Extremesarova/data_science_resources

Helpful resources for Data Scientists / Machine Learning Engineers

Learning Resources

Table of Contents:

• Learning Resources
  • Interview preparation and knowledge checking
    • Questions
    • Python
    • Behavioural interview
    • Tips
    • Interview Preparation
    • Courses
    • Blog posts
    • Other
  • Software Engineering
    • System Design
  • Data Science
    • Machine Learning System Design
    • Machine Learning
      • Applied ML
      • Blog posts
      • Blogs
      • Tutorials
    • MLOps
    • Deep Learning
      • Tutorials
      • Blog posts
    • NLP
      • Tutorials
      • Blog posts
    • Computer Vision
    • RecSys
    • Metrics
    • Kaggle
    • Not popular, but cool python libraries
    • Assignments for ML & DL
    • Cheatsheets
    • Datasets
    • Football Analytics
    • Other
  • Lists of materials
  • Python
  • Math
  • Statistics
    • A/B Tests
      • Blog posts
  • SQL
  • Algorithms and Data Structures
  • Linux
    • Tutorials
  • Team Lead
  • Books
    • Maching Learning
    • Nonfiction
    • Machine Learning
  • Note-taking system
    • Anki
    • Zettelkasten
  • Newsletters
  • Other
    • Website development

Interview preparation and knowledge checking

Questions

• Machine Learning FAQ by Sebastian Raschka Questions
• Machine Learning FAQ by X Questions
• OVER 100 Data Scientist Interview Questions and Answers by Terence Shin Questions
• Материалы для подготовки по машинному обучению от Тинькофф Rus Questions
• DS & Python questions by Artem Ryblov Questions
• Introduction to Machine Learning Interviews Book by Chip Huyen Book
• Awesome interview questions repository
• Data Science Interviews by Alexey Grigoriev Questions
• Тестовые задания по DS Rus
• Minimum Viable Study Plan for Machine Learning Interviews Repository
• Data-Science-Interview-Questions by Youssef Hosni Repository
• Data-Science-Interview-Preparation-Resources by Youssef Hosni
• A Guide for Machine Learning Technical Interviews (FAANG Companies) Repository
• Coding Interview University Repository
• Data science interview questions with answers Repository
• 120 Data Science Interview Questions Repository
• Вопросы для интервью по специальности Data Science Rus
• Interview Questions from interviewquery.com
• Вопросы с собеседовании по машинному обучению Rus
• Вопросы тестов по курсу «Глубокое обучение» Александр Дьяконов Rus
• Вопросы с собеседования по анализу данных SQL в 2023 году Rus

Python

• Вопросы с собеседований python. Как проходит собеседование Python-разработчика: вопросы для джуниоров и мидлов. Questions Python Rus
• 53 Python Interview Questions and Answers Questions Python
• Вопросы на собеседовании Python, ответы, на которые вам стоит знать Questions Python Rus

Behavioural interview

• 41 Behavioural Interview Questions You Must Know (Best Answers Included)

Tips

• How I Got 15 More Data Science Interviews in One Month?
• Как я проходил собеседования на Machine Learning Engineer Rus
• Google ML engineer interview: the only post you’ll need to read
• Amazon Data Scientist Interview Guide
• The Trimodal Nature of Software Engineering Salaries in the Netherlands and Europe
• Гайд по подготовке CV+Portfolio+Self Presentation+Home task Rus
• Методика STAR для прохождения структурированных собеседований Rus
• Как пройти собеседование на английском языке | StarTalk Rus Video
• Лена Кочева и Таня Дурова - Как эффективно подготовиться к собеседованию на английском Rus Video
• Гостевое выступление Тати Габрусевой, Staff Machine Learning Enjineer, NLP, LinkedIn 25.05.2022 Rus Video
• Полезные ссылки: Как проходить интервью в DS от Айры Rus
• FAANG Interview. Бортовые заметки сообщества Rus
• You should Review These Topics Before Data Science Technical Interview
• Хендбуки Академии Яндекса Rus
• Crack the Amazon Data Scientist Interviews | Ex-FAANG Data Scientist by Dan Lee
• Interview Warmup by Google
• Дайджест уходящего года: релокейт в Европу и США, главное о карьере и сверхзанятости Rus
• How I got in to Amazon, Microsoft, Google. All from studying these resources
• Articles, books and videos to help get well-paying tech jobs by TechPays
• How I Cracked the Meta Machine Learning Engineering Interview
• What we look for in a resume by Chip Huyen
• Не принимай оффер в Data Science, пока… Rus
• Стратегия поиска работы за границей: что писать, с кем говорить и к чему готовиться
• Data Science Interview Guide

Interview Preparation

• Interview Query is the best adaptive learning platform for Data Scientists by Data Scientists
• Как не провалить интервью. Исследование из Стэнфорда о пользе самоуверенности Rus
• На этой странице вы узнаете про Data Science направление в Маркете, наш стек технологий, этапы собеседований и материалы, которые могут пригодиться Rus
• IT-собеседование в Тинькофф
• Материалы для подготовки по машинному обучению от Тинькофф Rus
• Что надо знать сотруднику Цельса? Rus

Courses

• Хочу крутой оффер Rus

Blog posts

• Как я готовился к собеседованию на позицию Senior ML Engineer by Zarin Gleb Rus
• На что обращать внимание на алгоритмических секциях собеседований Rus
• Crack the top 40 machine learning interview questions
• [Карьера в IT] Главное: что нужно знать, чтобы найти работу, пройти собеседование и выбрать оффер Rus
• How I landed 18 FAANG+ software engineer offers after not interviewing for 5 years
• Emma Ding | Data Science Interview Blog | Data Interview Pro

Other

• Data Scientist total compensation and salaries in the Netherlands
• Programmer Competency Matrix
• Just know stuff (or, how to achieve success in a machine learning PhD)

Software Engineering

System Design

• The System Design Primer Repository
• Algorithms you should know before you take system design interviews
• Top 14 System Design interview questions for software engineers
• System Design for Interviews and Beyond Course

Data Science

Machine Learning System Design

• Stanford CS 329S: Machine Learning Systems Design Course
• Designing Machine Learning Systems by Chip Huyen Book
• ML System Design Course Rus
• Шаблон ML System Design Doc от телеграм-канала Reliable ML Repository Rus
  ML System Design - ML System Design Doc. Лекция-бонус от Reliable ML Rus Video
• Machine learning design primer by Ibragim Badertdinov Repository

Machine Learning

• Open Machine Learning Course by Yury Kashnitsky Free course + Paid additional assignments Открытый курс машинного обучения Rus Free course + Paid additional assignments
• StatQuest with Josh Starmer Videos
• Machine Learning Mastery by Jason Brownlee Free guides + Paid eBooks
• End to End Machine Learning by Brandon Rohrer Course
• Machine Learning Simplified: A gentle introduction to supervised learning by Andrew Wolf Free Book
• Stanford CS229: Machine Learning by Andrew Ng Course
• Учебник по машинному обучению от ШАД Яндекс Rus Book
• Машинное обучение (курс лекций, К.В.Воронцов) Rus Course
• Прикладные задачи анализа данных, Александр Дьяконов + Video Rus Course
• Google Machine Learning Courses Course

Applied ML

• Interpretable Machine Learning. A Guide for Making Black Box Models Explainable by Christoph Molnar Book
• Feature Engineering and Selection: A Practical Approach for Predictive Models by Max Kuhn and Kjell Johnson Book
• Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning by Sebastian Raschka Article
• Kaggle Learn Courses
• Geographic Data Science with Python

Blog posts

• Векторное представление товаров Prod2Vec: как мы улучшили матчинг и избавились от кучи эмбеддингов Rus
• Some characteristics of best-in-class ML portfolio projects
• Как метод подмены задачи борется с несовершенством данных (и мира) Rus
• Feature Selection — Exhaustive Overview by Danny Butvinik
• A highly anticipated Time Series Cross-validator is finally here article
• Интерпретация моделей и диагностика сдвига данных: LIME, SHAP и Shapley Flow Rus model explainability
• Machine Learning for Everyone. In simple words. With real-world examples
• Мое первое серебро на Kaggle или как стабилизировать ML модель и подпрыгнуть на 700 мест вверх Rus
• Soccer Analytics 2022 Review football
• Эй-Яй, крипта, MLOps и командный пет-проджект by yorko Rus MLOps
• Understanding UMAP
• A new perspective on Shapley values, part I: Intro to Shapley and SHAP SHAP explainability interpretability
• A new perspective on Shapley values, part II: The Naïve Shapley method SHAP explainability interpretability
• 10 первых ошибок в карьере ML-инженера Rus

Blogs

• Applying Machine Learning by Eugene Yan
• Eugene Yan
  • Data Science Project Quick-Start datascience engineering productivity
  • Stop Taking Regular Notes; Use a Zettelkasten Instead writing learning productivity
  • Writing is Learning: How I Learned an Easier Way to Write writing learning
  • Uncommon Uses of Python in Commonly Used Libraries python engineering
• Andrey Lukyanenko
  • A third life of a personal pet-project for handwritten digit recognition
• Dan Bader
• Matthew Brett
  • Introducing principal component analysis

Tutorials

• CatBoost - An In-Depth Guide tutorial
• Введение в библиотеку Transformers и платформу Hugging Face Rus
• R2D3 is an experiment in expressing statistical thinking with interactive design visualization
• Build a Telegram chatbot with any AI model under the hood

MLOps

• FastAPI for Machine Learning: Live coding an ML web application with the creator of FastAPI Sebastián Ramírez Video
• Build your MLOps stack
• MLOps и production подход к ML исследованиям Course Rus
• ML System Design. Autumn 22/23 Course Rus
• MLOps Guide

Deep Learning

• Dive into Deep Learning
  I prefer going through this book using Amazon SageMaker
• Deep Learning Specialization Paid Course
• Deep Learning with Python by François Chollet Paid Book
• MIT 6.S191 Introduction to Deep Learning Course
• CS25: Transformers United V2 Course
• Full Stack Deep Learning - Course 2022
• 11-785 Introduction to Deep Learning from Carnegie Mellon University Course
• Neuromatch Academy: Deep Learning Course
• Neural Networks: Zero to Hero by Andrej Karpathy
• Efficient Deep Learning Systems by Yandex School of Data Analysis Course Repository

Tutorials

• Introduction to Deep Learning by Sebastian Raschka Course
• Learn PyTorch for Deep Learning: Zero to Mastery book tutorial
• Коллекция ручных задачек о нейросетях Rus Tasks
• A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks by Sebastian Raschka
• Deep Learning Fundamentals by Sebastian Raschka and Lightning AI
• Neural Networks: Zero to Hero by Andrej Karpathy Course
• Deep Learning Tuning Playbook by Google
• A Step by Step Backpropagation Example
• Multimodal Deep Learning Book
• PyTorch Fundamentals by Microsoft

Blog posts

• WHAT IS TORCH.NN REALLY? by Jeremy Howard, fast.ai tutorial
• О «раздутом пузыре» нейросетей Rus

NLP

• Нейронные сети и обработка текста Rus Course
• Stanford CS224N: NLP with Deep Learning + Videos Course
• NLP Course | For You by Lena Voita + YSDA Natural Language Processing course Rus videos + Eng guides Course
• Hugging Face course Course
• Working With Text Data using Sklearn + Text feature extraction using Sklearn Guide
• Recommendations for Getting Started with NLP by Elvis Paid Recommendations
• Natural Language Processing course by Valentin Malykh Course
• Speech and Language Processing by Dan Jurafsky and James H. Martin Book
• Stanford LSA 311: Computational Lexical Semantics by Dan Jurafsky Course
• Stanford CS224U: Natural Language Understanding Course
• Введение в обработку естественного языка Rus Course
• Stanford CS 224V Conversational Virtual Assistants with Deep Learning Course
• Learn to Love Working with Vector Embeddings by Pinecone Course
• Transformer Recipe by Elvis Saravia Repository
• CS11-711 Advanced Natural Language Processing (at Carnegie Mellon University's Language Technology Institute) + Video + Assignments Course
• Stanford Webinar - GPT-3 & Beyond Video

Tutorials

• Train and Fine-Tune Sentence Transformers Models

Blog posts

• Мультиклассификация экстремально коротких текстов классическими методами машинного обучения Rus article
• Рейтинг русскоязычных энкодеров предложений Rus article
• Как определять пользовательские намерения, о которых мы узнали 5 минут назад Rus article
• Самая большая BERT-подобная модель на русском, которая поместится на ваш компьютер
• ChatGPT как инструмент для поиска: решаем основную проблему
• GPT in 60 Lines of NumPy
• What Is ChatGPT Doing … and Why Does It Work?
• From GPT-3 to ChatGPT: Training Language Models on Instructions and Human Feedback Rus Video

Computer Vision

• Нейронные сети и компьютерное зрение Rus Course
• CS231n: Convolutional Neural Networks for Visual Recognition + Videos Course
• EECS 442: Computer Vision + Videos Course

RecSys

• Your first recsys by MTS Rus Course
• Your Second RecSys by MTS Rus Course

Metrics

• Classification metrics (precision, recall, F1 and Matthews correlation coefficient) Twitter thread
• Classification metrics (precision, recall, F1 and Matthews correlation coefficient) vs Balanced Accuracy Twitter thread

Kaggle

• Best Kaggle Competitions for Beginners Competitions
• Roadmap for Beginners Notebook
• The Best Tutorial for Beginners Notebook
• Learning Materials on Kaggle Notebook
• Data Science for tabular data: Advanced Techniques Notebook Tabular
• Feature Selection with Null Importances Notebook Tabular Feature Selection
• The Most Comprehensive List of Kaggle Solutions and Ideas Competitions Solutions
• NLP:
  • Approaching (Almost) Any NLP Problem on Kaggle Notebook
  • Long Text Sentiment Classification BERT Windowing Notebook
• EDA Pipelines:
  • Titanic:
    • EDA To Prediction(DieTanic) Notebook
    • Titanic Top 4% with ensemble modeling Notebook
• Ensembling:
  • Introduction to Ensembling/Stacking in Python Notebook
• Разбор kaggle-соревнования "American Express" в MISIS AI Lab Rus Video

Not popular, but cool python libraries

• Mlxtend (machine learning extensions) is a Python library of useful tools for the day-to-day data science tasks Package ML
• MinT: Minimal Transformer Library and Tutorials Package NLP
• Проект Natasha. Набор качественных открытых инструментов для обработки естественного русского языка (NLP) Package Rus article NLP
• ML | Hydra Package ML
  Framework for managing configuration files, tailored for ML projects
• Lazy Predict Package ML
  Lazy Predict helps build a lot of basic models without much code and helps understand which models works better without any parameter tuning.
• Russian Texts Statistics Package Rus NLP

Assignments for ML & DL

• Stanford CS224n: Natural Language Processing with Deep Learning Assignments
  • Assignment 1: Introduction to word vectors
  • Assignment 2: Derivatives and implementation of word2vec algorithm
  • Assignment 3: Dependency parsing and neural network foundations
  • Assignment 4: Neural Machine Translation with sequence-to-sequence, attention, and subwords
  • Assignment 5: Self-supervised learning and fine-tuning with Transformers
• Stanford CS231n: Convolutional Neural Networks for Visual Recognition & Course notes Assignments
  • Assignment 1: Image Classification, kNN, SVM, Softmax, Fully-Connected Neural Network
  • Assignment 2: Fully-Connected Nets, Batch Normalization, Dropout, Convolutional Nets
  • Assignment 3: Image Captioning with Vanilla RNNs, LSTMs, Transformers, Network Visualization, Generative Adversarial Networks
• Stanford CS229: Machine Learning & Summer version & Assignments from Fall 2018 Assignments

Cheatsheets

• Stanford CS 229 ― Machine Learning Cheatsheet
  • Supervised Learning
  • Unsupervised Learning
  • Deep Learning
  • Machine Learning tips and tricks
• Stanford CS 230 ― Deep Learning Cheatsheet
  • Convolutional Neural Networks
  • Recurrent Neural Networks
  • Deep Learning Tips and Tricks

Datasets

• 24 Useful Open Datasets for Natural Language Processing Datasets

Football Analytics

• Soccer Analytics Handbook Repository

Other

• Courses at Stanford relative to AI
• Путь Лемминга. Про планирование карьеры, выгорание, здоровье, конкуренцию и т.п. Rus Course
• Публичные выступления Rus
• 5 Mindset Strategies For Success And Prosperity

Lists of materials

• NLP Pandect Repository
• Start Career in DS: навигация по постам
• Extra materials for ml-mipt course Repository
• Awesome FastAPI Repository
• Curated papers, articles, and blogs on data science & machine learning in production Repository
• Prompt Engineering Guide Repository
• Open Source Society University. Path to a free self-taught education in Computer Science! Repository

Python

• Python: основы и применение Rus Course
• Программирование на Python Rus Course
• Efficient Python Tricks and Tools for Data Scientists Book Repository

Math

• The Matrix Calculus You Need For Deep Learning
• В Data Science не нужна математика (Почти) Rus

Statistics

• Основы статистики + Основы статистики. Часть 2 + Основы статистики. Часть 3 Rus Course
• Statistics and probability from Khan Academy Course
• CS109: Probability for Computer Scientists + Course Book Course
• Математическая статистика и AB-тестирование Repository
• Стратификация. Как разбиение выборки повышает чувствительность A/B теста Rus article
• 12 бесплатных материалов по статистике – разберется каждый Rus
• Математическая статистика. Начало Rus
• Z Statistics by Justin Zeltzer Videos
• Seeing theory. A visual introduction to probability and statistics Book
• The Beginner's Guide to Statistical Analysis by Scribbr
• Прикладная статистика. Репозиторий для линейки онлайн-курсов по статистике Repository Rus
• Multivariate statistics Course Video

A/B Tests

Blog posts

• Шесть причин, почему ваши A/B-тесты не работают Rus
• АБ-тесты — это не только ценный мех… Но еще и процессы Rus

SQL

• Основы SQL + Продвинутый SQL + Проектирование баз данных Rus Paid Courses
• Интерактивный тренажер по SQL Rus Course
• Онлайн тренажер SQL Academy Course
• Ace the SQL & Data Science Interview
• Practice SQL
• SQL Tutorial
• SQLBolt. Learn SQL with simple, interactive exercises.
• SQL Tutorial by w3schools
• PostgreSQL Exercises

Algorithms and Data Structures

• LeetCode Explore Free + Paid Course
• Algoprog Rus Free + Paid Course
• Алгоритмы: теория и практика. Методы Free Rus Course
• Алгоритмы: теория и практика. Структуры данных Free Rus Course
• Algorithmic concepts By Afshine Amidi and Shervine Amidi
• LEETCODE PATTERNS
  List of questions with patterns + tips
• Тренировки по алгоритмам от Яндекса Rus
• NeetCode. A better way to prepare for coding interviews.

Linux

• Linux commands mind map
• The Art of Command Line

Tutorials

• The Linux command line for beginners tutorial

Team Lead

• Everything on team leadership
• Team Lead Simulator
• Teamlead Roadmap Rus

Books

Maching Learning

• Reliable Machine Learning: Applying SRE Principles to ML in Production by Cathy Chen

Nonfiction

• Atomic Habits
• 9 Books I Read In 2022 That Already Reshaped My Life
• Reading List by Lex Fridman

Machine Learning

• Machine Learning Q and AI. Expand Your Machine Learning & AI Knowledge With 30 In-Depth Questions and Answers by Sebastian Raschka
• Clean Machine Learning Code

Note-taking system

Anki

• How to Remember Anything. A study technique for all ages and all subjects

Zettelkasten

• The FUN and EFFICIENT note-taking system I use in my PhD

Newsletters

• MACHINE LEARNING QUESTIONS
• The Pragmatic Engineer by Gergely Orosz
• Nick Singh's Tech & Careers Newsletter
• Mindful Modeler by Christoph Molnar
• Tuesday Musings by Radek Osmulski
• Newsletter by Emma Ding

Other

• Hamming, "You and Your Research"

Website development

• Crafting My Personal Website: Jekyll, GitHub Pages, and a Custom Domain