The presentation is "nice", but not even close to what it needs to be for making any assumptions on the result. Questions that are necessary to be clarified:
1) What is your source? - the slides only state it's the biggest platform.
2) How do you compensate, that your dataset only represents x% of the real offers?
3) If you think you have a reasonable share of the job offers, how do you justify this assumption?
These are the main questions, that I would ask if you tell me to use it as business information - all the technical stuff is secondary. But there are more:
4) How do you compensate a change in recruitment strategy? You are just looking at one platform.
5) What makes you sure, that every item in your dataset is interpreted correctly? Can you somehow make sure, that your parsing and reformating is always correct? Can you verify that? Can you detect if not?
6) Can you detect a change on the website itself, e.g. new categories, differences in job description?
Also my experience is different, with getting a ton of job offers currently, directly via business network, I'm willing to look at a statistics based bigger picture that changes my mind. But the interpretation and the harsh change, together with these open questions make me believe that you more likely have a technical problem. So my advise would be, spend a lot more slides on the data and not the acquisition, because all acquisition effort is useless if you can not verify your data and results.
Programming is often reduced to the technical part of putting down code that runs a machine. But as writing is not drawing symbols and reading is not vocal reproduction of these symbols, there is more behind programming than making a machine do what you want. Everything that is teached in school is in the end reduced to being able to solve problems.
Learn how to program is in fact learning how to formalize a large problem, think about it in a structured way, and if the problem is too large as a whole, divide it into manageable parts, that solve parts and together solve the problem as a whole. Additionally an individual that has learned how to program also learned about importance of precision of instructions.
Being able to do this, and being able to think about systems and processes in a structured way is helpful in a lot of disciplines beside tech industry.
Therefore it also doesn't matter how children or young adults learn to code, this is more a question about how to get someones motivation and focus.
And especially this is not about getting a job in tech industry. Learning this type of problem solving (which is not exclusive to programming) and thinking about systems etc. will help you with/in most of the jobs out there, e.g. kitchen chef, event/wedding planner, car mechanic, musician,... you name it - even if you don't write a line of code anymore in your life.
1) What is your source? - the slides only state it's the biggest platform. 2) How do you compensate, that your dataset only represents x% of the real offers? 3) If you think you have a reasonable share of the job offers, how do you justify this assumption?
These are the main questions, that I would ask if you tell me to use it as business information - all the technical stuff is secondary. But there are more:
4) How do you compensate a change in recruitment strategy? You are just looking at one platform. 5) What makes you sure, that every item in your dataset is interpreted correctly? Can you somehow make sure, that your parsing and reformating is always correct? Can you verify that? Can you detect if not? 6) Can you detect a change on the website itself, e.g. new categories, differences in job description?
Also my experience is different, with getting a ton of job offers currently, directly via business network, I'm willing to look at a statistics based bigger picture that changes my mind. But the interpretation and the harsh change, together with these open questions make me believe that you more likely have a technical problem. So my advise would be, spend a lot more slides on the data and not the acquisition, because all acquisition effort is useless if you can not verify your data and results.