NYU Data Science Review: Writing Prompts
Unsure of what to write about? Read on for a set of data science writing prompts that will spark inspiration!
Let me set the scene. You’re brimming with excitement because you’ve just found out about the NYU Data Science Review. You can’t wait to put pen to paper (or fingers to keyboard) and write an amazing article.
You settle in at your desk, whip out your laptop, and —
blank.
What to write about?
Sometimes it can be difficult to figure out the article topic, despite your passion. Struggle no more! We’ve put together a list of brainstorming questions and data science topics to help your idea generation.
Prompts:
- What was an assignment you struggled with? How did you overcome it, with what skills, etc.?
- When did you last learn a new skill (coding language, etc.)? Can you teach it?
- What intersection of data science and your second major/minor interests you?
- Have you used data to address a real world problem?
- When did you last feel excited by a lecture topic? How can you dive deeper?
- Why is data important? Do you have a personal or academic experience in which you were reminded of this?
- What are the hidden ways that data infiltrates our daily lives?
- What are your favorite hobbies and passions? How is data applied or related to them?
- What is a recent research project you worked on?
- How do you want to use (or how have you used) data science to impact other’s lives?
Topics
Current Events
- Misrepresentation of data in the news
- Deepening understanding of current issues using data
- Data driven solutions to real world problems
Artificial Intelligence/Machine Learning Applications
- Natural Language Processing
- AI & Art (DALL-E, Stable Diffusion, MidJourney)
- Chatbots (LamDA AI)
- Computer vision
- Self driving cars
- Large language models (GitHub Copilot, Jasper.ai)
- Quantum computing in data science
- Network Security/Cryptography (fraud detection, homorphic encryption)
Data Handling
- Databases — How to use them/what exists?
- Data visualization tools and applications
- Data/web scraping
Ethical Data Science
- Data lifecycle
- How biases and discrimination arise in modeling
- Performance analysis and metrics
- Information use and privacy
Theory
- Reinforcement learning (Markov modelling)
- Deep learning (Convolutional Neural Networks, black box models)
- Probability and statistical testing (bootstrapping, hypothesis testing, etc.)
Miscellaneous
- Google Cloud Platform + AWS
- Coding for data science
We hope these prompts sparked some inspiration for you! Now, go forth and write! When you’re ready to submit an article for publication with us, visit this guide for instructions and the submission link.
