Insights from recent episode analysis
Audience Interest
Podcast Focus
Publishing Consistency
Platform Reach
Insights are generated by CastFox AI using publicly available data, episode content, and proprietary models.
Total monthly reach
Estimated from 6 chart positions in 6 markets.
By chart position
- 🇺🇸US · Mathematics#1435K to 30K
- 🇫🇷FR · Mathematics#10100K to 300K
- 🇸🇪SE · Mathematics#2330K to 100K
- 🇷🇴RO · Mathematics#630K to 100K
- 🇰🇪KE · Mathematics#630K to 100K
- Per-Episode Audience
Est. listeners per new episode within ~30 days
144K to 462K🎙 Weekly cadence·18 episodes·Long inactive - Monthly Reach
Unique listeners across all episodes (30 days)
205K to 660K🇫🇷45%🇸🇪15%🇷🇴15%+3 more - Active Followers
Loyal subscribers who consistently listen
62K to 198K
Market Insights
Platform Distribution
Reach across major podcast platforms, updated hourly
Total Followers
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Total Plays
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Total Reviews
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* Data sourced directly from platform APIs and aggregated hourly across all major podcast directories.
On the show
Recent episodes
Loss Function
Dec 13, 2021
Central Limit Theorem
Dec 4, 2021
Causality and Control
Dec 3, 2021
Neural Networks
Dec 1, 2021
Types of Data Attributes
Nov 29, 2021
Social Links & Contact
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| Date | Episode | Description | Length | ||||||
|---|---|---|---|---|---|---|---|---|---|
| 12/13/21 | Loss Function | The intuition behind loss function | — | ||||||
| 12/4/21 | Central Limit Theorem | A quick introduction to central limit theorem and why it helps data analysis | — | ||||||
| 12/3/21 | Causality and Control | Thoughts on causality and the need for a control sample | — | ||||||
| 12/1/21 | Neural Networks | Can we think of neural networks as layers of decisions with regression and classification at each layer? | — | ||||||
| 11/29/21 | Types of Data Attributes | What are the different types of data attributes? | — | ||||||
| 11/23/21 | Intercept | Independence of the dependent variable | — | ||||||
| 11/23/21 | Bias and Variance | Generalizing the estimations of population parameters | — | ||||||
| 11/19/21 | Linear Regression | Guessing the recipe of data! | — | ||||||
| 11/19/21 | Decision Trees and Entropy | How are decision trees trained and what is entropy? | — | ||||||
| 11/17/21 | Validation | What is the intuition behind cross-validation for estimating population parameters? | — | ||||||
Want analysis for the episodes below?Free for Pro Submit a request, we'll have your selected episodes analyzed within an hour. Free, at no cost to you, for Pro users. | |||||||||
| 11/16/21 | Ground Truths in Data Science | What is a population and what is a sample? What exactly do we want to do with them? | — | ||||||
| 11/16/21 | Thoughts on Machine Learning | What is Machine Learning? What are supervised and unsupervised machine learning methods? | — | ||||||
| 11/12/21 | Cosine Similarity | What is cosine similarity in multidimensional data? | — | ||||||
| 11/11/21 | Principal Component Analysis | What is PCA and what does it do? | — | ||||||
| 11/9/21 | Latent Features | Intuition behind latent features in singular value decomposition | — | ||||||
| 11/8/21 | Recommendation Systems Using Content | Building recommendation systems using content - features of users and items | — | ||||||
| 11/4/21 | Recommendation Systems Using Observed Data | Building recommendation systems using observed interaction data | — | ||||||
| 11/4/21 | Recommendation Systems | Why are recommendation systems important and how they are built? | — | ||||||
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Chart Positions
6 placements across 6 markets.
Chart Positions
6 placements across 6 markets.