Welcome to the 204th issue of the Sunday Briefing. This issue marks the fourth anniversary of our newsletter! It’s been quite a ride since the very first issue all the way back on June 3rd, 2019. We’ll be celebrating 🍾 throughout the month of June with a few changes and announcements!
The first major announcement is a rebranding. The Sunday Briefing will henceforth be known as the Data Science Briefing and, starting next week, will be published every Wednesday!
This week we continue our hiatus from blogging but we have several posts in the final stages of writing. In the meantime, you can catch up on the latest post in the Viz4Sci series: Waterfall Chart. The latest Medium post “Top 10 Books we read in 2022” and the most recent post on the G4Sci series: Network Attacks: Breaking up a network without observing it completely.
Several of you have reached out in recent weeks asking how they can help support the work we do here at Data For Science. While the Sunday Briefing is a labor of love and will always remain free, it is not without costs, and any help is appreciated.
If you wish to support our work, there are several options:
- Paid subscription to Graphs for Science or Visualization for Science which gives you complete access to the archive of previous posts.
- Subscribe to Medium using our referral link at no extra cost to you
- Directly through a one-time PayPal donation.
On our regularly scheduled content, we learn how to use graphs to model and analyze the customer journey, follow a Data-Centric Introduction to Computing and discover the Most Important Papers for Quantitative Traders.
On the academic front, we have A Brief Introduction to Machine Learning for Engineers, learn about the effects of cash transfers on adult and child mortality in low- and middle-income countries and Why Are There Six Degrees of Separation in a Social Network?
Our Data Science Book is “Network Science with Python”, by D. Knickerbocker. As always you can find all the previous book recommendations on our website. In the video of the week, we have a lecture on Message Passing Algorithms for Network Scheduling.
Data shows that the best way for a newsletter to grow is by word of mouth, so if you think one of your friends or colleagues would enjoy this newsletter, just go ahead and forward this email to them. This will help us spread the word!
Semper discentes,
The D4S Team
This week’s Data Science Book, " Network Science with Python ", by D. Knickerbocker is a highly recommended book for anyone interested in network analysis. It provides a comprehensive and accessible introduction to the topic. The book's linear progression and friendly tone make it highly engaging and easy to follow. The author's contagious enthusiasm and practical examples effectively communicate the power and importance of network analysis. The book covers various domains, including language and social media data mining, and explores the relationship between NLP and networks, an approach similar to our very own Graphs for Data Science substack. It emphasizes the value of actionable insights in the conversational AI domain and provides historical context and real-world use cases for NLP solutions. The book also introduces the Python packages used and dives into network science using the NetworkX library. It demonstrates how graphs can be used in machine learning and covers important concepts like betweenness centrality, page rank, and community detection with real-world applications. Overall, "Network Science with Python" is a well-written and comprehensive guide that offers practical insights and is suitable for readers of all levels.
