and so on, so it wasn't mystery functions that just appear in the environment.
And that convinces me that the issues I'm having with their courses are mostly sourced in their educational methodologies.
Now, I don't want to scare anyone off from trying DC. But at this stage of my life, their methods don't work so well with my brain.
On the other hand, the #Google + #Coursera Cybersecurity program and the #IBM + Coursera Backend Development program seem to do the opposite. Because of their target markets, they assume students don't have much relevant background and they give lots of effort covering the most basic things in great detail.
Which is good, but in some cases, it is hard to pay attention to things I've known for over twenty years.
Since he does that already in his present job, he may just need to spin up some personal projects that are similar to his work stuff, just using the desired technologies / languages / tools.
I felt like DQ's teaching method would be much faster and less prone to "I just did this in the prior course, but I can't recall a bit of it". However, DQ has only ~70 courses (versus 300+ on DataCamp) and won't tell you how much it costs without first creating an account. (Quite a scumbag marketing tactic. I'm supposing it is used to persuade clueless investors that DC's growth is unending.)
Anyway, Brandon ( @bthall ) was talking about the incomes that data scientists make, and some of those numbers are way more than I've ever been paid. So I accepted his offer to pay for #DataCamp.
@bthall (Brandon) and I discussed it a little bit at the time.
I haven't heard anything else about sexual harassment or assaults, so I presume that people's behavior changed.
So a year or so ago, Brandon offered to pay for a year of DataCamp in order to help me prepare for a better job in the future. (Yes, I know I'm old, but I have no pension or other benefits from my previous jobs. I expect to have to work for the rest of my life.) I looked it over and they cover many different things that have been on my "learn this" list for years.
There's another track where I've completed all the courses except one. If it turns out that tomorrow is the end, I'll try to see whether I can squeeze out four hours to complete it before they shut me down.
And I'll get additional "XP" gamification points and I'll hit 175 consecutive days (with an asterisk ... after I hit 125 or so, they awarded me two free days, which came in handy when the Internet service was down for a day and a half and again when power was out for most of an evening.)
I don't really care about gamification points, as I don't see any advantage to them. I'm sure #DataCamp is seeing some increased engagement and interaction, or they'd probably drop it.
What mattered to me was trying to keep pushing that consecutive days number higher. Not just so I could see it on the site. Also because I know that I have to do the coursework regularly (such as every day) if I want to see results.
https://gnusocial.jp/tag/datacamp (some socnet sites [such as Friendica instances] will also create local to their site copies of known hashtags ... so you should be able to go to the local tag URL and see this entire series)
After several hours of doing coursework, you get a page that just won't finish loading (neither the 'run code' nor the 'submit assignment' buttons load). Typically, when that happens, just go to bed and reload in the morning. (Reloading doesn't help ... I suspect that the company has to reset something, which gets downloaded when someone resets overnight / early morning.)
And it is Monday night / Tuesday morning, so there's not the "urgent" necessity for their technicians to do infrastructure work overnight ... I honestly think a server gets "stuck"
Still working on finishing the track on #DataCamp. But I wanted to add a little more to this.
It took me most of a year to discover this, but I struggled mightily with data analysis functions in #Python + #Numpy + #Pandas, in #R-lang, and in #Julia-lang. #SQL was much easier to comprehend. But I've recently had a few courses where they were covering pure Python, without the data analysis packages, and that is totally different.
Even though I've barely touched Python in the past 20 years or so, it feel familiar and almost everything we do feels "natural". With the data analysis / data science content, it feels like there are dozens of nearly identically-named functions and methods, each with its own special syntax and list of arguments to pass to it.
are easily mixed up and I always (no, seriously always) pick the wrong one first.
I guess that's not a DataCamp issue, but more of a problem with the tools being covered.
But DataCamp's methods don't help with this much. Each one-hour chapter of each four-hour course is supposed to be a sequence of bite-sized tools that one learns to use and then remembers it when it comes up again later. Unfortunately, it quickly turns into a big ball of mud.
Since the data science functions being studied (in Julia-lang, R-lang, and Python + numpy + pandas) consist of dozens of similarly-named functions and methods, each with its own very different set of arguments, they're already hard to keep sorted in my head. The video method doesn't help with that.
Now, this may just be me. One of my friends ("S") says she doesn't read well enough to use books as her primary learning channel. But even she had to buy some books to help with her own video-based learning. There are reasons that in-person college / university learning programs also feature textbooks as a major part of their learning.
@lnxw37j1 Hopefully, @bthall won't misinterpret this. I am genuinely glad he got me a year of #DataCamp.
Embed this noticeLinux Walt (@lnxw37j1) {3EB165E0-5BB1-45D2-9E7D-93B31821F864} (lnxw37j1@gnusocial.jp)'s status on Sunday, 01-Dec-2024 03:14:07 JST
Linux Walt (@lnxw37j1) {3EB165E0-5BB1-45D2-9E7D-93B31821F864}Less than two weeks left of #DataCamp. I have learned a lot, including:
* I'm not really a "watch video" learner. I'll see something and immediately after the video I cannot remember what to enter.
* Many DC courses feel a little rough. "Plot this against that" doesn't always mean this is X and that is Y. Further, a lot of times, the exercises go beyond the video lecture, so I look up Python | R-lang | Julia | SQL commands to achieve the requested results, but they're usually not the commands DC wants me to use.
* Often, the instructions in DC exercises are just big gray blocks to me. I don't read them because the level of detail is too high. IRL, there's generally a sort of introduction / summary and a conclusion summary that can inform the early work. This allows details to wait until required.
Probably next step is to get physical books to continue, renew, and expand my learning.
Lots not to like ... in many ways, their courses and especially their practice exercises look like they hired a couple of college kids to build them over a weekend, then posted them without having an editor look them over.
In multiple courses under the subjects of Python, SQL, Julia, and R-Lang, the exercises will mis-spell a language keyword or some library function / method that is widely used in Data Science. In a couple, the answer is already entered into the question ... just select the choice that matches what they've just showed.
In the "real world" projects, they tend to go beyond what the courses have covered. Yet, they're opinionated about which functions / methods are used (and sometimes even the order they're used in). So you do some research, find some functions that produce the exact desired results, and the project is rejected because your research didn't uncover the desired functions to use.
Now, there's lots to like, too. For example, despite having both R-lang and Julia (and Scala) on my to-learn list for years, this was my first hands-on experience with all three.
I drew a complete blank in 4 of the first 5 questions, so I didn't even finish it. #r-lang
While I return to the Python tracks, I'll try to do some more R stuff on the side. Now that I'm sure I absolutely will not get the approximately 200 hours of #Python related coursework done before the end of December, so I will have to pick and choose which subsets to complete.
So, we're taking stuff that I already don't really understand and wrapping them in functions. The "wrap this in a function" is mostly pretty easy, except the part about renaming a function-internal variable. That part is also easy, but I never remember it until I submit the exercise.
I think I need to download some data from the US Census Bureau and start chewing through their data with R-Lang, JuliaLang, and so on.
Too bad #DataCamp doesn't also cover GNU Octave. It's been so long since I used Octave and SciLab that I'm sure I'd just sit there staring at a blank screen.