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Great video. (1) the message about the data quality applies to all data analysis, including the simplest form of analysis. (2) it is not obvious the some of the examples such as AI chat bot producing incorrect answers is solely due to bad data, it may stem from an inadequate model too, and (3) there is a large literature in statistics and econometrics thinking about how to work with contaminated data, given that’s sometimes the best we can do. It will be interesting to see whether methods are developed in the AI/ML literature to work with bad data too in building these sophisticated LLMs, etc.

Do people worry about interference or spillover of treatment within the population of study in practice (in business settings)?

Should we consider practical significance in cases where we don’t have statistical significance?

Great stuff, Delali

Your alternative hypothesis at the 4:10 minute mark sets this up as a right-tailed test? Any reason to do that instead of a two-tailed setup in this example?

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What an illuminating Facts! So, which are the Soft Skills to learn, please...?

Good❤

Well Said. Thank you for sharing.

So what's the way forward

Essentially, the jobs that are cognitively involved and non-repetitive are the jobs that historically survive technological revolutions.

All hype 😂

Hello sir My name is Lekan. I’m learning data analytics and I spent A lot of time learning excel and SQL ( for like five months) I’m diving into tableau by next month. After tableau I will learn python. Please can you be my mentor. I also have LinkedIn account, can we connect on LinkedIn. I will be so glad for positive responses.

Interesting that most of the use cases have a cause and effect flavor …

Great way to conceptualize data science applications in retail. Demand forecasting is a huge pain point for businesses as you mentioned. What is the difference between demand forecasting and sales forecasting, and why did you categorize them separately?

This video is incredibly insightful! It's fascinating to see how retailers are leveraging data to improve their operations and customer experiences. I'm curious to learn more about specific case studies and the challenges that retailers might face when implementing data-driven strategies.

I agree. Understanding the big picture is very important. The way the business makes money is very important and how your role contributes to that is important

Informative and great alternatives to the mean, but I would have liked to see the median highlighted to remain in the realm of simplicity. Sure, the median appears in the box plot but the entire plot is still too complex for high level summaries. Any reason you didn’t explicitly mention the median as an easy (one statistic summary) alternative to the arithmetic mean or did I miss it? Again, looking at full data should be the default, but given people will typically end up summarizing info with one or two numbers, it will be good to advocate the median if one has to settle for one statistic.

Oh I see This is so on time and helped. I'd love to know more on the principles of creating very powerful/insightful recommendation systems for users. Similar to that of Netflix and Yango ride. Simply put, how do I keep users on my platform that's already in demand.

I like how you really simplified the concept of data science to its core value proposition. Valuable video for anyone looking to learn the basics void of dense technical jargons. Great job

Insightful