I haven’t written anything for “a thousand years”, but suddenly there was an occasion to blow the dust off a mini-cycle of publications on “learning Data Science from scratch”. In contextual advertising of one of the social networks, as well as on my favorite Habré, I came across information about the course . It cost mere pennies, the description of the course was colorful and promising. “Why not restore skills that have become dusty from uselessness by completing the next course?” I thought. Curiosity also played a role, I have long wanted to see how the organization of training at this office works.
I will warn you right away that I am in no way affiliated with the development of the course or their competitors. The entire material of the article is my subjective value judgment with a touch of irony.
So, you still do not know where to invest your hard-earned 990 rubles? Then you are welcome under cat.
As a small preface, I’ll say that I’m somewhat skeptical about promising courses that can turn a beginner into a “successful data analyst with a salary of over 100 rubles” in a short time (although you probably guessed this from the title picture of the article).
A few years ago, in the wake of active advertising for Data Science training, I tried to learn at least something from the field of data science in different ways and shared notes about the bumps I received with Habr readers.
Other articles in the series1. Learn the basics:
2. Practicing the first skills
And after a long time, I decided to try another course.
Course Description:
The description of the course "Start in Data Science" promises that, having spent only 990 rubles (at the time of writing) we will receive a four-week course in the format of video lectures and practical tasks for beginners. Also, do not forget about the compensation of part of the cost of the course in the form of a tax deduction (They promise to send all documents by mail).
There are two conditional blocks within the course, one will talk about what Data Science is, what are the popular areas, how you can develop a career in the field of DataScience. The second block looks at five tools for data analysis: Excel, SQL, Python, Power BI, and Data Culture.
Well, what does it sound like “delicious”, we pay for the course and wait for the start date.
In anticipation, we go to your personal account the day before the start of the course, scroll through parting words from the developers and wait for notification of the long-awaited start of the course.
Time flew by unnoticed, "D-Day" has come, you can start learning. Having opened the first lesson, we will see a scheme familiar to online learning systems - a video lecture, additional materials, tests and homework. If you have ever used Coursera, EDX, Stepik, then you should not have any problems.
Within the course:
Let's go in order. The theme of the first lesson is "DS Overview: Foundation, Benefit, Application", it begins with a video lecture, like all subsequent lessons.
And from the very beginning it is felt that the comrades were guided by the approach "So it will do" from my favorite Soviet cartoon.
From the very first minute, you understand that the material for the course was not written down on purpose, but was torn out of some other open lessons or specialized courses. Also to video no subtitles and no download option for offline viewing.
After the lecture, additional materials for the lesson were offered (a presentation from the video lecture and recommended literature), we will not analyze them.
Then we have a test. Tests are different in the degree of complexity and the adequacy of the questions to the material covered.
And here again the lack of interest in the result of training is manifested, You can fail the test, but it won't affect anything, you will still successfully pass the lesson, but the request for an additional attempt to retake will most likely remain unanswered.
Subsequently, the scheme of the lesson: “video -> additional. materials -> test" will be the basis of the entire course.
Sometimes the lesson will be diluted with questionnaires and independent homework.
There are only two homework assignments. And to be honest, I only went through one.
The first homework is to send in your resume with a description of your key skills. I can’t say for 100%, but it seems to me that almost any resume will be accepted and the task will be credited. After the assignment, you will be sent additional materials - recommendations. Remembering how I suffered with homework on Coursera, I was even a little upset at such simplicity.
After completing the introductory part, the study of the long-awaited "Tools for getting started in Data Science" begins. And the first is a lesson with a loud title: "Working in Excel: pumping skills from zero to analyst."
Wow! It sounds tempting, but in reality, the difference between expectation and fact is the same as between a photograph of a hamburger from a fast food advertisement and what they give you at the checkout.
In fact, we will observe how moving from autocomplete cells in Excel to a confused description of the function "VLOOKUP ()", the teacher will hesitate like Hamlet, on the topic of the question "To be, or not to be" "Explain everything for beginners" or "Give material interesting for the pros. In my subjective opinion, neither one nor the other turned out.
It is especially great that despite the fact that the course does not provide a live webinar. That is, these are not records of those classes that you missed, but simply records of classes that took place a long time ago (see the figure below), the authors still decided to preserve the atmosphere (or maybe just lazy) и make you spend five minutes watching a teacher solve sound problems.
After the video, according to the standard scheme, follow - additional material and a test.
The next topic is about the SQL language. The lesson gives the very basics and examples of working with SQL queries, in principle, videos and articles on a similar topic can be easy to find on the Internet for free.
SQL is followed by a lesson on processing a dataset from Kagle using the Pandas Python library. The scheme of the lesson has not changed: video -> add. materials -> test. There are no additional tasks provided, there is not even a task with automatic verification of results. Thus, you definitely won’t have to install Anaconda and write code. Also it is worth noting the small font of the code in the video lecture, it’s pointless to watch it on the phone, and I had to look at it almost point-blank on the monitor.
The fourth lesson "Visualization of a logistics report in PBI in 10 minutes" (видео кстати длится минут 50) . In this video, they will talk about the curious Power BI tool, to be honest, I have never heard of it before.
Unexpected end of the course:
The final fifth lesson will tell about the general principles of competent data storage, the lecture is again torn from another course. In this lesson, in addition to the standard test, homework appears again, but I did not do it. Do you want to know why?
Because, when I opened the page of a course that was only half completed today, I saw this:
That is the system considered that I successfully completed the course, although in fact I did not complete it.
Moreover, after watching all the remaining videos and conducting tests, the counter did not change, but remained at around 56%. I suppose that I could not watch anything at all and not take tests and still get a “Diploma”.
It is especially surprising that in the mailing list the course officially lasted from July 22 to August 14, and the “Diploma” was already issued to me on 04.08.2019/XNUMX/XNUMX.
Outcome of training
The company's website at the end of the training promises us: "Your qualifications will be confirmed by documents of the established form." But the trouble is, this course does not seem to be either a retraining program or an advanced training program, which means you will just get "certificate", which, in principle, has no official status.
Probably, the question would be reasonable: “What did you expect for 990 rubles?”. To be honest, I didn't expect anything. It is clear that quality courses are significantly more expensive. But the trouble is that there are free courses that are made not only no worse, but many times more professional, for example, courses from or from . The same "certificate" of passing the course (if at least someone needs it), there can be obtained completely free of charge.
Of the pluses, it can be noted that these review materials are collected in one place and it will be really easier for a person who is completely unfamiliar with Data Science to navigate in this area.
At the end of the course, we are promised that we will learn a bunch of tools, and in our resume we can write like this:
In fact this is a very strong exaggeration. In fact, you will simply hear about many instruments and nothing more.
Summary
In my opinion, the course carries a minimum of payload, it is especially upsetting that the authors were too lazy to record separate video lectures for it. In a good way, it’s a shame to ask for money for this, well, or you have to ask 10 times less.
But once again I repeat that all of the above is just my subjective value judgment, whether to take this course or not is up to you.
PS Perhaps over time, the authors of the course will finalize it and the entire article will lose its relevance.
Just in case, I’ll write that it is valid for the very first launch of this course from July 22 to August 14
PPS If the post turned out to be so unsuccessful, I will delete it, but at the beginning I would like to read the criticism, maybe just something needs to be edited. Otherwise, for now, it looks like a minus of uncomfortable criticism of a poor-quality course
Source: habr.com