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С чего начать обучение Data Science?

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Python, основные темы, курсы и полезные каналы

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Нужно ли сильно погружаться в Python?

1:12

Полезные ресурсы по Python

1:27

Математический анализ

1:38

Линейная алгебра

1:55

Методы оптимизации

2:03

Теория вероятности

2:05

Статистика + классная книга по этому предмету

2:32

Методы машинного обучения

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Полезная библиотека scikit-learn + их сайт с туториалами

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Чужой код на Github

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Практика на Kaggle / Pet-project

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Github подробнее

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Сколько в день нужно учиться?

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Полная программа обучения на сайте PyMagic

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Как понять, когда закончится обучение Data Science?

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Полезные видео по матану, статистике и линалу

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ityoutubersru

00:00:02

learning yes, this is science and how to learn it

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from scratch in 2020, even if you have no

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programming experience,

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I will share with you a training plan and

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also at the end of this video I will tell you about the

00:00:13

tricks that will allow you to quickly and

00:00:15

effectively dive into the field and

00:00:18

of course, find your first job,

00:00:20

hello everyone, my name is Nikulina Anastasia for

00:00:22

more than four years I have been working in yes, this is a

00:00:23

union and I also teach this direction of the

00:00:26

learning machine is quite a huge

00:00:28

area, so it’s worth breaking your

00:00:31

learning path into several components 1 thing you

00:00:34

should start is, of course, learning

00:00:35

python why not from statistics or

00:00:38

another subject because then you

00:00:40

will solve problems in these

00:00:43

areas using Python,

00:00:45

first you need the basics, this is syntax,

00:00:47

data structure, loops, conditions, how to

00:00:51

call a variable, and

00:00:53

object-oriented

00:00:54

programming, you don’t need to

00:00:56

go deep into it at this stage the language itself, for example,

00:00:58

study decorators or tests, I would

00:01:01

recommend leaving this for last,

00:01:04

after you go through

00:01:07

machine learning models, you can watch and

00:01:09

study, look towards

00:01:11

Timofey Kiryanov’s channel on YouTube, especially

00:01:14

if you come from the humanitarian field,

00:01:16

you can also try taking an excellent

00:01:19

course from Google on the cursor where you can

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also hone your knowledge on the final project,

00:01:25

then this is the theoretical basis of

00:01:28

mathematical analysis there is an excellent

00:01:31

channel, I think many people know it, but what’s also

00:01:35

cool is it is in Russian, in

00:01:38

addition to mathematical analysis,

00:01:40

you can also watch various videos

00:01:43

on linear algebra additionally below I’ll

00:01:45

leave you a link to a great guide about

00:01:48

linear algebra, there’s also an excellent

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course that I just say everywhere from

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Yandex, this photo and on the cursor, in addition to the

00:01:55

first two topics, there you’ll also find lessons

00:01:58

on optimization methods that are

00:02:00

necessary in data science, as well as the

00:02:02

theory of probability and statistics, I

00:02:05

recommend in particular, read the book

00:02:07

statistics and cats there abyss and quite

00:02:10

important things in simple language plus there are

00:02:13

illustrations that help

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you understand the topic more by the way, material with theory

00:02:19

and practice on mathematical analysis

00:02:21

linear algebra and statistics you can

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find on my channel they last only 15

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minutes this will allow you to refresh knowledge and

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understand the essence at the first stage and the

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next point is the basic algorithms of

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machine learning, this is a mandatory

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point even if you want to study deeply, do

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you need to analyze in detail how

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linear models work, decision trees

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and in composition, clustering methods,

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what input parameters they receive

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if you study for book

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or video courses, I advise you not to neglect

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the documentation for various libraries

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that will help you simplify training

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models, for example, one of the

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popular libraries like kettler on

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their website contains most of the

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information, but the only negative is of course

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it is in English, they have it as a

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theory for all the algorithms and examples

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immediately in python + an excellent course for

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riot courses at the Higher School of Economics, there are the

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main points that

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will give you a good basis and I want to touch on

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the main point: this is practice, this is the

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main guarantee of your success in learning,

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this is homework, this is just

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rewriting and further analysis of the code

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from a book or video, all this

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must be done. It’s important to pay attention

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to other people’s code on git hop, where sometimes

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developers implement this or that

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algorithm straight from scratch, try to

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gradually go through the code if you

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don’t know or don’t understand something.

00:04:01

be sure to google google again

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google the information you are interested in

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questions for example why in this step

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we transpose the matrix and here

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we take some random values

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after passing these points you

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need to start practicing on

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someone like your father make your

00:04:22

own project on these topics I

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also have videos on the channel with examples

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of what you can do, how in this

00:04:30

case it will be easier, how you don’t

00:04:32

have to prepare your datasette, and it’s

00:04:35

when you start putting your

00:04:37

knowledge into practice in a specific project that

00:04:40

you gain experience, which is then

00:04:42

consolidated in you head and at the level of

00:04:46

motor skills, another very important point at

00:04:49

this point is that your time should be

00:04:51

divided approximately 50 to 50

00:04:54

where you do your own projects and

00:04:56

study the code of other developers to get

00:04:58

up this is a useful practice so you can

00:05:01

learn new tricks and apply it

00:05:04

in the future, do not hesitate

00:05:06

to post your project to many people, but

00:05:08

try to keep it clean and

00:05:11

readable, if you collect at least a

00:05:13

couple of such repositories, then this will already be a

00:05:16

great plus when applying for a job,

00:05:18

since it is important for employers to understand what

00:05:21

you can do; I also advise you

00:05:23

to pay at least 1 up to 3 hours a week for

00:05:26

studying, or at least every other day if you

00:05:28

study less than an hour a day, which is quite

00:05:31

little, you will then spend too much

00:05:33

time catching up with what you already

00:05:37

did the previous time, but because this way

00:05:40

you can forget some points if,

00:05:43

on the contrary, you overdo it then there is a high

00:05:45

probability of burnout from the abundance of

00:05:48

information, if you need more

00:05:49

detailed information, what topics you need to

00:05:52

study, you can go to my website, they are

00:05:55

described in quite detail on the topic for the

00:05:58

part and with mathematics for

00:06:00

machine learning algorithms and for

00:06:01

programming, which is also very important, the

00:06:04

process of studying machine learning is not

00:06:06

finite and I’m still here after four

00:06:10

successes in your training and conquering

00:06:12

new heights

Description:

Курс по Data Science от экспертов из области https://pymagic.ru// Как изучить Data Science в 2022 году? С чего стоит начинать и в какой последовательности необходимо изучать материал? В видео разобраны основные темы, которые являются базисными, а также полезные ресурсы, где вы можете найти материал. Курс по Data Science https://stepik.org/course/125145/promo Новая группа про Data Science ВКонтакте https://vk.com/pymagic Таймкоды: 00:00 С чего начать обучение Data Science? 00:33 Python, основные темы, курсы и полезные каналы 00:58 Нужно ли сильно погружаться в Python? 01:12 Полезные ресурсы по Python 01:27 Математический анализ 01:38 Линейная алгебра 01:55 Методы оптимизации 02:03 Теория вероятности 02:05 Статистика + классная книга по этому предмету 02:32 Методы машинного обучения 03:39 Полезная библиотека scikit-learn + их сайт с туториалами 03:48 Чужой код на Github 04:15 Практика на Kaggle / Pet-project 05:05 Github подробнее 05:25 Сколько в день нужно учиться? 05:49 Полная программа обучения на сайте PyMagic 06:04 Как понять, когда закончится обучение Data Science? 06:10 Полезные видео по матану, статистике и линалу Курс по Data Science с нуля https://pymagic.ru Курс по Python c нуля https://pymagic-courses.ru/ 1. Канал Тимофея Хирьянова https://www.youtube.com/c/%D0%A2%D0%B8%D0%BC%D0%BE%D1%84%D0%B5%D0%B9%D0%A5%D0%B8%D1%80%D1%8C%D1%8F%D0%BD%D0%BE%D0%B2 2. Курс по Python от Google https://www.coursera.org/learn/python-crash-course 3. Канал 3Blue1Brown https://www.youtube.com/channel/UC6hAYNOWMmuqOBvFOuAFKwA/playlists 4. Гайд по линейной алгебре https://www.analyticsvidhya.com/blog/2017/05/comprehensive-guide-to-linear-algebra/ 5. Программа обучения на моем сайте, по которой вы можете ориентироваться, какие темы необходимо изучать https://pymagic.ru/#programma 6. Книга по Data Science – «Data Science наука данных с нуля» Джоэла Граса 7. Курс на Coursera от Яндекса и МФТИ https://www.coursera.org/learn/mathematics-and-python 8. Вводные примеры ноутбука от одного из разработчиков Scikit-learn https://github.com/amueller/introduction_to_ml_with_python 9. Библиотека scikit-learn https://scikit-learn.org/stable/ 10. Курс на Coursera от Высшей школы экономики https://www.coursera.org/learn/vvedenie-mashinnoe-obuchenie#syllabus 11. Kaggle www.kaggle.com Instagram* https://www.facebook.com/unsupportedbrowser Группы в ВКонтакте https://vk.com/pymagic Telegram https://t.me/pymagic *Компания Meta - организация, деятельность которой запрещена на территории Российской Федерации

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