Habr advise: how to win the NTI Olympiad for schoolchildren in the Big Data profile


    When I took exams at the university, I always sat down to prepare for the desk closest to the teacher and never went first, but let 8-10 people go ahead. Thus, I managed to “eavesdrop” on additional questions and, in part, answers to them.

    And then I had an idea how to make tips on the Olympics for those high school students who read Habr (which suggests that they really have advanced brains). I found those guys who showed excellent results at the previous NTI Olympiads and asked them what additional materials could help to understand the topic better and give additional benefits at the final practical stage.

    Applications for the Olympiad are received until October 22 , and there is still time to prepare.

    Briefly about the NTI Olympiad

    A group of students looked at what was going on with education and decided to make an effort to fix it. What is happening? And the fact that the parents of many schoolchildren do not allow building a drone in the garage or going to courses on building satellites, "because you need to pass the exam." So, our initiative comrades decided to create a movement that talented schoolchildren will be able to get points for the exam and enter universities not with the help of tests, but by working with their hands and head. And now the schoolchildren have an excuse why he pushed electrodes into the brain of a cat - “I'm getting ready for the Olympics”.

    I think this is important and worthy. And I respect the olympiads.

    My reports on the 2016 Olympics

    The organizers of the olympiad themselves here is what they write about the requirements for the knowledge and skills of participants:

    • understanding of basic concepts in the field of machine learning and neural networks;
    • programming in languages ​​that combine development speed with the ability to use modern tools for working with data (for example Python);
    • the ability to quickly find solutions, understand them and adapt to your needs on the Internet on specialized resources (stackexchange, etc.);
    • the ability to work effectively with big data, write algorithms efficiently; ability to conduct testing, debugging;

    And here are the recommended “official training materials”:

    How does the Big Data Olympiad take place:


    During the final stage, the finalists work to identify the given information from the data array, for this the participants need to write their program using a set of texts for training. The workplace is a desk, a laptop connected to the Internet.


    The team works in a “everyone behind their laptop” mode, from time to time discussing something with each other. To check the tasks, the facilitator starts the program of the participating team with a set of random texts and evaluates the result of the data analysis.

    Tips "experienced"

    Anonymus1111 advises.

    To drag specifically the last stage in the "typewriter", then something like this:

    But in order to understand what a man says:

    Anonymousus2222 advises.

    General recommendations:

    • start not with neural networks, but with simpler methods
    • play with real data
    • use python in jupyter notebook

    Specific Resources:

    Anonymousus3333 recommends.

    About the neurons.

    About tensorflow.

    A simple math course on machine learning with vectors, terver and basic algorithms.

    Advice from me personally.
    We must try our best to be friends with the GoTo project , because there are quality courses and teachers who are always ready to answer questions and hang out with students in Yandex on weekends. There are grants for the trickiest . In extreme cases, you can join a group in VK and ask everyone for advice.

    What would you advise high school students to improve their skill in Big Data?

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    How many high school students are reading Habr?

    • 16% I am in grade 7 4
    • 4% I am in 8th grade 1
    • 4% I am in grade 9 1
    • 32% I am in grade 10 8
    • 44% I am in grade 11 11

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