How to become datsayntistom if you are over 40 and you are not a programmer

    There is an opinion that you can only become a dascientist with a corresponding higher education, and better a degree.

    However, the world is changing, technologies are becoming available for mere mortals. I may surprise someone, but today any business analyst is able to master machine learning technologies and achieve results that compete with professional mathematicians, and perhaps even the best.

    In order not to be unfounded, I will tell you my story - as an economist, I became a data analyst, having obtained the necessary knowledge through online courses and participating in machine learning competitions.

    Now I am a leading analyst in the big data group at QIWI, but three years ago I was quite far from datasynes and heard about artificial intelligence only from the news. But then everything changed, thanks in part to Coursera and Kaggle.

    So, first things first.

    About myself

    I am an economist, I worked as a business consultant for quite a long time. My specialization is the development of a budgeting and reporting methodology for subsequent automation. If in a simple way - this is about first building the process normally, so that the result from automation will be the result.

    3 years ago, at the age of 42, when I felt that I was starting to bronze from consulting success, and began to think about the need for change. About the next career. I already had the experience of how to start a career from scratch (in 30 years I changed the quiet life of an economist for consulting), so changes did not frighten me.

    It does not occur at once, but when you think about it, it becomes obvious that despite the fact that I had already worked for 20 years, there are still about 25 years ahead of retirement (it’s been a long time that it’s necessary to rely on retirement at 70 or even later ). In general, the road ahead is longer than the one that has already passed, and it would be nice to go through it with an actual specialty. So, it was worth learning. At that time I was freelance, and for the sake of the future I reduced the number of projects and was able to allocate enough time to study.

    While I was thinking where to move further, I discovered the Coursera. The Western approach to education, when you first of all explain the meaning, the general idea, and only then the details, turned out to be close to me. Unlike the brutal Soviet education system, which assumes that only decent ones will come up, they give a chance to those like me, who have gaps in basic education.

    I started with business intelligence courses. It was extremely useful for me as a consultant. These same courses helped me to better understand the role of AI-technologies for business development and, most importantly, to see my role in this. This is the same as with other technologies - it is not at all necessary that those who develop new technologies will be the best in their application. For technology to really help a business, it is important to understand this business. Expertise in business processes is no less important than understanding the very technologies of machine learning, big data processing, and so on.

    And I plunged into courses on datasynes, statistics, programming.
    With interruptions, I have mastered more than 30 courses on the Coursera in a year and no longer felt like an alien in the world of big data and machine learning.


    Some courses have recommended Kaggle as an excellent practice site. Do not repeat my mistakes - I came there only when I already felt that I had accumulated enough knowledge. But it was worth doing it half a year earlier, when the first understanding of what and how appeared. I would be steeper for six months. After all, this is not just one of the venues for competitions, it is the best (at present) platform for mastering machine learning in practice, which is useful for both beginners and superguru. And there you grow, as they say, a day in two - only courses without practice will not give such an effect.

    My first competition was a competition.from Santander Bank - prediction of customer satisfaction. I was a novice and wanted to check the level of my knowledge in business. I combined my experience as a bank customer, skills in analyzing business cases and machine learning technology and made a pretty good model with which I climbed into the top 50 in the public leaderbord. It was much higher than my expectations from the first competition, given that more than 5 thousand people participated in it.

    But not everything was so simple. I didn’t earn the happy one then. There is such a common problem among beginners as “retraining a model” that I met in practice. Local validation was poorly organized, I was too focused on public, and as a result - on the closed part of the test, I flew down 500+ positions down. Of course, I was upset, but the lesson was good: good validation is the basis of machine learning, and it needs to be taken seriously. Now this component is one of the strengths of my models.

    Despite the weak first result, there was confidence that getting into the top is real, you need more practice and additional knowledge.

    For those who do not know how good Caggle is, the community is ready to help newcomers by overcoming some gags, discussing ideas, sharing examples of how it works. Well and no less important - at the end of the competition there is an opportunity to study the decisions of the leaders. Learning from one’s experience can make rapid progress. Not necessarily all the rake to attack yourself.

    I can not help but recall the OpenDataseSayns (, the Russian-speaking community of datasientists. The machine learning trainings that ods organizes are another way to get to know the subject more deeply. Well, as a platform for communication on any issues also helps a lot. If you are thinking about your future in datasynes, and you have not registered with ods yet, this is a serious mistake.

    Since the vacancies for datasientists' positions often mentioned the expectations of high results on Kaggle, I saw a chance for myself - besides the fact that I am gaining experience, it is possible to fill in an empty resume with a more or less relevant experience. I began to treat Caggle as a job, where a career start could be a bonus.

    As soon as free time appeared, I built models on Kaggle, and with each competition the result became better.

    I had something that most of the participants didn’t have - the ability to analyze business cases and my consulting experience, this helped a lot in building models. Six months later, I took the 7th place in the next competition from the Santander Bank and earned my first gold medal.

    If you aggressively strive for a specific goal, you will reach it - in June 2017, after a year or so of my battles on Kaggle, we, together with Agnis Lyukis, a developer from Latvia, won a competition from Sberbank to predict apartment prices in Moscow. Our strengths were an understanding of the case (this is a complex task, the solution of which was not worth going to the forehead, as most did) and strong local validation. We finished second in public, but our model almost did not suffer from retraining and didn’t lose much on closed data - in the final we were the first with a giant margin.

    This victory threw me into the top 50 of the global Kaggle ranking, which resulted in job offers. Having studied the options, I chose the bank, as a place where there are many tasks on which you can pump skills, and also feel the whole truth of life when developing models - yet in competitions conditions are more greenhouse.

    I had ambitious plans for career growth and the option “not to rush to work for several years to grow to the next level” was not considered. It was necessary to plow up and at work, and in the second shift not to forget about Kaggle. Not easy, but who is easy now? And it gave results - 3 more gold medals and I earned epaulets of Grandmaster on Kaggle plus was fixed in the global top (now 23rd).

    Like a cherry on a cake - 3 place in bank scoring competitions, what I professionally did last year. And, as you can see, he did well.

    Alas, the truth of life in a bank is also a very conservative and slow decision-making process. The introduction of my models moved slowly. There were no plans to restructure the work of the entire bank, so it was easier, albeit with regret, to change jobs.

    It turned out to be not difficult at all - thanks to the results on Kaggle, the search did not take much time, and for several months I’ve been digging billions of tables in QIWI. We have a lot of interesting tasks , I am sure that pretty soon we will be able to turn our data into a profit for the company - the background of the economist helps a lot with this. Kagglopyop here also appeared in the cashier for several cases.

    And now how to succeed in competitions

    The most important part is to understand the task and find all the drivers that can affect the result. The better you understand the case, the more chances to perform cool. Anyone can generate hundreds or even thousands of statistical features, but they can come up with those that are specifically designed for this task and explain well the target, which is much more complicated. Invest in it, and quickly find yourself in the top. It is necessary to apply any relevant experience (business, household, etc.) - it helps a lot.

    Then - local validation. Your main enemy is retraining, especially if you use such a powerful technology as gradient boosting. I know how psychologically difficult it is to stop focusing on a public leaderboard, but if you don’t want disappointment, the correct answer is to use cross-validation, say “No” to the delayed sample. Of course, there are exceptions, but even in tasks with time series, you can fasten cross-validation, greatly increasing the reliability of the model. Not always the local validation scheme will be simple, but it is worth spending time on it - both in competitions and in real life. The reward will be stable models.

    Of course, you need to study the basic tools well. Knowing the principles of different technologies, you will be able to adequately choose the best tool for solving a specific problem. For tabular data, the current leader is gradient boosting, and specifically Lightgbm. But it is important to be able to use other methods, from logging to neural networks - in life and in competitions will not be superfluous.

    By the way, the best way to understand which technologies rule now, when everything is changing rapidly - to see which libraries are used by competition leaders. In recent years, many worthwhile technologies have broken through the Caggle world.

    Hyperparameters. It is important to know the key hyperparameters of the tools used. Usually not many parameters need to be changed. My belief is that you should not spend a lot of time on the selection of hyperparameters. Of course, it is necessary to find good hyperparameters, but you should not dwell on it.

    Usually, when the model is outlined, I select a more or less stable set of parameters and return to their tuning only towards the end, when other ideas have run out. Common sense dictates that the time spent on creating and testing new variables, libraries, non-standard ideas, can give a much larger increase in the model than the improvement from the transition from a good set of hyper parameters to the ideal one.

    If you make a bet on Kaggle as a feature that will pump your resume - consider this as a job, you will not regret. It helped me, will help you.

    Well, once again about the competition. It is very high here, so it’s very difficult to win alone. Teamwork is very useful, the synergy of ideas allows you to jump above your head. Feel free to use it.


    Well, a little motivation in the end. First of all, I proved to myself that I can become a dancinetist at 44 years old. The recipe turned out to be surprisingly simple - online education, business-oriented thinking, efficiency and purposefulness. Now in every possible way I incite my friends to do the same way. A new digital economy needs (and will need) highly qualified specialists. Coursera + Kaggle - this is just an excellent opportunity to start. Once after all, Excel was a new and incomprehensible tool (I even remember how difficult the first battles with the traditional calculator were). And now, after all, no one has any doubt that a specialist who understands his business can squeeze out of Excel much more real benefits than the Excel developers themselves.

    It will take a little time, and possession of machine learning tools will become as mandatory as owning Excel, so why not prepare for this in advance and win the competition in the labor market right now?

    Moreover, the competition should not be afraid. The more people on the business side will come to datasynes - the more money. The introduction of new technologies in traditional sectors of the economy can accelerate business, and for this, businesses must begin to understand the opportunities that new technologies are opening today. In fact, any business analyst, having mastered several courses, may be at the forefront of progress and help his company outrun conservative competitors.

    I hope my experience will help someone make an important decision.
    If you have any questions about Kaggle, please write, I will gladly answer in the comments.

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