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.

**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: introduction of Data Science
- week 2: Basic Python
- week 3: Introduction of NumPy
- week 4: Introduction of Pandas
- week 5: Introduction of Matplotlib
- week 7: Introduction of Machine Learning
- week 8: Natural Language Processing

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

Downlaod Python 3x from the official website.

```
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 to get all the necessary pacakges at a time. You may also need to install git 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`

.

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.

```
|-Python for Data Science
| |-Code with Matplotlib
| | |-Matplotlib_Exercise
| | |-Visualization
| |-Code with Numpy
| | |-Numerical Exercises
| | |-Numerical Python
| | |-Satellite Image Processing
| |-Code with Pandas
| | |-Exercise with Pandas
| | |-Pandas in Data Science
| |-Machine Learning Approach
| | |-European Soccer Regression Analysis using scikit-learn
| | |-K-Means
| | |-Weather Data Classification using Decision Trees
| |-NLP-Databases
| | |-Movie Reviews Using NLTK
| | |-Twitter API for Tweet Analysis
| | |-Working with Databases
| |-Planck
| |-Python Word Count
| |-Soocer Data Analysis - kaggle
```

You may also need to donwload additional dataset but here are the most important ones.

Worth Watching The Joy of Stats - BBC Four

Some interesting project mentioned below.

- Kaggle | Soccer Data Analysis
- Satellite Image Processing using Numpy package
- World Development Indicators | Data Visualization
- Folium Library for Geographic Overlays
- European Soccer Regression Analysis using scikit-learn
- Weather Data Clustering using k-Means
- Weather Data Classification using Decision Trees
- Twitter API for Tweet Analysis

If there's anything you would like to inform me here or if you're just feeling social, feel free to contact on quora or reach out on LinkedIn.

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Jupyter Notebook (241,797)

Python3 (33,362)

Database (9,748)

Data Science (9,195)

Data Visualization (4,512)

Pandas (3,936)

Numpy (3,496)

Machine Learning Algorithms (2,401)

Matplotlib (2,244)

Twitter Api (1,408)

Nltk (801)

Kmeans Clustering (665)

Decision Tree Classifier (207)

Scikitlearn Machine Learning (161)

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