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# Introduction
Maintaining data science isn’t always easy. There are new libraries, papers, datasets, and tools every day, and I can’t remember them all. I find that just following newsletters or threads doesn’t really work. What helps more is to have some resources ready. For me, it’s like a little hub where I keep research, coding stuff, datasets, visualizations, and quick references all in one place. After trying a bunch of things, I now have 10 bookmarks that I use all the time. They help me stay focused, save time and know what’s going on. Every morning I open them and they set the tone for my day. Here’s a look at my top bookmarks and why I keep them:
# 1. Arxio: Machine Learning (CS.LG) New Papers
Arxio Where I check out the latest machine learning research. The CS.LG section covers everything from theory to applied machine learning in NLP, vision, and RL. I bookmark it and check back often so I don’t miss papers that might inspire new ideas or projects. It’s a great way to stay ahead and learn about new methods before hitting articles or GitHub.
# 2. GitHub Trending Python Repos
This page Showcasing the hottest Python projects every week, from new libraries to experimental tools. I bookmark it because data science is not only about algorithms, it is also about tools. Scanning what’s trending helps me quickly find useful libraries or patterns, before they get too crowded. Just 10 minutes a week here usually enables me to give a thing or two.
# 3. Data is abundant
Data is abundant There is a newsletter and an archive full of unusual and interesting datasets. I bookmark it because it’s great for finding project ideas, tutorials, or hackathon challenges. Each dataset has a brief description and a link. It’s an easy way to discover new data and get ideas beyond Kaggle or the usual sources.
# 4. Rundown AI
Rundown AI Saves me hours of searching, aggregates AI and machine learning news and papers above. Whether it’s a new paper, a tool release, or an emerging approach, it’s quickly reviewed so I can see what’s relevant. Basically, an easy way to stay informed and keep up with trends.
# 5. Raw graphs
raw Clean is a free, browser-based tool for creating custom charts fast. I can create visuals straight from CSV or JSON without complicated writing matplotlib or Seaborne This code is great for creating charts for trends, outliers, or reports. Charts export easily in vector formats, so they look professional in slides or essays.
# 6. Quartz Fault Data Guide
Quartz Bad Data Guide It’s one of my go-tos whenever I’m cleaning up messy data. It goes over common problems like missing values, garbled text, inconsistent formatting, and incorrect numbers, and gives tips on how to fix them. Dirty data is only part of the job, and this guide saves me a lot of time troubleshooting. I also like how it’s structured as to who should fix who, which makes tracking and resolving issues much easier.
# 7. Five minute statistics
Five minute statistics A quick reference for essential statistical concepts and formulas. I can easily brush up on topics like hypothesis testing, probability distributions, correlation, and descriptive statistics in just a few minutes. It’s perfect when checking math, preparing lessons, or writing lessons without digging through textbooks.
# 8. Amazing data analysis
Strong data analysis GitHub is a collection of tools and resources for all parts of the data workflow. I keep bookmarking it because it’s great for cleaning, manipulating, visualizing data, and building machine learning pipelines. If I’m trying out new libraries, refreshing my toolkit, or sharing with colleagues or students, it helps me quickly find reliable, well-maintained tools.
# 9. Stop
Stop it A tool for generating random data and mock APIs. I can create realistic datasets in CSV, JSON, SQL, or Excel without typing everything by hand. This is great for testing code, dashboards, or machine learning workflows, including hard edge cases. Mock APIs also let me work on the frontend and backend at the same time.
# 10. Furrella
foorilla Tech and Data is a job listing platform. I use it to browse new openings, follow companies, and filter jobs by title, location, or remote options. You can also export lists in CSV or JSON, making it easy to track opportunities. It’s an easy way to stay updated on the job market without hopping between multiple sites.
Kanwal Mehreen is a machine learning engineer and technical writer with a deep passion for data science and the intersection of AI with medicine. He co-authored the eBook “Maximizing Productivity with ChatGPT”. As a 2022 Google Generation Scholar for APAC, she champions diversity and academic excellence. He has also been recognized as a Teradata Diversity in Tech Scholar, a MITACS GlobalLink Research Scholar, and a Harvard Wicked Scholar. Kanwal is a passionate advocate for change, having founded the Fame Code to empower women in stem fields.