Course Information

Data analysis and analytics. Uncover insights and transform your organisation.

To excel in today’s data science field, proficiency in the most effective tools is essential. These proficiencies include a mastery of both R and Python programming languages. This course is designed for data scientists, statisticians, and analysts who are proficient in R and want to transition to using Python for their data science and automation tasks.

This course will cover the fundamental differences between R and Python, key Python libraries for data science, and practical examples to help you become comfortable with Python. This course is a great companion to the Intermediate and Advanced R programming courses offered by CCE.

Aims

This aim of this course is to equip data scientists with the skills to seamlessly transition from using R to Python for their data analysis and machine learning tasks. You will learn to leverage Python’s powerful libraries and tools, enabling you to enhance your data science capabilities and productivity.

Outcomes

By the end of this course, you should be able to:

• leverage the different strengths of both R and Python
• set up a Python Jupyter development environment and install packages
• write Python code for automation of tasks
• use Python and Pandas for data manipulation
• know Matplotlib and Seaborn for data visualisation
• develop functions in Python.

Content

Introduction to Python

• Overview of Python and its applications in data science
• R and Python – pros and cons
• Setting up Python environment using Jupyter Notebooks
• Installing packages in Python
• Basic syntax and data types in Python vs R
• Comparison of R and Python syntax
• Python loops

Data structures in Python

• Lists, tuples, dictionaries, and sets
• Numpy arrays and their advantages over R vectors
• Pandas DataFrames vs. R DataFrames

Data manipulation with Pandas

• Comparison to R Dplyr package
• Importing and exporting data
• Data cleaning and preprocessing
• Data manipulation using Pandas (filtering, grouping, merging)
• Practical examples and exercises

Data visualisation

• Introduction to Matplotlib and Seaborn
• Creating various types of plots (line, bar, scatter, histograms)
• Comparison of visualisation capabilities in R ggplot and Python

Advanced topics

• Python functions
• Introduction to Object Oriented Programming
• Python Streamlit vs R Shiny data applications

Intended audience

This course is ideal for data scientists, analysts, and programmers who regularly use R and are eager to broaden their expertise by learning Python programming.

Prerequisites

It is assumed you have completed CCE’s R Programming Course: Intermediate, or have equivalent knowledge.

Delivery modes

• Face-to-face, presenter-taught training using your own device
• Online training via the platform Zoom

Face-to-face classes

These classes run in a classroom and you need to bring your own device with R, RStudio and Python installed. You should ensure it is fully charged as access to power is limited. Please note that the University does not carry any responsibility for your lost, stolen, or damaged devices whilst on the University premises.

Online classes

You will need your own device with R, RStudio and Python installed.

Delivery style

This course is an interactive experience that includes lectures, individual exercises and discussion.

Materials

All course materials are provided electronically (via Dropbox). Printing services are not provided.

Before the course

Please download and install the following software prior to class:

• R 3.6.x
• R Studio
• Python 3.12.5

Other required package software including pandas, Reticulate and Jupyter notebooks, will be downloaded and installed during class.

Recommended reading

Boehmke, B. and Greenwell, B. (2019). Python and R for Data Science. CRC Press.

Scavetta, R.J. and Angelov, B. (2021). Python and R for the Modern Data Scientist. O’Reilly Media.

VanderPlas, J. (2023). Python Data Science Handbook: Essential Tools for Working with Data. 2nd ed. O’Reilly Media.