Data science has an impact on everyone’s lives, whether they are practitioners or unsuspecting recipients of data scientists’ efforts. Assuming you fall into the former category, or at least aim to do so, here are 11 data science blogs that will help you enjoy anything that is related to data science.
The majority of them are authored by and for experienced data scientists. However, there are some pearls of wisdom for newcomers here, explaining what all the hubbub is about in layman’s terms. Since we did curate a technology resource for the community like choosing the best laptop for data science and data analysts, we thought sharing the curated top data science blogs would add even more value.
Kaggle Winner’s Blog
The Kaggle Winner’s Blog, often known as No Free Hunch, may be the most entertaining data science blog. Kaggle runs the blog and the website, and it provides a customisable Jupyter Notebooks environment with free GPU access and a repository of community-published data and code.
Kaggle Winner’s Blog also hosts a variety of competitions for data scientists. In these competitions, data scientists must develop the best model for data sets. Organisations such as Microsoft and the National Football League host challenges with cash prizes.
A particular blog has a post about Sanghoon Kim (also known as Limerobot), the third-place winner of Booz Allen Hamilton’s 2019 Data Science Bowl, a competition focused on social good. The South Korean data scientist earned for identifying parameters that can assist measure how young toddlers learn. The competition has attracted almost 50,000 participants.
r/DataScience
With almost nine years of experience and 338,000 followers, r/DataScience or Data Science Reddit is regarded as one of the most popular data science communities. Members ask questions like, “Am I shooting myself in the foot for listing that my master’s degree has a specialisation in data science?” which received 70 answers in the first 14 hours after member u/beepboopdata posted it.
Another issue to consider is, “Why is R so valuable to some employers if you can literally do all of the same things in Python?” This received over 300 comments six days after it was posed by reader u/willcostiganjr.
Datafloq
Datafloq, based in The Hague, Netherlands, serves as a conduit for data science materials as it does anything else. Founder Mark Van Rijmenam created Mavin, a content rating tool that rates member-submitted blogs. Van Rijmenam founded Mavin alongside Thomas Modeneis, Patrick Joore, and George Visniuc.
- The site has articles like “How to Use AI to Enhance the Performance of Your Social Media Strategy,” written by Lauren Wiseman.
- Dhaval Sarvaiya’s article, “6 Ways to Effectively Increase Your Conversion Rate with Chatbots,” provides insights.
- “Transforming Big Data to Competitive Intelligence for Achieving Competitive Advantage,” by Shilpa Marketer, Inc.
Revolutions
Microsoft hosts Revolutions, a blog that covers R-related news and information. Microsoft acquired the blog, which was previously known as Revolution Analytics, in 2015. As of December 2020, David Smith, Microsoft’s R community leader, serves as the blog editor.
The blog posts are divided into approximately 25 categories, including “applications,” which showcases interesting R applications to real-world problems; “predictive analytics,” which includes posts about predictive analytics, data mining, and machine learning; and “developer tips,” which contains information for R package authors and developers.
Aylien
It employs AI to assist organisations and developers in collecting, analysing, and comprehending vast amounts of human-generated content. The core of its services is the use of natural language processing (NLP) to filter through thousands of news sites and analyse them based on your requirements.
Topics include “sentiment analysis,” which is the application of machine learning and natural language processing to analyse text. In one blog post, Eoin Kilbride, a product specialist at Aylien, examines sentiment analysis to see whether it is “being discussed in a positive, neutral, or negative light.”
KDnuggets
The KDnuggets website delves into AI, analytics, big data, data mining, data science, and machine learning. The site’s editors are Matthew Mayo and Gregory Piatetsky-Shapiro. Mayo is a machine learning researcher, whereas Piatetsky-Shapiro is a co-founder of the Knowledge Discovery and Data Mining conference, as well as a previous chair of ACM SIGKDD, a professional association for data mining and data science.
KDnuggets is a well-known data science website that provides connections to data sets, tutorials, and webinars. Nicole Janeway Bills, a data scientist at Atlas Research and federal government consultant, guides her readers through five common mistakes in data science planning projects, while Frank Fineis, lead data scientist at Avatria, writes about the business reasons for deep learning models.
Subconscious Musings
Subconscious Musings, a data science blog from vendor SAS, provides the viewpoints of SAS data scientists as they reveal the technical methodologies used to tackle many of the hard problems that organisations face today, from recurrent neural networks to feature engineering. The blog is thorough and popular among people seeking a deeper understanding of NLP, neural networks, AI, and other relevant topics.
Brandon Reese, a senior machine learning developer in Scientific Computing R&D, demonstrates how to represent data as a network, run standard network science algorithms, and interpret the results, as does Susan Kahler, a global product marketing manager for AI at SAS with a Ph.D. in human factors and ergonomics. She describes how she used analytics to measure and compare mental models of how people learn difficult procedures.
Data Science Central
Data Science Central, a member of the TechTarget network, is the industry’s online destination for big data professionals. Data Science Central offers a community experience across analytics, data integration, and visualisation. The site provides a variety of information, including webinars, free books, and forums. The site discusses analytics, business intelligence, and Hadoop.
The blog section of the site features approximately 24 posts per week. The authors include Vincent Granville, an executive data scientist and co-founder of Data Science Central. Granville was a finalist in both the Wharton School Business Plan Competition and the Belgian Mathematical Olympiads, and he has published 40 papers in statistical journals.
He also developed the first Internet of Things platform to automate growth and content development for digital publications, utilising a system of APIs for machine-to-machine connections that included Hootsuite, Twitter and Google Analytics.
Data Plus Science
The Data Plus Science blog is managed by Jeffrey A. Shaffer, Unifund and Recovery Decision Science’s chief operating officer and vice president of information technology and analytics. If you’re into data visualisation, Tableau, or, more broadly, data mining. Data Plus Science is worth a look.
Shaffer was instrumental in the creation and development of Unifund’s business intelligence platform. He has a master’s degree in management from the University of Cincinnati and an MBA from Xavier University.
Data Science 101
For anyone seeking a career in data science or simply wanting to learn more about the subject, Data Science 101 is an excellent place to begin. Ryan Swanstrom, who holds a doctorate in computational science and statistics as well as a master’s degree in computer security, founded one of the first data science blogs.
Blog posts include “How Deep Neural Networks Work” by Brandon Rohrer, a deep learning expert, and “A New Approach to Drug Discovery” by Daphne Koller, a former Stanford professor, co-founder of Coursera, and founder of insitro.
• “The Future of AI and Machine Learning,” by Hilary Mason, founder of Fast Forward Labs.
Algobeans
Consider Algobeans as a preparatory course before moving on to Data Science 101. Written by Annalyn Ng, a senior data scientist at Amazon Web Services, and Kenneth Soo, who has a master’s degree in statistics from Stanford University, it provides short explanations of essential topics without resorting to mathematics. If you’re much beyond the fundamentals, it’s still a very useful site for explaining to others (i.e., laypeople) what you do for a job. It’s also an important site for average folks, whose lives are heavily touched by big data.
For example, one post demonstrates how kernel density plots can aid in the interpretation of form and distribution data in a two-dimensional environment. This is explained by applying a random forest predictor to all shots taken by Liverpool Football Club in the 2017-18 English Premier League.
