Самообучение в Data Science и Machine Learning

Уровень 0 · материалов: 5

В кластер входят документы о путях и стратегиях самостоятельного профессионального перехода в области анализа данных и машинного обучения.

Общие признаки: смена карьеры, самостоятельное изучение, технические навыки, бесплатные образовательные ресурсы, переход в сферу данных

Группа выше: Самообразование

Материалы

• #12509 — Self-Teaching Data Science from Scratch to Senior — Представитель этого кластера

  Самообучение в Data science, с нуля до Senior за два года — FFelix

  Смысл: The main idea is that a structured, self-directed approach to learning—focusing on fundamental principles, free high-quality resources, and the combination of math and Python—can rapidly accelerate a career transition into Data Science.

  A guide on efficiently self-teaching Data Science using free resources, focusing on Python, SQL, basic mathematics, and English to move from beginner to senior level.

• #9675 — Free Self-Study Guide for Machine Learning from Scratch

  Вкатываемся в Machine Learning с нуля за ноль рублей: что, где, в какой последовательности изучить — tech_priestess

  Смысл: The main idea is that becoming a Machine Learning professional is possible through a disciplined, self-directed study of mathematics, programming, and specialized ML theory using free high-quality internet resources, provided one is willing to invest significant time and effort.

  A detailed roadmap for learning Machine Learning and Data Science for free, covering everything from foundational math and Python to advanced neural networks and job search strategies.

• #30590 — Becoming a Data Scientist After 40 Without Programming Background

  Как стать датасайнтистом, если тебе за 40 и ты не программист — johnpateha

  Смысл: The main idea is that professional career pivoting into Data Science is possible at any age through a combination of structured online learning, rigorous practical application on platforms like Kaggle, and leveraging existing domain expertise in business.

  An economist turned Kaggle Grandmaster explains how to transition into data science after 40 using Coursera, Kaggle, and business expertise.

• #31733 — Mathematics Learning Plan for Data Science and Machine Learning

  Математика для Data Science и машинного обучения за 8 месяцев. Подробный план обучения — egaoharu_kensei

  Смысл: The main idea is to provide a structured, resource-rich roadmap for individuals to self-study the essential mathematics needed to understand and implement Machine Learning algorithms, transforming a daunting task into a manageable 8-month plan.

  A detailed 8-month study roadmap covering school math, linear algebra, calculus, and statistics, complete with curated textbooks and online courses for aspiring Data Scientists.

• #27466 — Career Transition to Data Science at 37

  4 месяца борьбы за место DS джуна (перекатиться в 37 лет) — artyba

  Смысл: The text illustrates the challenging reality of career switching into a highly competitive technical field like Data Science later in life. The main idea is that theoretical knowledge must be supplemented by practical projects, networking, and resilience in the face of repeated rejection to become a viable candidate.

  A 37-year-old's candid journey of learning Data Science, facing numerous interview failures, and using hackathons and portfolio building to gradually increase their marketability.