Monthly EditionThe art of finding the right topic—and the right scope—for data Science and ML projectsPhoto by Christopher Paul High on UnsplashWhat is it that makes a data science project stand out? At a time when job applicants are expected to have shiny portfolio sites and when data teams need to demonstrate their business value on an ongoing basis, the question has rarely been more crucial.Technical ability is a major ingredient, of course, but as editors we often realize that it isn’t enough. The most compelling project-based articles we share with our readers certainly showcase their authors’ know-how and expertise, but even more important, they display an understanding of scope. The problems they tackle can be theoretical or practical, work-related or entirely passion-driven; regardless of topic, though, we never lose sight of what they aim to do (and what they don’t) and of how each step along the way takes us closer to a solution.Rather than go on and on about the characteristics of great data science and machine learning projects, we invite you to explore some excellent ones for yourself—we’ve selected a strong lineup of recent project walkthroughs where you can find inspiration, guidance, and perhaps even a practical roadmap for developing your own ideas.Before we dive in, we wanted to thank you, as always, for all your support. For those of you who’d like to make a meaningful contribution, consider becoming Medium members.TDS EditorsTDS Editors HighlightsRock Paper Scissors: A Quantum Computing Twist (May 2023, 14 minutes)There’s been a lot of buzz around quantum computing recently, but it remains a somewhat hazy concept for many. Kory Becker found an intuitive way to highlight the differences between how classical and quantum computing work: creating a program that simulates the winning hands in rock paper scissors games.The Forbidden Pages: A Data Analysis of Book Bans in the US (March 2023, 17 minutes)Data can help us gain a deeper and more nuanced understanding of social issues, but to accomplish that we also need to have a robust approach to data collection, analysis, and storytelling. Yennie Jun’s deep dive into the worrying trend of book bans in the U.S. is a powerful example of how this process can facilitate constructive conversations.Building an AI to Recognize my Handwriting — Part I (April 2023, 12 minutes)A personal passion project can serve as a solid starting point for learning about complex technical challenges. Case in point: Jonas Schröder’s walkthrough of the tool he’s building to convert his handwritten journals into plain-text files.How I Built a Lo-fi Music Web Player with AI-Generated Tracks (January 2023, 11 minutes)What do you do if you’re curious about deep generative models and have had a soft spot for lo-fi hip hop for years? Well, if you’re Aleksandra Ma, you learn how to generate midi tracks with LSTM models, and then embed them in a nifty web player so others can enjoy your creations, too.Unsupervised Learning with K-Means Clustering: Generate Color Palettes from Images (April 2023, 13 minutes)If you ever feel like it’s all been done before, you should check out Nabanita Roy’s recent project, which leverages unsupervised learning and k-means clustering to generate color palettes based on image inputs. It’s a great reminder that we can express our creativity by combining elements of established practices and giving them a personal touch.A Short and Direct Walk with Pascal’s Triangle (November 2022, 10 minutes)One of the best things about well-defined problems is that even as you incorporate new elements and perspectives into your solution, your work is more likely to remain focused and clear. Rhys Goldstein’s article on pathfinding algorithms illustrates the point well: it touches on math, gaming, problem-solving, and… Blaise Pascal, but the various moving parts (including some neat animations!) all serve the overarching goal.Original FeaturesExplore our latest selection of resources and reading recommendations.Data Science Expertise Comes in Many Shapes and Forms Don’t miss our selection of excellent contributions by our latest cohort of new authors.The Emerging Art of Prompt Engineering To make the most of generative-AI tools we need to communicate effectively with models. Here you’ll find our best resources on this topic.Popular PostsIn case you missed them, here are some of last month’s most-read posts on TDS.10 Exciting Project Ideas Using Large Language Models (LLMs) for Your Portfolio by Leonie MonigattiHow I Stay Up to Date With the Latest AI Trends as a Full-Time Data Scientist by Matt ChapmanHow GPT Models Work by Beatriz StollnitzData-Oriented Programming in Python by Tam D Tran-TheWhat I Learned in My First Year as a Director of Data Science by CJ SullivanTen Years of AI in Review by Thomas A DorferWe were thrilled to welcome a new cohort of TDS authors in May — they include Lee Vaughan, Dan Wilentz, Brendan Mapes, Yuji Yamamoto, Andrea Valenzuela, Igor Šegota, Lando L, Przemek Pospieszny, Andreas Lukita, Dr. Bernd Ebenhoch, Jozsef Meszaros, Stephanie Lo, Mostafa Wael, Beatriz Stollnitz, Edgardo Solano Carrillo, Prabodh Agarwal, Markus Hubrich, Dan Jackson, Matt Tengtrakool, Benjamin Thürer, Matt Blasa, Simon Aytes, and Jack Saunders, among others. If you have an interesting project or idea to share with us, we’d love to hear from you!See you next month.June Edition: Projects That Inspire was originally published in Towards Data Science on Medium, where people are continuing the conversation by highlighting and responding to this story.