I love your channel’s intersection of higher level math with stats and data science! Feels like no one does it quite like you

dude I'm so grateful that this channel exists !!

This is probably the best use of 8.5 minutes I'll see all day. Love the insights, concise and organized delivery, and relatable examples.

Teaching the intuition behind Data science and math in general, I find to be much more important than people might think

Love this channel too! I love discussions about intuitions… it’s so easy to get lost in statistical jargon and it’s refreshing to step back and put things into perspective.

Amazing content that links stats and real world data. Greatly appreciate your work and clear examples!

Thank you! I realy like how you can explain everything simple way

Excellent work. The casual discussion is great to explain the concepts for newbies in data science or even the old dogs who want to learn new tricks. The most knowledgeable presenters are the ones who can explain something to a 5 year old. I'm also glad you have some content that formalizes these concepts as well. Always very helpful and though provoking.

Awesome stuff, very useful! Thanks ❤

Another fantastic video. Nice job.

That video is really very useful! Please keep on telling about intuition behind the data distributions! That’s really hard to find such explainations in regular books or any other formal sources of data

This is the most informative video on the intuition behind distribution interpretation I ever watched! For the "pointy" distribution, I've just thought of them as Gaussians with low variances.

Brilliant description of distributions

I absolutely love this channel

This was super helpful! Thanks for sharing!

Thank u for your work brother!🙏

Love that last one. I use QQ plots more, makes more sense to me, but I've def seen these. Thanks for providing well explained content on a higher level than many do.

One of the best videos on Data science makes us understand data better

Practicality and 'rule of thumb'... You excel at that sort of stuff. 👌🏽

Tweedie distribution baby! Can be seen in some regression datasets where the government / local authority restrics max salary/price or whatever (California housing).

Great video, it would be EXTREMELY helpful to me as a perpetually aspiring data scientist if you could show how you might go about fitting a distribution to your data and using it in a simulation exercise. (I have an idea of how I might go about doing this, but I'm acutely aware that others might have better insights!)

I definitely turn noisy data into sensible data by making bins. This is especially true with frequency per day. At the daily level, picking out trend is difficult, but grouped to several months, or even several years, really helps create some worthwhile numbers.

I like your teaching style.

Love the content. I started studying data science and your videos helped me a lot. A small suggestion/ request. For each concept/video that you are covering, can you also share some resource that you followed? Thanks

Just one realization of that pointy distribution from my work: it happened to a variable that is regulated but a not so powerful regulator. In my case, wind velocity in a tunnel (so signed and 1D) that is being regulated but some not so powerful fan

Im excited that you teach the message “what does it tell me” and explain by real life 🎉.

the power of this video 🔥

I think that pointy distribution is modelled as a cauchy distribution and the skewed distribution is what you call a pareto distribution or an exponential distribution

would be nice if you'd expand on how to analyze these dists

Pretty nice. Add another one on how to model those distributions.

I don't think most data scientists have the additional time to delve into geometry, but geometry is very much about the "shape" of mathematical objects.

1:44 I don't agree that a max GPA of 4 is a physical limitation in any usual sense of physics. If it is, then by what physical principle? Conservation of angular moment? But I appreciate the video overall. These are definitely common cases in data science. The video is both information and practical. Lately I have been thinking about counterfactual inference when there is an unknown upper bound on a facility's capacity. The bound will not change when intervening on the rates, but how the shape of the distribution will change with respect to the boundary and the expectation of the intervention distribution is non-obvious to me. From the modelling side I could derive a truncated distribution. Or I could derive the distribution of MAX(X, c) where c is a parameter or hyperparameter, although in NUTS/Gibb/MH sampling I find that such bounds are sampled poorly (i.e. lots of divergences) when they're treated as a parameter. Or you can have mixture distribution that transitions from "away-from-boundary behaviour" to "near-to-boundary behaviour".

Can you make a video on data engineering vs machine learning engineering vs data scientist vs data analyst? Great vid btw!

Isn't the pointy one the plot of distances away from a SLR line with L1 cost? I can't precisely remember the name of the curve, but it's not the curve shown.

interesting ideas, but would be more helpful if you had a list of action items w/ each distribution.

Has Bard/ChatGPT impacted your work in any way? How did you land up in DS?

👍

always great to watch your videos! Is there a way to contact you directly @ritwikmath?