A good Data Analyst is likely to be assigned various tasks (because of how awesome they have been on other tasks), some of which (explicitly or implicitly) require good Python programming skills to make them more efficient. This session is intended to help Data Analysts become more productive by using Python to automate parts of their growing variety of tasks.

Delivery ModeOnline
Duration2 hours
PrerequisitesNone
Includes Hands-On SessionYes

Target Audience

This session is suitable for:

  • Data Analysts who wish to brush up their Python programming skills
  • Other professionals who have at some point learnt Python but feel the need to brush up their Python programming skills
  • University students with an interest in Data Engineering, Software Engineering, or just wanting to improve their Python programming skills

Minimum Python Skills Expected

As of November 2023, the minimum Python skills expected from good Data Analysts are:

  • Generators
  • Iterators
  • Object-Oriented Programming
  • Regular Expressions
  • Threading and Multiprocessing

You can read our blog post for simple explanations of the skills listed above,
📰 “Minimum Python Expected From Junior Data Engineers”

Benefits For Data Analysts

What are some scenarios where a Data Analyst with a good grasp of Python is in an advantageous position?

Here are a few (with names and certain details masked for privacy reasons) based on observed scenarios at the workplace.

Scenario 1: 📊💥➜ 🤘👌

A Data Analyst, let’s call her Amy, was typically used to working with moderate-sized Excel files, and then, one day, she was assigned a task with datasets that exceeded spreadsheet limits. Amy had been steadily trying to improve her Python programming skills in her spare time. Based on what she had recently learned, she could perform interactive analyses on her entire dataset and find patterns across millions of rows within minutes. The speed and depth of her analysis impressed senior leadership.

Which skills helped in this scenario? Generators, Threading and Multiprocessing

Scenario 2: ✍️📊➜ 🤖📊

A Data Analyst, let’s call him Andrew, noticed another department’s team members manually updating dozens of Excel reports each month by copying and pasting from some web pages. He had some prior experience curating tabular datasets by scraping websites using Python. He helped his colleagues auto-populate these reports based on live data from the websites they would scan manually, format the information in the required format, and have them ready for distribution. This saved 15+ hours of work each cycle and gained his recognition as a problem solver.

Which skills helped in this scenario? Generators, Iterators, Regular Expressions, Threading and Multiprocessing

What Will Be Covered

In this session, you will,

  • Get an overview of each of the minimum Python skills expected from good Data Analysts.
  • Develop (via iterative improvements) efficient Python code for a few common tasks, such as data transformations, and feature engineering to support model training.
  • Learn how to write unit tests to improve reliability of code developed.
  • Learn how to speed up slow (inefficient) Python code.

Hands-On Session

We will use Google Colab notebooks to simplify the process by eliminating the need to install prerequisite libraries.

Upcoming Sessions

WhenWhere
Sunday, 18 Feb 20249 - 11 PM 🌏OnlineRegister
Sunday, 24 Mar 20249 - 11 PM 🌏OnlineRegister

Please note:

  • All times above are expressed in SGT. Click on the 🌏 icon next to sessions of interest to get your location’s corresponding date and time.
  • For reference, 9 PM SGT is 6:30 PM (Bangalore), 8 PM (Jakarta), 6 PM (Lahore)

Other Dates & Times

👋 I am interested but don’t see a date or time that works for me.

You can indicate your preferred date & time when you register.

What’s Next?

What should I learn after attending this session?

Tracks

This session covers content that is part of the following tracks:

Full List

Further Reading

  1. Minimum Python Expected From Junior Data Engineers (Nov, 2023)