DDDI’s 2023 Summer Hangouts program offers students the opportunity to participate in informal, hands-on tutorials led by our team of DDDI postdoctoral research fellows. These tutorials are open to students from all backgrounds and skill levels and cover various data science methods and topics. Hangouts will be held once a week on Thursdays, starting from July 20th until August 17th. We’ll also provide a pizza lunch between tutorial sessions. All tutorials will take place in the newly renovated RDDSX space, conveniently located near the Collaborative Classroom in Van Pelt-Dietrich Library.
Our Hangouts series this year will be a “choose your own adventure” through data science. Students are welcome to attend tutorials as best fit their time and interests: no prior registration required.
Location
All tutorials will take place in the new RDDSX space just outside the Collaborative Classroom in Van Pelt-Dietrich Library.
Live Stream
- Morning session (11:00 – 12:00) zoom meeting room
- Afternoon session (12:30 – 1:30) zoom meeting room
- August 17th Special Session (1:30 – 2:30) zoom meeting room
Recordings
Recordings of the sessions are available here.
Schedule
Hangouts will run once a week on Thursdays from July 20th through August 17th. A pizza lunch will be served between tutorial sessions.
July 20
| Time | Instructor | Title | Description |
|---|---|---|---|
| 11:00 – 12:00 | Colin Twomey | Coding for Data Science | What’s a programming language, and why use one for data science? What’s a coding assistant, and what can (and can’t) it do for you? In this session, we’ll give a big picture introduction to programming for data science that should be accessible to everyone. No prior coding experience required. |
| 12:00 – 12:30 | Lunch | ||
| 12:30 – 1:30 | Sarah Lee | First Steps with R | In this tutorial, we’ll walk through some practical steps for working with R for the first time: covering data organization in spreadsheets, importing data into R, inspecting data, data wrangling, and very basic visualization. |
July 27
| Time | Instructor | Title | Description |
|---|---|---|---|
| 11:00 – 12:00 | Vlad Ayzenberg | First steps with Python notebook |
Similar to the “First steps with R” tutorial last week, we’ll take an introductory walk through Python for data science.
|
| 12:00 – 12:30 | Lunch | ||
| 12:30 – 1:30 | Sam Dillavou + Kieran Murphy | Introduction to Machine Learning notebook |
Peeling away the recent buzz around AI, how do you make a machine learn? How is it different than how humans learn? We’ll go over the basics with some simple machine learning models. Python will be used but not necessary for understanding the core concepts. |
August 3
| Time | Instructor | Title | Description |
|---|---|---|---|
| 11:00 – 12:00 | Jingye Yang | Introduction to LLMs |
Ever wonder how ChatGPT seem to ‘understand’ what you’re saying? This friendly talk will dive into the world of large language models. We’ll break down how these AI brains learn to chat just like us, their cool uses (think helping with work or writing a novel), and the challenges we face with them, like making sure they’re used responsibly. |
| 12:00 – 12:30 | Lunch | ||
| 12:30 – 1:30 | Carlos Schmidt-Padilla | Introduction to Causal Inference
in person only |
In this tutorial, we will provide a comprehensive explanation of causal inference, which involves understanding and determining the cause-and-effect relationship between variables or events. We will explore how this concept is applicable to various fields, including technology (such as A/B testing), health sciences, and the social sciences. By delving into these applications, we will gain insights into how causal inference plays a crucial role in understanding and making informed decisions based on causal relationships in diverse domains. |
August 10
| Time | Instructor | Title | Description |
|---|---|---|---|
| 11:00 – 12:00 | Chang-Yu Chang | The Art of Naming Your Pets and R Objects | Have you ever found yourself running out of English letters to name your R objects? Fear not! We’ll dive into the world of naming conventions for R objects and functions. We’ll also introduce you to a basic structure for managing data science projects, ensuring a smooth and organized workflow. |
| 12:00 – 12:30 | Lunch | ||
| 12:30 – 1:30 | Brynn Sherman | Data Visualization with R and ggplot | In this tutorial, we’ll discuss tips and tricks for data visualization in R. We’ll start with a brief refresher of R and the basics of ggplot. We’ll then go more in depth about how to customize various aspects of graphs, decide on color schemes, etc., all with the goal of making your graphs as clear and visually appealing as possible. |
August 17
| Time | Instructor | Title | Description |
|---|---|---|---|
| 11:00 – 12:00 | Sergey Molodtsov | Python data analysis and visualization | This tutorial will provide basic concepts of data processing and visualization. We will use multidimensional climate and geoscience datasets to demonstrate the simple statistical analysis and the plotting capabilities of Python. We will also cover the different libraries available in Python to work with large multidimensional arrays. |
| 12:00 – 12:30 | Lunch | ||
| 12:30 – 1:30 | Sunghye Cho | Introduction to Automatic Speech Recognition slides and notebook |
Have you ever wondered how Siri or Alexa understands what you say? In this tutorial, we will walk through very basics of Automatic Speech Recognition (ASR). At the end of the tutorial, we will learn how to run one of the state-of-the-art models, Whisper, on audio files and how to evaluate the model’s performance, using Python. |
| 1:30 – 2:30 | Erçağ Pinçe | Image processing and Computer Vision with Deep Learning Tools | In this tutorial, we will delve into how neural networks and machine learning frameworks can be harnessed to process and track images of microscopic objects. Our journey will commence with an exploration of DeepTrack 2.0, an extensive open-source deep-learning framework tailored for digital microscopy within the Python environment. The tutorial will then branch into the realm of 3D tracking, where we’ll explore the complexities of monitoring moving microscopic elements in a three-dimensional space and several caveats that come with this. Then, we’ll venture into label-free tracking, a particularly intricate tracking task. Finally, we’ll examine how this novel microscopy approach can unlock new insights into bacterial microecology, motility research, and the domain of active matter more generally |