For what feels like a 'thousand years', I haven't written anything, but suddenly a reason appeared to dust off my mini-cycle of publications on 'learning Data Science from scratch'. I stumbled upon information about a course in a contextual ad on a social media platform and also on my favorite 'Habr'. . It was quite inexpensive, and the course description was colorful and promising. 'Why not revive the dusty skills by taking another course?' I thought. My curiosity also played a role; I had long wanted to see how the training was organized at this company.
Let me warn you right away, I am not affiliated with the course developers or their competitors. The entire content of this article is my subjective evaluation with a slight touch of irony.
So, you still don't know where to invest your hard-earned 990 rubles? Then please read on.
As a small preface, I would say that I have a somewhat skeptical attitude towards promising courses that can turn a newbie into a 'successful data analyst earning over 100,000 rubles' in a short time (although you probably guessed this from the headline image of the article).
A few years ago, amid the active promotion of Data Science education, I tried various ways to grasp at least something from the field of data science and shared my notes on the lessons learned with Habr's readers.
Other cycle articles,1. Learning the basics:
2. Practicing initial skills
And after a long time, I decided to try yet another course.
Course Description:
The course description for "Start in Data Science" promises that by spending just 990 rubles (at the time of writing this article) we will receive a four-week course in the format of video lectures and practical assignments for beginners. Don't forget about the compensation of part of the course cost in the form of a tax deduction (they promise to send all documents via email).
The course consists of two conditional blocks: one will explain what "Data Science" is, the popular directions available, and how to develop a career in the Data Science field. The second block covers five tools for data analysis: Excel, SQL, Python, Power BI, and "Data Working Culture."
Well, it sounds "tempting"; let's pay for the course and wait for the start date.
Anticipating, we log into our personal account the day before the course begins, scroll through the encouraging words from the developers, and wait for the notification about the long-awaited start.
Time flies unnoticed, "D-Day" has arrived, and we can begin our studies. Opening the first lesson, we see the familiar scheme of online learning systems – video lecture, additional materials, tests, and homework. If you've ever used Coursera, EDX, or Stepik, you shouldn't have any problems.
Inside the course:
Let's go in order. The topic of the first lesson is "Overview of DS: fundamentals, benefits, applications," starting with a video lecture, just like all subsequent lessons.
And right from the beginning, one can feel that the creators were guided by the approach "It’ll do" from my favorite Soviet cartoon.
From the very first minute, it’s clear that the material for the course wasn't recorded specifically; it was taken from some other open lessons or specialized courses. Also, the video lacks subtitles and the option for offline viewing. After the lecture, additional materials for the lesson are offered (presentation from the video lecture and recommended literature), but we won't cover them.
Next, we're faced with a test. Tests vary in difficulty and the adequacy of questions relative to the covered material.
And here again, the lack of interest in the learning outcome is evident,
you can fail the test, but that won't affect anything , you will still successfully pass the lesson, and the request for an additional attempt at retaking will likely go unanswered., you will successfully complete the lesson, but your request for an additional attempt to retake it will most likely go unanswered.
In the future, the lesson structure: “video -> additional materials -> test” will be the foundation of the entire course.
Sometimes the lesson will be supplemented with surveys and independent homework assignments.
There are only two homework assignments. To be honest, I only completed one.
The first homework assignment is to send your resume with a description of key skills. I can't say for sure, but it seems that practically any resume will be accepted and the assignment will be credited. After the assignment, you will receive additional materials – recommendations. Remembering how I struggled with homework on Coursera, I was a bit disappointed by its simplicity.
After completing the introductory part, we start studying the long-awaited “Tools for Getting Started in Data Science.” The first lesson has an intriguing title: “Working in Excel: Skill Improvement from Beginner to Analyst.”
Wow! Sounds tempting, but in reality, the difference between expectation and reality is like the difference between a photo of a hamburger from a fast food ad and what you actually receive at the counter.
Essentially, we will observe how, moving from auto-filling cells in Excel to a convoluted description of the function "VLOOKUP()", the instructor, like Hamlet, will waver between the questions of "To be, or not to be" – "Explain everything for beginners" or "Provide material interesting for professionals". In my subjective opinion, neither was achieved.
It's especially great that despite the course not providing live webinars, meaning these are not recordings of the sessions you missed but just recordings of classes that took place long ago (see the picture below), the authors still decided to maintain the atmosphere. (or maybe they just got lazy) and they make you watch for five minutes as the instructor resolves sound issues.
After the video, the standard structure follows – additional materials and a test.
The next topic is about the SQL language. The lesson provides the basics and examples of working with SQL queries; basically, videos and articles on similar topics can be easily found on the internet completely free of charge..
The SQL lesson covers processing a dataset from Kaggle using the Python library "Pandas". The structure of the session remains the same: video -> additional materials -> test. No extra assignments are provided, not even a task with automated result checking. Therefore, you definitely won't need to install "Anaconda" or write code. Additionally, it's worth noting the small font size of the code in the video lecture.Watching it on a phone is pointless, and on a monitor, I had to watch it almost up close.
The fourth lesson is "Visualizing a Logistics Report in PBI in 10 Minutes". (by the way, the video lasts about 50 minutes) . This video will introduce an interesting tool, Power BI, which I honestly had never heard of before.
An unexpected conclusion to the course:
The final fifth lesson will cover the general principles of effective data storage, but the lecture is again taken from another course. In this lesson, in addition to the standard test, there’s a homework assignment again, but I chose not to do it. Want to know why?
Because when I opened the course page today, which I only completed halfway, I saw this:
That is, the system considered that I completed the course successfully, even though I haven’t actually finished it..
Moreover, after watching all the remaining videos and passing the tests, the counter did not change and remained stuck at 56%. I suspect that I could have done nothing and not taken any tests and still received a "Diploma.".
It’s particularly surprising that the email stated the course ran from July 22 to August 14, yet I received my "Diploma" on August 4, 2019.
Outcome of the training.
The company's website promises us at the end of the training: "Your qualification will be confirmed by established documents." But alas, this course does not seem to be either a retraining program or a qualification improvement program, which means you will simply receive a "certificate," which has no official status whatsoever..
It’s probably reasonable to ask: "What did you expect for 990 rubles?" Honestly, I expected nothing. It’s clear that quality courses are significantly more expensive. But the unfortunate thing is that there are free courses that not only compare favorably but are often far more professional, such as the courses from or from The same "certificate" for completing the course (if it's even needed by anyone) is there. can be obtained completely free.
One of the advantages is that these review materials are collected in one place, making it significantly easier for someone unfamiliar with Data Science to navigate this field.
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 reality this is a significant exaggeration. You will essentially only hear about many tools and nothing more.
Summary
In my opinion, the course offers minimal value; it is especially disappointing that the authors did not bother to record separate video lectures for it. Honestly, it's shameful to ask for money for something like this, or at the very least, they should charge ten times less.
But I repeat, everything stated above is merely my subjective assessment; whether to take this course or not is up to you.
P.S. Perhaps over time, the course authors will refine it, and this article will become outdated.
Just in case, I will mention that it is valid for the very first run of this course from July 22 to August 14
P.P.S. If this post turns out to be a failure, I will delete it, but at first, I would like to read some criticism; perhaps there's just something that needs editing. For now, it looks like a dismissal of uncomfortable criticism of a poor-quality course.
Source: habr.com
