlap
EstimatedEstimated shared audience with similar creators — useful for avoiding overlap (or doubling down) when planning a campaign.
YouTube TV
16%
New Scientist
13%
E! Entertainment
13%
Popular Science
11%
Slack
11%
WFAA-TV Weather
10%
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Towards Data Science has 250,976 followers on X.
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