Python Foundations

The objective is to get a holistic overview of solving a business problem through analytics and to set up the foundations of the skills required to work with data, delivered via the foundations of Python.

Reference Material

Here is a list of important python functions and attributes (arranged alphabetically) from different libraries, along with links to their official documentation:

• Data Visualization — How to Pick the Right Chart Type?

• Explore more with the functionalities of the seaborn library

• An Extensive Step by Step Guide to Exploratory Data Analysis

• When Should You Delete Outliers from a Data Set?

• How to Handle Missing Data with Python

• Selecting the appropriate outlier treatment - Common and advanced approaches for industry applications

• Why 1.5 Is Used in the IQR Rule for Outlier Detection

• List Comprehension, Conditional, and Looping Statements (Quick Code-Python)

• All About Python List Comprehension

• Guidelines for working with external data in Google Colab

• Why Should We Use NumPy?

• The pandas DataFrame: Make Working With Data Delightful

• How to Clean Text for Machine Learning with Python

• A comprehensive guide on effective text cleaning techniques