102 All-Time Launches
3 2026 Launches
0.22 Avg Engagement
-80% YoY Change

We've been tracking Data Science since 2021. 102 products indexed. The trajectory tells you where builders are investing and where the market sees opportunity.

Below: launch volume by year, engagement patterns by quarter, and the products that defined each period.

Launches Per Year

1 2021
15 2022
48 2023
20 2024
15 2025
3 2026

Quarterly Breakdown

QuarterLaunchesAvg Interest ScoreTop Product
Q1 2026 3 135 Ocean Orchestrator
Q1 2025 7 93 Streamoku
Q2 2025 2 136 Picsellia Atlas
Q3 2025 3 118 Minicule
Q4 2025 3 76 Baselight AI
Q1 2024 5 179 DataMotto
Q2 2024 5 330 Forloop.ai
Q3 2024 5 163 Future AGI
Q4 2024 5 137 marimo
Q1 2023 9 118 Hyperquery
Q2 2023 13 127 Prelaunch.com
Q3 2023 16 119 AI & Analytics Engine
Q4 2023 10 113 DVC Extension for VS Code
Q1 2022 2 91 TweetFeast
Q2 2022 5 101 NannyML
Q3 2022 1 182 Obviously AI Data Validator

Market Direction

The Data Science category has been cooling over the past 6 years of tracked data. Total launches went from 1 in 2021 to 3 in 2026.

Average engagement ratio across all Data Science launches: 0.22. Products above that line tend to solve a specific, painful problem. Products below it often entered a crowded space without clear differentiation.

Peak Activity

Data Science peaked in 2023 with 48 launches. That was 3 years ago. The decline since then could signal market consolidation, saturation, or attention shifting to adjacent categories.

Engagement Quality

Average engagement per product has risen from 0.05 in 2021 to 0.07 in 2026. That upward trend means the community is spending more time with each new launch. Either the products are getting better, or the audience is getting more selective. Probably both.

Strongest Quarter

The highest-performing quarter was Q2 2024, with an average interest score of 330 across 5 launches. Forloop.ai led that quarter.

B2B vs B2C Split

102 B2B launches (100%) vs 0 B2C (0%) across the full Data Science dataset. Data Science is heavily B2B. The products here target teams, companies, and professional workflows.

Year by Year

2021: 1 launches. Average interest: 93. Average engagement: 0.05. Top launch: Flookup 2.0 (93 interest).

2022: 15 launches (+1400% vs 2021). Average interest: 97. Average engagement: 0.28. Top launch: Obviously AI Data Validator (182 interest).

2023: 48 launches (+220% vs 2022). Average interest: 120. Average engagement: 0.24. Top launch: AI & Analytics Engine (285 interest).

2024: 20 launches (-58% vs 2023). Average interest: 202. Average engagement: 0.24. Top launch: Future AGI (548 interest).

2025: 15 launches (-25% vs 2024). Average interest: 100. Average engagement: 0.14. Top launch: Streamoku (242 interest).

2026: 3 launches (-80% vs 2025). Average interest: 135. Average engagement: 0.07. Top launch: Ocean Orchestrator (145 interest).

Top Data Science Products by Year

2026

Run AI jobs from your IDE with a one-click workflow
145
Mar 2026 12 discussions
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Teach your repo how to run itself
132
Mar 2026 9 discussions
View on Product Hunt
Extract structured data from text, files and archives.
129
Mar 2026 9 discussions
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2025

Streamlit hosting made simple
242
Mar 2025 15 discussions
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Your vision AI agent to build VisionAI applications
161
Apr 2025 18 discussions
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Self-host AI/ML with the world's cheapest GPU cloud
128
Feb 2025 10 discussions
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Use AI to visualize biomedical knowledge
126
Jul 2025 9 discussions
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A "virtual satellite" to map Earth in unprecedented detail
124
Aug 2025 6 discussions
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2024

Automate error detection and ensure high accuracy of your AI
548
Sep 2024 187 discussions
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No-code platform for web scraping & data automation
533
May 2024 60 discussions
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Open-source, insanely prosodic text-to-speech model
489
Jun 2024 114 discussions
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Bring everyone together with data
311
May 2024 25 discussions
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Make your data ready and clean with AI
304
Mar 2024 58 discussions
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2023

Build AI models and get predictions, no code required
285
Aug 2023 55 discussions
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Product validation platform
263
May 2023 91 discussions
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Data notebook built for speed, visibility, and collaboration
251
Feb 2023 420 discussions
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Build LLMs powered by GPT & your own data
236
Jun 2023 56 discussions
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Open source data labelling platform for AI model tuning
185
Jun 2023 28 discussions
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Frequently Asked Questions

Volume without engagement is saturation. Engagement without volume is opportunity. Check which one you're looking at.

Sum of all interest scores in the quarter divided by number of products. Simple average. We don't weight by category or product age.

Depends on what's declining. If volume drops but engagement rises, the market is maturing. That's often good for existing players. If both drop, the category may be dying. The quarterly breakdown on each page tells you which pattern you're seeing.

At least three. Two data points is a line, not a trend. We have five years of data for most categories, which is enough to distinguish real shifts from noise.

Current year launches compared to the same period last year. Positive means more products launching. Negative means the category cooled. Neither is inherently good or bad. A mature category with fewer but better launches is often healthier than one flooding the market with clones.

Launch volume drops but engagement per product rises. Fewer builders entering, but the ones that do find a more receptive audience. That's an opportunity signal. We flag it when we see it.

We report what happened. We don't predict. Five years of data shows patterns, but markets surprise people for a living.

Data Science market moves, weekly

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