This repository combines the files from different Data Science courses and specializations I've taken so far and will be frequently updated.

NOTE: Although you are more than welcome to use the code posted here as guide, I do encourage you to try your own solutions before using it... At the end of the day, making your own mistakes is the best way to learn.

A comprehensive course to learning how to use the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms.

I found this specialization a good starting point if you want to learn how to code for data analysis in Python, however, in my opinion, it lacks a bit of theory in order to get a better understanding about the reasons to use one method or another.

1. Introduction to Data Science in Python
2. [Applied Plotting, Charting & Data Representation in Python (Pending)]
3. [Applied Machine Learning in Python (Pending)]
4. [Applied Text Mining in Python (Pending)]
5. [Applied Social Network Analysis in Python (Pending)]

This specialization will give you a good theory foundations to analyze data science projects, however I would suggest to take the UMichigan specialization first to have better programming skills before getting into deep with this specialization.

1. Python for Data Science
2. [Statistics and Probability in Data Science using Python (Pending)]
3. [Machine Learning for Data Science (Pending)]
4. [Big Data Analytics Using Spark (Pending)]

An introduction to computer science as a tool to solve real-world analytical problems using Python 3.5.

Camilo Cruz, 2018.