The Dataquest Download
Level up your data and AI skills, one newsletter at a time.
Hello, Dataquesters!
Here’s what we have for you in this edition:
Top Read: 30 data science projects with real datasets, source code, and step-by-step instructions to help you build a portfolio that gets noticed. Learn more
From the Community: When R still beats Python, why Python and SQL are better together, and a closer look at financial data using the Nasdaq Data Link API. Join the discussion
What We’re Reading: Common Python mistakes that trip up even senior developers, and new MIT research on making AI predictions easier to explain. Learn more
Top Read
The fastest way to build a data science portfolio is to finish real projects. This guide shares 30 beginner-to-advanced data science projects, each with source code, real datasets, and step-by-step instructions so you can start building immediately.
If you want projects that actually help you get hired, this list gives you practical ideas you can turn into strong GitHub portfolio pieces.
From the Community
When Data Scientists Still Choose R Over Python: Alla discusses the application domains of Python and R programming language and highlights scenarios in which R’s advantages make it the preferred tool for specific data science tasks.
Python vs. SQL: Linky explains why Python and SQL work not as rivals but as teammates, how each serves a different stage of a project, and how they can be combined into a powerful workflow to ask better questions and uncover deeper insights.
Exploring Financial Data Using the Nasdaq Data Link API: Casandra examines the importance of accrued expenses turnover in a business context, its relationship to company success or failure, and how it may be influenced by accounting regulations across different countries.
What We're Reading
Python Mistakes Even Senior Devs Make: Python looks simple, but its behavior around names, memory, and logging can lead to subtle bugs. This roundup highlights common mistakes that even experienced developers run into. A quick read that could save you hours of debugging.
Improving AI Models’ Ability to Explain Predictions (MIT): Most AI models can generate predictions but struggle to explain them. MIT researchers developed a technique that extracts concepts learned during training and converts them into plain-language explanations. An interesting step toward more trustworthy and interpretable AI systems.
Give 20%, Get $20: Time to Refer a Friend!
Give 20% Get $20
Now is the perfect time to share Dataquest with a friend. Gift a 20% discount, and for every friend who subscribes, earn a $20 bonus. Use your bonuses for digital gift cards, prepaid cards, or donate to charity. Your choice! Click here
High-fives from Vik, Celeste, Anna P, Anna S, Anishta, Bruno, Elena, Mike, Daniel, and Brayan.