Part 1 - Introduction :

Data Analysis is the process of inspecting, cleansing, transforming, and model data with the goal of discovering useful information, informing conclusions, and supporting decision-making.

Python and Julia are the main programming languages used to do data analysis. These are programming languages which means they have a steep learning curve, however, they are extremely powerful. On the other hand, there are the auto-managed closed tools, which are closed source expensive and limited, though easy to learn.

The process in data analysis are as follow :

1. Data Extraction
2. Data Cleaning
3. Data Wrangling
4. Analysis
5. Action

The libraries we use:

• pandas : cornerstone of Data Analysis in Python
• matplotlib : foundational library for visualization
• numpy : numeric library fundation of calculations in python
• seaborn (statistical visualization tool built on top of matplotlib)
• statsmodels : A library with many statistical functions
• scipy : Advanced scientific computind
• scikit-learn : most popular machine learning library for Python