Before the course I was already familiar with IT and statistics, but very superficially and without understanding how and where to apply the knowledge. Such study of the field may be useful for general outlook, but it is extremely impractical. My goal before the course was to get into IT, get up-to-date knowledge, do projects and visualise the whole picture (what an analyst should do). The course gave me all of this. What I liked most about the course was the structured and sequential dive into data analysis. Each next unit is linked to the previous one (example: after Python, you learn statistics not just in words, but by applying programming, which helps to visualise what was in theory). After the course, the help does not end. HR courses help you to find a job in the most structured way possible. It turns out to be a kind of another block of the course with a supervisor (huge thanks for help and support on the final straight) and a lot of interviews and test tasks.)

I was satisfied with the course: I learnt new technologies (in applied, not review format), and closed gaps in fundamental understanding. And most importantly, I got the idea of deploying my data solution in the cloud. As a result, I took a server on DigitalOcean and made my workspace there: I deployed clusters, Jupyter, Superset for visualisation, Airflow for automation, as well as Spark and ClickHouse. I was very satisfied with it.

I used to work as an analyst in a bank - doing BI-analysis, preparing reports of various complexity and data showcases for other users. I took the course to learn classical machine learning so that I could change jobs in the future.

Expectations from the training were met by 80 per cent. The course is very interesting and intensive. It was challenging, especially in the module on deep learning, but I hope to fill in the gaps in the future. It's great that all the materials stay with us and can be referred back to.

I enjoyed the first two modules the most - Python and machine learning. The tutors were very approachable in explaining difficult points and helping to solve problems, tech support too :)

I found the statistics module a bit fast, I would have liked to spend more time on it.

The course had an impact on my career - in July I started applying for vacancies and in August I found a job as a Junior Data Scientist, which I am very happy about! There were two stages of interview, and now I am working in a new place.

To all the instructors, thank you so much for teaching us from your experience and helping us to achieve our goals :)

Thank you very much to the whole team, you make a cool product!

I am a thermal power engineer, graduating with a master's degree in 2019. I have been working as a data analyst for the last year. I am the only analyst in the department, mostly doing ad-hoc tasks, mostly using SQL and Python. Before that, I worked at the intersection of thermal power and IT for 2,5 years.

I originally wanted to gain knowledge in machine learning. After the February events I came to the conclusion that it was time to change my profession to the appropriate one.

I liked the first two blocks very much, I enjoyed the course for all 4 months. Somewhere from the third or fourth lesson of the third module the tasks became impossible for me. Since I was very responsible in my approach to the course, I had a kind of burnout because of it.

Why the tasks stopped being feasible is a multifactorial phenomenon: the complexity of the topic, burnout, and a drop in the quality of the lecture material. Judging by the survey in Telegram, the majority also liked the first two modules, and there were many more questions for the third and fourth modules. The quality of the lectures in the fourth module raised the most questions: 20-minute videos with an average duration of an hour and a half for the rest of the course, little practice, just definitions and sometimes screenshots of solved problems.

Nevertheless, the tasks were quite doable, but I can't say that I got anything out of this module in my head. So a special thanks for opening a block on statistics from the Data Analyst course. But I haven't touched it yet because of the accumulated fatigue from studying.

The final project is cool in terms of stack and idea, but it was too big and without hints (as I realised, they appeared on the next threads). Because of that, only a few people turned the project in before the deadline, as I understand it. Still, it's what the course is all about.

The fifth block on interviews is very interesting due to algorithms, special thanks to Lyosha Kozharin for these 4 lessons. Unfortunately, I didn't hand in all the tasks on time, as my "burnout" hadn't passed by that time :(

I think Docker should be added to the first module. But as I found out recently, K/C has a free course on Docker planned, which sounds awesome. I would also add more information on bousting in the second module.

The course hasn't impacted my career yet, but hopefully it will. I'm not sure what to mark as results - getting familiar with ML basics really went well, repeating and strengthening the Python base too.

I hope that the third module will become simpler. For example, instead of one huge task, there will be several smaller tasks, as in the second module. In the fourth module, we should double (or even triple) the timing of lectures and add as much practice not with screenshots of written code, but with live coding and comments.

I would like to thank the whole team of course creators for quite a high-quality course on the basics of machine learning. I don't have much to compare it with, a year ago I took a course from Learn Python, but it was very basic. I didn't know about K/C at that time, otherwise I would have chosen a course on data analytics.

Perhaps we should reward the most active students somehow - for example, give a ticket to Matemarketing, give a big discount on another course or something nice.

I liked the practical orientation. There were enough modern approaches, theory and tasks in the selected topics. I liked the fact that there were analyses of assignments - it really helped. I also liked the interactivity with lecturers and other students. Very useful course, keep it up!

Extremely useful courses on advanced data analysis. I would especially like to highlight the modules on A/B testing and Uplift modelling. The quality of the material and the tasks are at a high level. This course had a global impact on my career. Thanks to the acquired skills, especially the module on advanced A/B testing, 2 months after the end of the course I received an offer for the position of Senior research analyst, although I had not worked in IT companies before.