Data-Science-For-Beginners-from-scratch-course

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Data-Science-For-Beginners-from-scratch-course

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Data science for beginners involves learning to extract insights from data using statistics, programming (Python/R), and visualization. Key steps include data collection, cleaning, analysis, modeling, and communicating findings. Beginners should start with Python, basic math (linear algebra/calculus), and build projects to create a portfolio.

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# Улучшения репозитория
- Улучшен [ишьюс ревью](https://github.com/SENATOROVAI/intro/issues/5), [обновлены пункты 1.7, 3.2](https://github.com/SENATOROVAI/intro/issues/13), спасибо [rizespbya](https://github.com/rizespbya)
- Улучшен [ишьюс ревью](https://github.com/SENATOROVAI/intro/issues/5), [обновлены пункты 5.5-5.6](https://github.com/SENATOROVAI/intro/issues/12), спасибо [lessWronger](https://github.com/lessWronger) 
- Синхронизирован линтер pre-commit,  [пул реквест](https://github.com/SENATOROVAI/Data-Science-For-Beginners-from-scratch-SENATOROV/pull/474), спасибо [rizespbya](https://github.com/rizespbya)
- Улучшен [интро](https://github.com/SENATOROVAI/intro/blob/main/README.md),[обновлена структура](https://github.com/SENATOROVAI/intro/issues/10), спасибо [savemysoul382](https://github.com/savemysoul382)
- Улучшен [ишьюс git](https://github.com/SENATOROVAI/intro-cs/issues/1), [обновлена структура](https://github.com/SENATOROVAI/intro-cs/issues/15), спасибо [savemysoul382](https://github.com/savemysoul382)
- [Тестирование функционала](https://github.com/SENATOROVAI/intro/issues/11), спасибо [smrnvdn](https://github.com/smrnvdn)
- Улучшено описание [3 группы](https://github.com/SENATOROVAI/Math-Data-Analysis/issues/1), добавлены этапы пайплана, спасибо [savemysoul382](https://github.com/savemysoul382)
- Исправлена опечатка **where** [ишьюс Cpython](https://github.com/SENATOROVAI/intro-cs/issues/16), спасибо [SERG-1613](https://github.com/SERG-1613)
- Обновлён pre-commit **codespell** [ишьюс](https://github.com/SENATOROVAI/Data-Science-For-Beginners-from-scratch-SENATOROV/issues/551), [pull](https://github.com/SENATOROVAI/Data-Science-For-Beginners-from-scratch-SENATOROV/pull/552), спасибо [gorop51-2](https://github.com/gorop51-2)
- Идея создания новой ветки при сдаче ДЗ, [ишьюс](https://github.com/SENATOROVAI/Data-Science-For-Beginners-from-scratch-SENATOROV/issues/545), спасибо [dshilin](https://github.com/dshilin)
- Реализация создания новой ветки при сдаче ДЗ, [ишьюс](https://github.com/SENATOROVAI/Data-Science-For-Beginners-from-scratch-SENATOROV/issues/557), спасибо [SERG-1613](https://github.com/SERG-1613)
- Убрать из ишьюс python ссылку на инструкцию ревью, [ишьюс](https://github.com/SENATOROVAI/intro/issues/17), спасибо [stokato](https://github.com/stokato)
- Улучшен [ишьюс Виртуальное окружение](https://github.com/SENATOROVAI/intro-cs/issues/7), [обновлено описание и добавлено видео](https://github.com/user-attachments/assets/eda6f086-4804-4dfa-80d7-172c301791a4), спасибо [solik220-bit](https://github.com/solik220-bit)

# Улучшения курсов
- Улучшил описание статьи, дельта Х [простая регрессия](https://stepik.org/lesson/1734634/step/3?unit=1758544), [пулл](https://github.com/SENATOROVAI/Normal-equations-scalar-form-solver-simple-linear-regression-course/pull/14), спасибо [Toleranzy](https://github.com/Toleranzy)
- Добавлен скрин с дискуссиями в регламент взаимодействия в курсах, спасибо [iVenok](https://github.com/iVenok) 
- Опечатка в курсе по [множественной регрессии](https://stepik.org/lesson/1792642/step/10?auth=login&unit=1818331), некорректная формулировка, спасибо [rizespbya](https://github.com/rizespbya)
- неправильно раскрывается (X@θ).T [множественной регрессии](https://stepik.org/lesson/1792813/step/1?discussion=12716622&unit=1818503), [ишьюс](https://github.com/SENATOROVAI/multy-linear-regression-stepik/issues/1), спасибо [rizespbya](https://github.com/rizespbya)
- Формула обратной матрицы [множественная регрессия](https://stepik.org/lesson/1792746/step/1?unit=1818436), [ишьюс](https://github.com/SENATOROVAI/multy-linear-regression-stepik/issues/2), спасибо [rizespbya](https://github.com/rizespbya)
- Улучшение курса [множественной регрессии](https://stepik.org/lesson/1792746/step/1?unit=1818436), внедрение хэлперов и курса академ. математики, спасибо [rizespbya](https://github.com/rizespbya)
- Улучшение курса [простая регрессия](https://stepik.org/lesson/1734634/step/3?unit=1758544), внедрение лупы и доп. заданий python, спасибо  [J4zzyflavor](https://github.com/J4zzyflavor) доп. акк  [Jazzyflavor](https://github.com/Jazzyflavor)

- Улучшение курса [множественная регрессия](https://stepik.org/lesson/1792608/step/10?unit=1818297), внедрение Data Leakage, спасибо [smrnvdn](https://github.com/smrnvdn)
- Улучшение курса [множественной регрессии](https://stepik.org/lesson/1792810/step/2?auth=login&unit=1818500), внедрение логической связи вывода матричной формы SSE, спасибо [rizespbya](https://github.com/rizespbya)
- Улучшение курса, [множественной регрессии](https://stepik.org/lesson/1792848/step/2?unit=1818537), внедрён аудиопересказ, спасибо [Ekubbo](https://github.com/Ekubbo) 
- Улучшение курса [SVD](https://stepik.org/lesson/1863889/step/1?unit=1889025), исправлена опечатка подстановки, спасибо [rizespbya](https://github.com/rizespbya)
- Нет V^T в итоговой записи и у 2 айгенвелью 3 компонента без "-" [SVD](https://stepik.org/lesson/1832067/step/1?unit=1857101),[ишьюс](https://github.com/SENATOROVAI/svd-stepik/issues/1), спасибо [rizespbya](https://github.com/rizespbya)
- Неправильно посчитан вектор u1 [SVD](https://stepik.org/lesson/1863989/step/2?unit=1889127),[ишьюс](https://github.com/SENATOROVAI/svd-stepik/issues/2), спасибо [Svetankova](https://github.com/Svetankova)
- Матрица сигма не mxn [SVD](https://stepik.org/lesson/1865202/step/1?auth=login&unit=1890345),[ишьюс](https://github.com/SENATOROVAI/svd-stepik/issues/3), спасибо [Svetankova](https://github.com/Svetankova)
- Уточнение в задании response:numpy, pandas, , python-dateutil, pytz, six, tzdata в курсе по [линтерам](https://www.udemy.com/course/python-opensource-git/learn/quiz/7695835#reviews), спасибо [AmadeyML](https://github.com/AmadeyM111)
- Опечатка "автоматически" в курсе по [линтерам](https://stepik.org/lesson/1711770/step/13?unit=1735194), спасибо [dshilin](https://github.com/dshilin)
- Обновление названия репозитория в курсе по [линтерам](https://stepik.org/lesson/1711770/step/13?unit=1735194), [ишьюс](https://github.com/SENATOROVAI/python-open-source-standards-course/issues/15), спасибо [iVenok](https://github.com/iVenok)
- Внедрение контейнера UBUNTU в курс по [линтерам](https://stepik.org/lesson/1719986/step/1?unit=1743519), [ишьюс](https://github.com/SENATOROVAI/python-from-zero-to-open-source-stepik/issues/3), спасибо [stokato](https://github.com/stokato/)
- Не рабочий сервис в курсе [линтеры](https://stepik.org/lesson/1719986/step/1?unit=1743519), [ишьюс](https://github.com/SENATOROVAI/python-from-zero-to-open-source-stepik/issues/4), , спасибо [dshilin](https://github.com/dshilin)
- Нерабочая ссылка [Холецкий](https://stepik.org/lesson/1886776/step/3?unit=1912268), [ишьюс](https://github.com/SENATOROVAI/cholesky-stepik/issues/2), спасибо  [J4zzyflavor](https://github.com/J4zzyflavor) доп. акк  [Jazzyflavor](https://github.com/Jazzyflavor)
- 10:15 добавил описание для тэта нулевое θ0 [Холецкий](https://stepik.org/lesson/1886772/step/1?unit=1912264),[ишьюс](https://github.com/SENATOROVAI/cholesky-stepik/issues/4), спасибо [rizespbya](https://github.com/rizespbya)
- Дублирование заданий [Холецкий](https://stepik.org/lesson/1886776/step/3?unit=1912268), [ишьюс](https://github.com/SENATOROVAI/cholesky-stepik/issues/3), спасибо  [J4zzyflavor](https://github.com/J4zzyflavor) доп. акк  [Jazzyflavor](https://github.com/Jazzyflavor)
- Ошибка в описании задания [Градиентный спуск](https://stepik.org/lesson/1829058/step/2?unit=1855081), [ишьюс](https://github.com/SENATOROVAI/multy-linear-regression-gradient-descent-stepik/issues/1), спасибо  [J4zzyflavor](https://github.com/J4zzyflavor) доп. акк  [Jazzyflavor](https://github.com/Jazzyflavor)
- Ошибка в выводе задания [Холецкий](https://stepik.org/lesson/1886772/step/4?unit=1912264), [ишьюс](https://github.com/SENATOROVAI/cholesky-stepik/issues/1), спасибо  [J4zzyflavor](https://github.com/J4zzyflavor) доп. акк  [Jazzyflavor](https://github.com/Jazzyflavor)
- Опечатка в слове "константа", курс [GD](https://stepik.org/lesson/1829077/step/13?unit=1855099), [ишьюс](https://github.com/SENATOROVAI/multy-linear-regression-gradient-descent-stepik/issues/2), спасибо [Lookmir](https://github.com/Lookmir)
- Опечатка в слове "ортогонализации", курс [QR](https://stepik.org/lesson/1880356/step/3?unit=1905773), [ишьюс](https://github.com/SENATOROVAI/qr-stepik/issues/1), спасибо [svetlana-s88](https://github.com/svetlana-s88)
- Старая ссылка на репозиторий в курсе по [линтерам](https://stepik.org/lesson/1711926/step/6?unit=1735359), спасибо [solik220-bit](https://github.com/solik220-bit)

# Улучшение организации
- Обновление интро,внедрение раздела о нас, договора, средства коммуникации, спасибо [ViktorVinogradov89](https://github.com/ViktorVinogradov89)
- Структурированна информация об организации, [ишьюс](https://github.com/SENATOROVAI/Data-Science-For-Beginners-from-scratch-SENATOROV/issues/547), спасибо [svetlana-s88](https://github.com/svetlana-s88)

---

# Data Science For Beginners 🚀

> Beginner-friendly course and practical materials for learning Data Science from scratch with Python, Machine Learning, and Mathematics.

## 📌 About This Repository

This repository contains structured materials, exercises, and practical examples for learning **Data Science from beginner to intermediate level**.

You will learn:

- Python for Data Science
- NumPy & Pandas
- Data Visualization
- Statistics for Data Science
- Machine Learning Basics
- Supervised & Unsupervised Learning
- Regression & Classification
- Optimization Algorithms
- Gradient Descent
- Linear Models
- Regularization (L1 / L2)
- Model Evaluation
- Practical ML Projects

---

## 🎯 Who Is This For?

✅ Beginners in Data Science  
✅ Python developers who want to learn ML  
✅ Students learning Machine Learning  
✅ Developers moving into AI / Data Analytics

---

## 🛠 Technologies Used

- Python 🐍
- NumPy
- Pandas
- Matplotlib
- Seaborn
- Scikit-Learn
- Jupyter Notebook
- Machine Learning Algorithms

---

## 📂 Repository Structure

```

Data-Science-For-Beginners/
│
├── math/
├── statistics/
├── python/
├── data_analysis/
├── machine_learning/
│   ├── regression/
│   ├── classification/
│   ├── optimization/
│
├── projects/
└── notebooks/

```

---

## 📈 Topics Covered

### 🔵 Python for Data Science
- Data types
- Functions
- OOP basics
- Working with files

### 🔵 Data Analysis
- Data cleaning
- Feature engineering
- Exploratory Data Analysis (EDA)

### 🔵 Statistics
- Probability
- Distributions
- Hypothesis testing
- Confidence intervals

### 🔵 Machine Learning
- Linear Regression
- Logistic Regression
- Gradient Descent
- L1 & L2 Regularization
- Decision Trees
- KNN
- Model evaluation metrics

---

## 🚀 Practical Projects

You will build:

- House price prediction model
- Classification model
- Data analysis project
- Real dataset experiments

---

## 🔎 SEO Keywords (Optimized for Search)

Data Science course  
Data Science for beginners  
Machine Learning Python  
ML from scratch  
Data Analysis Python  
Statistics for Machine Learning  
Python Machine Learning projects  
Gradient Descent implementation  
Linear Regression from scratch

---

## ⭐ Why This Repository?

This repository is designed for:

- Deep understanding of algorithms
- Practical implementation
- Mathematical foundation
- Production-ready mindset

---

## 📬 Contact

#### https://SenatorovAI.com

---

⭐ If this project helps you — give it a star!

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