Jupyter Notebooks and code for the 2019 STAT2601 Python Data Science Workshop.

• Courses
• Workshop pre-requisites
• Required software

This repository contains Jupyter Notebooks for the following short courses: Please click notebooks shown below, which is a shared Jupyter Notebook on Google Colab.:heart_eyes:

This course teaches some of the building blocks for handling data in Python. We will show how to parse and manipulate data using Numpy and Pandas, and perform interactive visualization with Plotly. We analyze a real dataset using Scipy, demonstrating how to perform basic statistical tasks in Python.

Notebook: 1. Introduction to Data Science in Python.ipynb

This course introduces the matplotlib library for doing visualization in Python. Students will start by learning about drawing scatter plot, and gradually explore how matplotlib interacts with Pandas. Also, we will introduce the alternative libraries for matplotlib in python and compare the differences.

Notebook: 2. 2. matplotib_seaborn_student.ipynb

This course introduces the Scikit-learn library for doing machine learning in Python. Students will start by learning about support vector machines, and gradually explore how Scikit-learn allows you to build a full machine learning pipeline, from feature extraction all the way through to prediction.

Notebook: 3. Intro to Machine Learning in Python with Scikit-learn.ipynb

Neural networks are becoming an increasingly important tool in machine learning. In this short course, we demonstrate how to rapidly prototype an artificial neural network (ANN) in Python using the Keras library. We briefly introduce ANNs, including important variations like convolutional networks. Using Keras, students will build their own networks for some basic machine learning problems.

Notebook: 4. Building Neural Networks With Keras.ipynb

This workshop is designed to give beginners the practical skills when examples and exercises emphasize how these techniques can be applied to real-world situations and use cases. So, previous math or coding experience is not required!

In this workshop, we will use Google Colab.