Python Programming

Course Overview

This course is designed to provide a comprehensive introduction to Python and its application in various programming tasks, particularly in data science.

Starting with basic programming concepts, data input/output, functions, and classes, the course progresses through an exploration of common libraries essential for data science, including Numpy for linear algebra, Keras and PyTorch for neural networks, Pandas for data manipulation, and TensorFlow for Google’s data science projects. Participants will learn about different Platforms/IDEs, including writing code in PyCharm versus text editors, and the use of Git for version control.

You will receive a comprehensive an introduction to Jupyter Notebooks, comparing it with Databricks for writing Python in pieces and facilitating collaboration in machine learning projects. Further modules cover data visualisation with tools like Plotly and Matplotlib, basics of Exploratory Data Analysis (EDA), data cleaning techniques, and reframing data to answer specific questions. The course concludes with best practices in testing and troubleshooting, including test-driven development, pair programming, code reviews, and automated testing procedures.

Learning Objectives

• Acquire a foundational understanding of Python programming, including basic data input/output, the creation of functions and classes, and how to access key resources for learning and problem-solving.

   

• Gain proficiency in utilising common Python libraries for data science such as Numpy for linear algebra, Keras and PyTorch for neural networks and deep learning, Pandas for data manipulation, and TensorFlow for machine learning projects.

   

• Understand the differences and applications of various Integrated Development Environments (IDEs) and platforms, including PyCharm and text editors, and learn to work with Git for version control and publishing/hosting Python applications.

   

• Learn to use Jupyter Notebooks for writing Python code in pieces, performing inline data analysis, and facilitating collaboration in machine learning projects, along with comparing its capabilities with Databricks.

   

• Develop skills in data visualisation using tools like Plotly and Matplotlib to create histograms, scatterplots, and other visual representations of data, including how to highlight key reference points in graphs.

   

• Master the basics of Exploratory Data Analysis (EDA), including data cleaning techniques, framing common questions to understand data better, and reframing data to answer specific questions, along with testing, troubleshooting, and best practices in Python programming.