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These case studies videos are helping me alot. Please come up with more case studies.

Hey Jay, very useful video, thanks for sharing! If you had a take-from-home assignment like this, do you think it would be useful to run any more advanced statistical analysis? Maybe analyzing the population means with a t-test? Cheers

Thanks Jay, great example for a SQL + case study practice! I'm a beginner and still learning, can you please explain a little bit more about the normalization and casual inference you mentioned at 6:59? Or just pointing me to some material will be appreciated.

@iqjayfeng

2 жыл бұрын

Nothing on deck but we’re coming out with a data analytics course soon going over it!

Interesting! Good information to have

Amazing resource! :) Well don!! TY! Keep going! :)

Hi Jay! Why does the output still show 0 under the at_least_once_call_connected column? I thought we filtered that out when we call MAX in the sub query. Please advise. Thanks

Great video ! Really in-depth

@iqjayfeng

2 жыл бұрын

Much appreciated!

Hey ! I’m new to this, but I wanted to ask why double joining on the same tables ?

Hi Jay, why did you use Max aggregate function for "at least once call connected"?

@iqjayfeng

2 жыл бұрын

When you run MAX on only values that are 1 or 0, you’re basically setting a binary outcome for if they at least connected once. For example if I have nine 0s and one 1, then the MAX function will return 1 showing that there was at least one call!

Good stuff. Would this type of case study be something that entry-level analysts would be expected to solve?

@iqjayfeng

2 жыл бұрын

Probably not but it would make you the top of the pack if you could

can you also solve data analytic problem wrt ecommerce example number of product vs revenue trend over number of years

Hi Jay, This was very helpful! I was using this to prep for an interview and wanted to understand your approach here. Why did you left join marketplace into chats? Wouldn't that exclude any transactions that did not bother with the chat function ? If we do marketlace left join chats, then avg(count (m.id)/count(c.id)) as score, case 1 if call connected is 1 else 0 as used_chat group by used_chat would also work right?

@iqjayfeng

2 жыл бұрын

LEFT JOIN allows us to all transactions but not all the chats

@user-gy7uu9gt8n

Жыл бұрын

I think the chat function is for customer who have product problems. So we only focus on those who attempt to make calls and whether successfully connected calls lead to real purchase. Yes, the query Jay provided would exclude transactions that did not use the chat function, but it does not matter. They just don't need it.

I'm trying to get into Data Analytics. It seems really interesting. I have no background or experience with this line of job. Gotta learn the basics then maybe come back to this video. XD But overall, great video. :)

and I will find my way back there too!

Thanks for watching! If you're interested in learning more about Data Analytics, then check out our newly launched Data Analytics course! The course is designed to help you learn everything you need to know about working from basic concepts to more advanced techniques. www.interviewquery.com/learning-paths/data-analytics

Great work 👏 But in this case study you didn't show numerical data as in questions

This too advance for me, what should I do?

Hell yeah I think it’s time we do it in the kitchen. I totally and sincerely agree with you

I rarely see case studies good vidoe

@iqjayfeng

2 жыл бұрын

Thanks!

00:30

Lmao I don't know what's wrong with my thinking, but I thought 'success' was just whether or not people used it... 'Well just compare the number of buyers to the number of people who made calls'... I feel too dumb for this field now...

@iqjayfeng

Жыл бұрын

Lol that’s not dumb - it’s just a common fallacy that you will now completely see through now when it comes up again!

teach you a lot of the fundantals of soft. You don't even need to sit through the entire tNice tutorialng to get a good grasp on the basics.

Too much water then solving. Too much talk.