@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.
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.
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.
This is sort of a tangent to the Google + Coursera https://www.coursera.org/ CyberSecurity and the IBM + Coursera Backend Development programs, but it just happens that both of them are currently covering #Bash and Python right now and the CyberSecurity program is also covering SQL.
An example:
```
SELECT common_name, COUNT(*)
FROM biology
WHERE common_name LIKE '%lady%'
GROUP BY common_name
ORDER BY common_name;
```
gives the same results as
```
SELECT common_name, COUNT(common_name)
FROM biology
WHERE common_name LIKE '%lady%'
GROUP BY common_name
ORDER BY common_name;
```
when there are no nulls (missing values) in the common_name field.
And without some clues in the assignment text, it isn't possible to know which one their JS-based checker will accept. So in the majority of assignments, I get things like this "wrong" in the first pass.
I've lately been vocal about my perception that quality control and beta testing should have exposed such things and led to their correction before deployment to paying customers.
In the case of the #SQL courses, I did recently have a multi-part assignment where they asked for the NOT NULL version from the start, but only in the 3rd or 4th part of the assignment did they expressly say they wanted that and use pre-entered SQL scaffolding to show why it matters in that specific case.
In other news, I've temporarily de-emphasized both #Python and #R-lang (and delayed my exploration of #Julia) because I'm on a "track" that focuses on SQL. At the current rate, I should be finished with the SQL track & emphasis in a month or so.
I've also noticed they have some courses that cover MSExcel / PowerBI, Tableau, Google Sheets, and (of course) "AI". I expect to take the intro courses for most of these topics. I don't typically use spreadsheets except as gridded formatting tools for lists, but many years ago I used Lotus 1-2-3 and Quattro Pro and even took some classes. So relearning such things as formulas and internal scripting may be helpful in making these tools more broadly useful.
With SQL, at least, it seems to be an artifact of the way their hands-on code runner works (Displays a short `head` of the relevant tables ... so when you're working on queries, you may not have a direct way to see whether your query does specifically what you expected and intended.)
With R-Lang, it is just that it isn't always apparent what the language will do. Some things are inexplicably backwards compared to most other languages I've seen, so mentally I tend to go with the wrong choice. Also, the practice question set is too small. I've reached the point where some of the practice exercises are familiar enough that I know which answer to choose immediately without having any understanding of why that is the correct choice.
Their continuing practice exercises seem to emphasize the smallest tripping factors, but I guess that will be useful if I'm ever paid to use these languages for data analysis.
I am Ștefan (ș as sh, I also accept Stephan or the equivalent in your language). I’m 21 years old, ♑, he/him, proud #leftist and soon to graduate CS @ UVABc. Sort of proudly living in #romania. My native language is Romanian, fairly proficient at English, slowly learning #finnish (and #italian).