AppliedStats

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A repo with homeworks and labs from a course on applied stats taken by me during my bachelor's degree in MIPT, Ru. Course authors: Andrii Hraboviy, @andriygav and Oleg Bakhteev, @bahleg.

AppliedStats 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# 🚀 AppliedStats
Here I gather my homeworks on Applied Statistics. Later I will add my hw's from Avito Analytics Academy.

**Feel free to 🌟 the repo!**

![AppliedStats logo](https://github.com/yk4r2/AppliedStats/blob/main/pics/logo.jpg "applied stats pic")
## 🛠 Requirements

You should have a `Scikit-learn` package preinstalled. Also `pandas` is needed. You should launch all the notebooks using `jupyter notebook`.
```bash
pip install numpy scipy pandas matplotlib ipython notebook
```
Other packages install-scripts are in front of the notebooks.

## ⏳ Quick Start
Just install `jupyter notebook` and have fun.

## 🗺 Roadmap
Here there will be two main folders: [MIPT applied stats folder](https://github.com/yk4r2/AppliedStats/tree/main/MIPT/homeworks "MIPT") and AVITO sometimes.

In the [MIPT folder](https://github.com/yk4r2/AppliedStats/tree/main/MIPT/homeworks "here") you can find my homeworks and labs:

Homeworks:
* [HW1](https://github.com/yk4r2/AppliedStats/tree/main/MIPT/homeworks/HW1) — basic EDA methods;
* [HW2](https://github.com/yk4r2/AppliedStats/tree/main/MIPT/homeworks/HW2) — parametric hypothesis tests;
* [HW3](https://github.com/yk4r2/AppliedStats/tree/main/MIPT/homeworks/HW3) — non-parametric hypothesis tests;
* [HW4](https://github.com/yk4r2/AppliedStats/tree/main/MIPT/homeworks/HW4) — multiple hypothesis testing;
* [HW5](https://github.com/yk4r2/AppliedStats/tree/main/MIPT/homeworks/HW5) — correlation analysis;
* [HW6](https://github.com/yk4r2/AppliedStats/tree/main/MIPT/homeworks/HW6) — dispersion analysis;
* [HW7](https://github.com/yk4r2/AppliedStats/tree/main/MIPT/homeworks/HW7) — regression analysis;
* [HW8](https://github.com/yk4r2/AppliedStats/tree/main/MIPT/homeworks/HW8) — generalized regression;
* [HW9](https://github.com/yk4r2/AppliedStats/tree/main/MIPT/homeworks/HW9) — time series analysis;
* [HW10](https://github.com/yk4r2/AppliedStats/tree/main/MIPT/homeworks/HW10) — sequential analysis;
* [HW12](https://github.com/yk4r2/AppliedStats/tree/main/MIPT/homeworks/HW12) — Markov models;

Labs:
* [Lab1](https://github.com/yk4r2/AppliedStats/blob/main/MIPT/labs/lab1) — first lab;
* [Parsed list of lab1 emails](https://github.com/yk4r2/AppliedStats/tree/main/MIPT/labs/lab1/github_parser);
* [Lab2](https://github.com/yk4r2/AppliedStats/blob/main/MIPT/labs/lab2) — first lab;
* [Parsed list of lab2 emails](https://github.com/yk4r2/AppliedStats/tree/main/MIPT/labs/lab2/github_parser);

### 🛤 Progress
Homeworks:\
![Homeworks](https://progress-bar.dev/11/?scale=12&width=200&suffix=)

Labs:\
![Labs](https://progress-bar.dev/2/?scale=2&width=200&suffix=)

### 🏁 Finally
Total grade:\
![T0t4l gr4d3](https://progress-bar.dev/8/?scale=10&width=200&suffix=)

AppliedStats

AppliedStats 최신버젼 다운로드

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AppliedStats

AppliedStats 최신버젼 다운로드

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