Ever wondered how statisticians / data scientists analyse data and create visually appealing graphs? This workshop serves as an introduction to the most popular data analysis library - tidyverse in R. Do not worry if you have never used R, we are going to guide you from the very basics of R programming to using dplyr for data wrangling followed by creating meaningful visualisations using ggplot2. Empowered with this knowledge, you shall be able to create your own plots and analyse any dataset that interests you. 

Ever wondered how to convert insights into beautiful visuals and messages that will engage your audience? Join us on 27th Jan to see how we can leverage on Gestalt principles to tell the story of your data more effectively. Come join us to learn more about cognitive load, visual tools and how you can put knowledge such as significance tests to real life application. For those who are interested, we also introduce JASP, a software that could be useful to many of you in the future. 

We’ll start with an introduction to classical computer vision and the OpenCV library, where you will learn about image processing techniques, edge detection, template matching and more, followed by deep learning concepts for object detection. You’ll be exposed to a variety of convolutional neural network-based image models that you can choose to use when building your model for the Data Science Competition. Finally, we will elaborate on how you can leverage Transfer Learning to simplify and accelerate the model development process.

In this age of big data, many algorithms are restricted by large dimensional data, commonly known as the “Curse of Dimensionality”. Dimensionality reduction is a class of methods that can alleviate this problem. In this workshop, we introduce a dimensionality reduction algorithm in-depth, Principal Component Analysis (PCA), covering the statistics and linear algebra prerequisites required, the PCA algorithm and what it computes, and situate the algorithm in a diverse range of applications: Machine Learning, Biology and Finance. The workshop is conducted by the Workshops team’s most diverse people: Ang Yi Zhe (Data Science and Analytics), Ang Ming Liang (Computational Biology and Mathematics) and Zheng Peng (Quantitative Finance). 

Have a real-world problem you want to solve with machine learning (ML), but don’t know where to begin? Fret not - In our project-driven workshop, we will cover everything from picking the right ML tools, sourcing or even creating your own dataset, model training, all the way to deploying it on a web server to be viewed as an interactive, demonstrable product. At the end of the workshop, you can expect to take away your very own Face Mask Detector that you can enhance in any direction you might like. Join us on 17 September to build and deploy your face mask detector👍

Excited to learn about how to work with multiple tables and tap into more powerful functions with SQL? Join us, as we learn to write better SQL functions together in this questions-based workshop. Don’t work for data, make SQL and data work for you instead! 

In this workshop, we employ a questions-based approach to learning SQL for data science. We introduce basic relational database architectures and demonstrate basic queries using SELECT, FROM and WHERE statements to retrieve data that is relevant to your needs. Join us as we learn to have fun writing queries to answer important questions in data science! 

At the Student Life Fair, hear about the events and activities we organise, and experience a workshop trailer on machine learning from scratch!