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:snake: Data Science Boot-Camp : UC San DiegoX

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[Python for Data Science | Py4-DS](https://www.edx.org/course/python-for-data-science)
[![Python 3](https://img.shields.io/badge/python-3-brightgreen.svg)](https://www.python.org/)
[![Data Science](https://img.shields.io/badge/UCSanDiegoX-data--science-blue)](https://www.edx.org/es/course/python-for-data-science-3)
[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/innat/Py4-DS/gh-pages)
============================================

<p align="justify">
This repo contains resource materials that are part of the Data Science MicroMasters program of UC San DiegoX in edX, which introduces a collection of powerful open-source tools needed to analyze data and to conduct data science. Data science, which is also known as data-driven science, is an interdisciplinary field of scientific methods, processes, algorithms and systems to extract knowledge or insights from data in various forms, either structured or unstructured, similar to data mining. In this course, covering some of the most used python packages in the field of Data Science. A very effective overview on Jupyter notebook , Numerical Python , Matplotlib , Pandas , NLTK , Sci-Kit and lots of projects and famous data sets recorded in kaggle.

## Update 2019
**Tutorials and Exercise** of this course is now separated into **per-weeks**. You can just download the repo and can able start sequentially. You may need to download some files which is larger than allowed file sizes (100MB).

- [week 1](https://nbviewer.jupyter.org/github/innat/Py4-DS/tree/gh-pages/Week%201/): introduction of Data Science 
- [week 2](https://nbviewer.jupyter.org/github/innat/Py4-DS/tree/gh-pages/Week%202/): Basic Python
- [week 3](https://nbviewer.jupyter.org/github/innat/Py4-DS/tree/gh-pages/Week%203/): Introduction of NumPy
- [week 4](https://nbviewer.jupyter.org/github/innat/Py4-DS/tree/gh-pages/Week%204/): Introduction of Pandas
- [week 5](https://nbviewer.jupyter.org/github/innat/Py4-DS/tree/gh-pages/Week%205/): Introduction of Matplotlib
- [week 7](https://nbviewer.jupyter.org/github/innat/Py4-DS/tree/gh-pages/Week%207/): Introduction of Machine Learning
- [week 8](https://nbviewer.jupyter.org/github/innat/Py4-DS/tree/gh-pages/Week%208/): Natural Language Processing

**Note**: Week 6 was only for a Mini-Projects based on Week 1-5.

# Installation
Downlaod [Python 3x](https://www.python.org/) from the official website.

### Local
``` bash
pip install numpy
pip install pandas
pip install pillow
pip install matplotlib
pip install -U scikit-learn
pip install nltk
```
or just download [anaconda distribution](https://www.anaconda.com/download/) to get all the necessary pacakges at a time. You may also need to install [git](https://git-scm.com/) to download the repo as follow and to run:

```
git clone https://github.com/innat/Py4-DS.git
cd Python for Data Science
jupyter notebook
```
Now, go do the desired notebook, files that end with '.ipynb'. To run the file, go to the menu then click on `Cell > Run`.

## Folder structure
Apart from the separated **per-weeks**; The following directory, **Python for Data Science** holds the most important content combinedly. You may prefer in this way if you're confortable with the packages and contents.

## Data Sets
You may also need to donwload additional dataset but here are the most important ones.
* [IMDB Movie-Set](https://grouplens.org/datasets/movielens/)
* [Soccer Data-Set](https://www.kaggle.com/hugomathien/soccer)
* [World Development Indicator](https://www.kaggle.com/worldbank/world-development-indicators)

Worth Watching [The Joy of Stats - BBC Four](https://www.youtube.com/watch?v=jbkSRLYSojo)

## Key Projects
Some interesting project mentioned below.
* [Kaggle | Soccer Data Analysis](https://github.com/innat-2k14/Data-Science-In-Python/tree/gh-pages/Python%20for%20Data%20Science/Soocer%20Data%20Analysis%20%20-%20kaggle)
* [Satellite Image Processing using Numpy package](https://github.com/innat-2k14/Data-Science-In-Python/tree/gh-pages/Python%20for%20Data%20Science/Code%20with%20Numpy/Satellite%20Image%20Processing)
* [World Development Indicators | Data Visualization](https://github.com/innat-2k14/Data-Science-In-Python/tree/gh-pages/Python%20for%20Data%20Science/Code%20with%20Matplotlib/Visualization/World%20Development%20Indicators)
* [Folium Library for Geographic Overlays](https://github.com/innat-2k14/Data-Science-In-Python/tree/gh-pages/Python%20for%20Data%20Science/Code%20with%20Matplotlib/Visualization/Using%20Folium%20Library%20for%20Geographic%20Overlays)
* [European Soccer Regression Analysis using scikit-learn](https://github.com/innat-2k14/Data-Science-In-Python/tree/gh-pages/Python%20for%20Data%20Science/Machine%20Learning%20Approach/European%20Soccer%20Regression%20Analysis%20using%20scikit-learn)
* [Weather Data Clustering using k-Means](https://github.com/innat-2k14/Data-Science-In-Python/tree/gh-pages/Python%20for%20Data%20Science/Machine%20Learning%20Approach/K-Means)
* [Weather Data Classification using Decision Trees](https://github.com/innat-2k14/Data-Science-In-Python/tree/gh-pages/Python%20for%20Data%20Science/Machine%20Learning%20Approach/Weather%20Data%20Classification%20using%20Decision%20Trees)
* [Twitter API for Tweet Analysis](https://github.com/innat-2k14/Data-Science-In-Python/tree/gh-pages/Python%20for%20Data%20Science/NLP-Databases/Using%20the%20Twitter%20API%20for%20Tweet%20Analysis)

## Contact
If there's anything you would like to inform me here or if you're just feeling social, feel free to contact on [quora](https://www.quora.com/profile/Mohammed-Innat#) or reach out on [LinkedIn](https://www.linkedin.com/in/innat2k14/).

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