Python

Python

Session 01: Programming for Data Analysts

Why Programming for Data Analysts?

Before starting the session, first lets try to install and configure the Python.

Session 02: Python basic Syntax, Data Structures

Let revenue be \(r\) and tax rate be \(t\).

Session 03: Introduction to Pandas

There are 3 options to unzip the file:

Session 04: Advanced Pandas

In the previous session, we learned how to import .csv files into Pandas and perform the first exploratory checks on a dataset. We worked with the structure of a single…

Session 05: Intro to Data Visualization

Data visualization is not only about making charts look attractive. It is about helping the reader understand patterns, comparisons, distributions, relationships, and trends…

Session 06: Data Visualization

So far, we used matplotlib to visualize variables that already existed in the instacart DataFrame. However, in real data analytics work, we often need to go one step further.

Session 07: Working with Dates

Before building time-based visualizations, we need to become comfortable working with dates. In analytics, dates are everywhere: transaction dates, signup dates, campaign…

Session 08: Data Visualization | Plotly

Plotly is a Python library for creating interactive visualizations. It is widely used in analytics, data science, business intelligence, dashboards, and reporting because it…

Session 09: Customer Segmentation | RFM

Before starting this exercise first let’s download the data, which you can find here