Data Science and Artificial Intelligence

In this Data Science session, you’ll learn foundational skills for adding Python to your Data Science and Analytics Toolbox.

A professional instructor from COSO IT will guide you through writing your first machine learning algorithm (K Nearest Neighbour) in Python from scratch. You'll learn how the algorithm works, where it can be used, and you'll get a chance to run it on real text data. You’ll leave this session equipped to start writing your own Python scripts to analyze data, and recommendations about next steps to take on your pathway to data science.


This Session is for beginners and does not require prior programming experience or in-depth knowledge of statistics or probability. If you have never seen Python before, never fear! True beginners are welcome to come to listen, learn, and observe.

If you've already got some familiarity with Python, that's wonderful! Participants who are familiar with concepts of data analysis, statistics, and probability will be better equipped to apply their skills after the conclusion.

We recommend installing anaconda ( on your computer and quickly testing your ability to run a simple Python script (make a text file called, type into it print("Hello"), and in Terminal try running python If you want to learn or review some Python basics beforehand to make the most of the event, we recommend checking out the free online book Think Python (, and in particular reviewing:

  • Strings
  • Lists
  • Dictionaries
  • For loops
  • Functions
What You’ll Learn:
  • How to derive K nearest neighbors (KNN) and why it's useful?
  • How to read data files into Python and pull out simple text features?
  • How to build a K nearest neighbors based on our extracted text features?
  • How to evaluate how well our classifier performs in terms of accuracy, precision, recall, score?