TOPIC: MACHINE LEARNING
Some Data Science newsletters that may be worth your time
19th October 2025Staying informed about developments in data science and artificial intelligence without drowning in an endless stream of blog posts, research papers and tool announcements presents a genuine challenge for practitioners. The newsletters profiled below offer a solution to this problem by delivering curated digests at weekly or near-weekly intervals, filtering what matters from the constant flow of new content across the field. Each publication serves a distinct purpose, from broad data science coverage and community event notifications to AI business strategy and statistical foundations, allowing readers to select resources that match their specific interests, whether technical depth, practical application, career development or strategic awareness. What follows examines what each newsletter offers, who benefits most from subscribing, and what limitations or trade-offs readers should consider when choosing which digests merit a place in their inbox.
Launched in 2014 by Lon Reisberg, this newsletter distinguishes itself through expert curation with minimal hype. It maintains strong editorial consistency and neutrality, presenting a handful of carefully selected articles that genuinely matter rather than overwhelming subscribers with dozens of links. The free version delivers this curated digest, whilst the Pro tier (fifty dollars annually) offers searchable archives spanning over 250 issues back to 2019, plus AI-powered learning tools including a SQL tutor and interview coach. The newsletter's defining characteristic is its quality-over-quantity approach, serving professionals who trust expert curation to surface what is genuinely important without the noise and hype that characterises many industry publications.
Data Science Weekly Newsletter
One of the oldest independent data science newsletters, having published over 400 issues since 2014, this publication sets itself apart through longevity and unwavering consistency. It delivers every Thursday without fail, maintaining a simple, distraction-free format with no over-commercialisation or fluff. Its unique value lies in this dependability, with subscribers knowing exactly what to expect each week, making it a practical baseline for staying current without surprises or dramatic shifts in editorial direction.
Unlike newsletters that simply curate external content, this publication builds its own ecosystem of learning resources, offering something fundamentally different through its open, community-driven approach. It combines free courses (Zoomcamps), events and a supportive Slack community, with all materials publicly available on GitHub. The newsletter keeps members informed about upcoming cohorts, webinars and talks within this collaborative environment. The defining feature is its entirely open and peer-supported approach, where readers gain access not just to information, but to hands-on learning opportunities and a community of practitioners willing to help each other grow.
Founded in 1997 by Gregory Piatetsky-Shapiro, this publication stands apart through industry authority spanning nearly three decades. It holds unmatched credibility through its longevity and comprehensive coverage, known for its annual software polls, data science career resources and balanced mix of expert articles, surveys and tool trends that appeal equally to technical practitioners and managers seeking a global overview of the field. What sets it apart is this authoritative position, with few publications able to match its track record or breadth of influence across both technical and strategic aspects of data science and AI.
Connected to the Open Data Science Conference network, this newsletter distinguishes itself as the gateway to the global data science event ecosystem. It serves as the practitioner's bridge to events, training, webinars and conferences worldwide. It covers the full stack, from tutorials and research to business use cases and career advice, but its distinctive strength lies in connecting readers to the broader data science community through live events and practical learning opportunities. The defining characteristic is this conference-linked, community-rich approach, proving especially valuable for professionals who want to remain active participants in the field rather than passive consumers of content.
Maintaining a unique position by focusing entirely on statistical foundations, Whilst most data science newsletters chase the latest AI developments, it maintains an unwavering focus on statistics and foundational analysis, providing step-by-step tutorials for Excel, R and Python that emphasise statistical intuition over trendy techniques. This singular focus on fundamentals makes it unique, serving as an essential complement to AI-focused newsletters and helping readers build the statistical knowledge base that underpins sound data science practice.
Created by the makers of KDnuggets, this digital newsletter and media platform carves out a distinctive niche with business-focused AI news for non-technical leaders. It curates AI developments specifically for executives and decision-makers, emphasising practical, non-technical insights about tools, regulations and market moves, backed by an AI tool database and a claimed community of over 400,000 subscribers. What sets it apart is this strategic, implementation-focused perspective, concentrating on what AI means for business strategy rather than explaining how AI works, making it accessible to leaders without deep technical backgrounds.
Published weekly by DeepLearning.AI, co-founded by Andrew Ng, this newsletter offers trusted commentary that combines AI news with insightful analysis. Written by leading experts, it provides a balanced view that merges academic grounding with applied, real-world context. The distinguishing feature is this authoritative perspective on implications, helping engineers, product teams and business leaders understand why developments matter and how to think about their practical impact rather than simply reporting what happened.
An AI email newsletter roundup: Cutting through the noise
23rd August 2025This time last year, I felt out of the loop on all things AI. That was put to rights during the autumn when I experimented a lot with GenAI while enhancing travel content on another portal. In addition, I subscribed to enough email newsletters that I feel the need to cull them at this point. Maybe I should use a service like Kill the Newsletter to consolidate things into an RSS feed instead; that sounds like an interesting option for dealing with any overload.
So much is happening in this area that it is too easy to feel overwhelmed by what is happening. That sense got me compiling the state of things in a previous post using some help from GenAI, though I was making the decisions about what was being consolidated and how it was being done. The whole process took a few hours, an effort clearly beyond a single button push.
This survey is somewhat eclectic in its scope; two of the newsletters are hefty items, while others include brevity as part of their offer. Regarding the latter, I found strident criticism of some of them (The Rundown and Superhuman are two that are mentioned) in an article published in the Financial Times, which is behind a paywall. Their content has been called slop, with the phrase slopaganda being coined and used to describe this. That cannot be applied everywhere, though. Any brevity cannot cloak differences in tone and content choices can help with developing a more rounded view of what is going on with AI.
This newsletter came to my notice because I attended SAS Innovate on Tour 2025 in London last June. Oliver Patel, who authors this and serves as Enterprise AI Governance Lead at AstraZeneca as well as contributing to various international organisations including the OECD Expert Group on AI Risk and Accountability, was a speaker with the theme of his talk naturally being AI governance as well as participating in an earlier panel on the day. Unsurprisingly, the newsletter also got a mention.
It provides in-depth practical guidance on artificial intelligence governance and risk management for professionals working in enterprise environments, though not without a focus on scaling governance frameworks across organisations. Actionable insights are emphasised in place of theoretical concepts, covering areas such as governance maturity models that progress from nascent stages through to transformative governance, implementation strategies and leadership approaches needed to drive effective AI governance within companies.
Patel brings experience from roles spanning policy work, academia and privacy sectors, including positions with the UK government and University College London, which informs his practical approach to helping organisations develop robust AI governance structures. The newsletter targets AI governance professionals, risk managers and executives who need clear, scalable solutions for real-world implementation challenges, and all content remains freely accessible to subscribers.
Unlike other newsletters featured here, this is a seven-day publication that delivers a five‑minute digest on AI industry happenings each day that combines news, productivity tips, polls and AI‑generated art. It was launched in June 2023 by Matt Village and Adam Biddlecombe, using of beehiiv’s content‑focused platform that was acquired by HubSpot in March 2025, placing it within the HubSpot Media Network.
Created by Zain Kahn and based in Toronto, weekday issues of this newsletter typically follow a structured format featuring three AI tools for productivity enhancement, two significant AI developments and one quick tutorial to develop practical skills. On Saturdays, there is a round-up on what is happening in robotics, while the Sunday issue centres on developments in science. Everything is crafted to be brief, possibly allowing a three-minute survey of latest developments.
The Artificially Intelligent Enterprise
My interest in the world of DevOps led me to find out about Mark Hinkle, the solopreneur behind Peripety Labs and his in-depth weekly newsletter published every Friday that features comprehensive deep dives into strategic trends and emerging technologies. This has been complemented by a shorter how-to version which focusses on concrete AI lessons and implementation tips and comes out every Tuesday, taking forward a newsletter acquired from elsewhere. The idea is that we should concentrate on concrete AI lessons and implementation tips in place of hype, particularly in business settings. These forms part of The AIE Network alongside complementary publications including AI Tangle, AI CIO and AI Marketing Advantage.
Found though my following the Artificially Intelligent Enterprise, this daily newsletter delivers artificial intelligence developments and insights within approximately five minutes of reading time per issue. Published by Rowan Cheung, it covers key AI developments, practical guides and tool recommendations, with some articles spanning technology and robotics categories. Beyond the core newsletter, the platform operates AI University, which provides certificate courses, implementation guides, expert-led workshops and community networking opportunities for early adopters.
From boardroom to code: More options for AI and Data Science education
27th July 2025The artificial intelligence revolution has created an unprecedented demand for education that spans from executive strategy to technical implementation. Modern professionals face the challenge of navigating a landscape where understanding AI's business implications proves as crucial as mastering its technical foundations. This comprehensive examination explores five distinguished programmes that collectively address this spectrum, offering pathways for business professionals, aspiring data scientists and technical specialists seeking advanced expertise.
- Strategic Business Implementation Through Practical AI Tools
LinkedIn Learning's Applying Generative AI as a Business Professional programme represents the entry point for professionals seeking immediate workplace impact. This focused five-hour curriculum across six courses addresses the practical reality that most business professionals need functional AI literacy rather than technical mastery. The programme emphasises hands-on application of contemporary tools including ChatGPT, Claude and Microsoft Copilot, recognising that these platforms have become integral to modern professional workflows.
The curriculum's strength lies in its emphasis on prompt engineering techniques that yield immediate productivity gains. Participants learn to craft effective queries that consistently produce useful outputs, a skill that has rapidly evolved from novelty to necessity across industries. The programme extends beyond basic tool usage to include strategies for creating custom GPTs without programming knowledge, enabling professionals to develop solutions that address specific organisational challenges.
Communication enhancement represents another critical component, as the programme teaches participants to leverage AI for improving written correspondence, presentations and strategic communications. This practical focus acknowledges that AI's greatest business value often emerges through augmenting existing capabilities rather than replacing human expertise. The inclusion of critical thinking frameworks for AI-assisted decision-making ensures that participants develop sophisticated approaches to integrating artificial intelligence into complex business processes.
- Academic Rigour Meets Strategic AI Governance
The University of Pennsylvania's AI for Business Specialisation on Coursera elevates business AI education to an academic level whilst maintaining practical relevance. This four-course programme, completed over approximately four weeks, addresses the strategic implementation challenges that organisations face when deploying AI technologies at scale. The curriculum's foundation in Big Data fundamentals provides essential context for understanding the data requirements that underpin successful AI initiatives.
The programme's exploration of machine learning applications in marketing and finance demonstrates how AI transforms traditional business functions. Participants examine customer journey optimisation techniques, fraud prevention methodologies and personalisation technologies that have become competitive necessities rather than optional enhancements. These applications receive thorough treatment that balances technical understanding with strategic implications, enabling participants to make informed decisions about AI investments and implementations.
Particularly valuable is the programme's emphasis on AI-driven people management practices, addressing how artificial intelligence reshapes human resources, talent development and organisational dynamics. This focus acknowledges that successful AI implementation requires more than technological competence; it demands sophisticated understanding of how these tools affect workplace relationships and employee development.
The specialisation's coverage of strategic AI governance frameworks proves especially relevant as organisations grapple with ethical deployment challenges. Participants develop comprehensive approaches to responsible AI implementation that address regulatory compliance, bias mitigation and stakeholder concerns. This academic treatment of AI ethics provides the foundational knowledge necessary for creating sustainable AI programmes that serve both business objectives and societal responsibilities.
- Industry-Standard Professional Development
IBM's Data Science Professional Certificate represents a bridge between business understanding and technical proficiency, offering a comprehensive twelve-course programme designed for career transition. This four-month pathway requires no prior experience whilst building industry-ready capabilities that align with contemporary data science roles. The programme's strength lies in its integration of technical skill development with practical application, ensuring graduates possess both theoretical knowledge and hands-on competency.
The curriculum's progression from Python programming fundamentals through advanced machine learning techniques mirrors the learning journey that working data scientists experience. Participants gain proficiency with industry-standard tools including Jupyter notebooks, GitHub and Watson Studio, ensuring familiarity with the collaborative development environments that characterise modern data science practice. This tool proficiency proves essential for workplace integration, as contemporary data science roles require seamless collaboration across technical teams.
The programme's inclusion of generative AI applications reflects IBM's recognition that artificial intelligence has become integral to data science practice rather than a separate discipline. Participants learn to leverage AI tools for data analysis, visualisation and insight generation, developing capabilities that enhance productivity whilst maintaining analytical rigour. This integration prepares trainees for data science roles that increasingly incorporate AI-assisted workflows.
Real-world project development represents a crucial component, as participants build comprehensive portfolios that demonstrate practical proficiency to potential employers. These projects address authentic business challenges using genuine datasets, ensuring that participants can articulate their capabilities through concrete examples.
- Advanced Technical Mastery Through Academic Excellence
Andrew Ng's Machine Learning Specialisation on Coursera establishes the technical foundation for advanced AI practice. This three-course programme, completed over approximately two months, provides comprehensive coverage of core machine learning concepts whilst emphasising practical implementation skills. Andrew Ng's reputation as an AI pioneer lends exceptional credibility to this curriculum, ensuring that participants receive instruction that reflects both academic rigour and industry best practices.
The specialisation's treatment of supervised learning encompasses linear and logistic regression, neural networks and decision trees, providing thorough grounding in the algorithms that underpin contemporary machine learning applications. Participants develop practical proficiency with Python, NumPy and scikit-learn, gaining hands-on experience with the tools that professional machine learning practitioners use daily. This implementation focus ensures that theoretical understanding translates into practical capability.
Unsupervised learning includes clustering algorithms, anomaly detection techniques and certain approaches in recommender systems, all of which contribute to powering modern digital experiences. The programme's exploration of reinforcement learning provides exposure to the techniques driving advances in autonomous systems and game-playing AI. This breadth ensures that participants understand the full spectrum of machine learning approaches, rather than developing narrow expertise in specific techniques.
- Cutting-Edge Deep Learning Applications
Again available through Coursera, Andrew Ng's Deep Learning Specialisation extends technical education into the neural network architectures that drives contemporary AI. This five-course programme, spanning approximately three months, addresses the advanced techniques that enable computer vision, natural language processing and complex pattern recognition applications. The intermediate-level curriculum assumes foundational machine learning knowledge whilst building expertise in cutting-edge methodologies.
Convolutional neural network coverage provides comprehensive understanding of computer vision applications, from image classification through object detection and facial recognition. Participants develop practical skills with CNN architectures that power visual AI applications across industries. The programme's treatment of recurrent neural networks and LSTMs addresses sequence processing challenges in speech recognition, machine translation and time series analysis.
The specialisation's exploration of transformer architectures proves particularly relevant given their central role in large language models and natural language processing breakthroughs. Participants gain understanding of attention mechanisms, transfer learning techniques and the architectural innovations that enable modern AI capabilities. This coverage ensures they understand the technical foundations underlying contemporary AI advances.
Real-world application development represents a crucial component, as participants work on speech recognition systems, machine translation applications, image recognition tools and chatbot implementations. These projects utilise TensorFlow, a dominant framework for deep learning development, ensuring that graduates possess practical experience with production-ready tools.
- Strategic Integration and Future Pathways
These five programmes collectively address the comprehensive skill requirements of the modern AI landscape, from strategic business implementation through advanced technical development. The progression from practical tool usage through academic business strategy to technical mastery reflects the reality that successful AI adoption requires capabilities across multiple domains. Organisations benefit most when business leaders understand AI's strategic implications, whilst technical teams possess sophisticated implementation capabilities.
The integration of business strategy with technical education acknowledges that artificial intelligence's transformative potential emerges through thoughtful application rather than technological sophistication alone. These programmes prepare professionals to contribute meaningfully to AI initiatives regardless of their specific role or technical background, ensuring that organisations can build comprehensive AI capabilities that serve both immediate needs and long-term strategic objectives.
The critical differences between Generative AI, AI Agents, and Agentic Systems
9th April 2025
The distinction between three key artificial intelligence concepts can be explained without technical jargon. Here then are the descriptions:
- Generative AI functions as a responsive assistant that creates content when prompted but lacks initiative, memory or goals. Examples include ChatGPT, Claude and GitHub Copilot.
- AI Agents represent a step forward, actively completing tasks by planning, using tools, interacting with APIs and working through processes independently with minimal supervision, similar to a junior colleague.
- Agentic AI represents the most sophisticated approach, possessing goals and memory while adapting to changing circumstances; it operates as a thinking system rather than a simple chatbot, capable of collaboration, self-improvement and autonomous operation.
This evolution marks a significant shift from building applications to designing autonomous workflows, with various frameworks currently being developed in this rapidly advancing field.
Claude Projects: Reusing your favourite AI prompts
28th March 2025Some things that I do with Anthropic Claude, I end up repeating. Generating titles for pieces of text or rewriting text to make it read better are activities that happen a lot. Others would include the generation of single word previews for a piece or creating a summary.
Python or R scripts come in handy for summarisation, either for a social media post or for introduction into other content. In fact, this is how I go much of the time. Nevertheless, I found another option: using Projects in the Claude web interface.
These allow you to store a prompt that you reuse a lot in the Project Knowledge panel. Otherwise, you need to supply a title and a description too. Once completed, you just add your text in there for the AI to do the rest. Title generation and text rewriting already are set up like this, and keywords could follow. It is a great way to reuse and refine prompts that you use a lot.
Automating writing using R and Claude
16th April 2024Automation of writing using AI has become prominent recently, especially since GPT came to everyone's notice. It is more than automation of proofreading but of producing the content itself, as Mark Hinkle and Luke Matthews can testify. Figuring out how to use Generative AI needs more than one line prompts, so knowing what multi-line ones will work is what is earning six digit annual salaries for some.
Recently, I gave this a go when writing a post that used content from a Reddit post thread. The first step was to extract the content from the thread, and I found that I could use R to do this. That meant installing the RedditExtractoR package using the following command:
install.packages("RedditExtractoR")
Then, I created a short script containing the following lines of code with placeholders added in place of the actual locations:
library("RedditExtractoR")
write.csv(get_thread_content("<URL for Reddit post thread>"), "<location of text file>")
The first line above calls the RedditExtractoR package for use so that its get_thread_content function could be used to scape the thread's textual content. This was then fed to write.csv for writing out a text file with content.
Once I had the text file, I could upload it to Anthropic's Claude for the next steps. Firstly, I got it to give me a summary of the thread discussion before I asked it to give me the suggested solutions to the issue. Impressively, it capably provided me with the latter.
Now armed with these answers, I set to crafting the post from them. Claude did not do all the work for me, but it certainly helped with the writing. This is something that I fancy exploring further, especially given how business computing is likely to proceed in the next few years.
A little bit of abstraction
21st August 2021
Data science has remained in my awareness since 2017 though my work is more on its fringes in clinical research. In fact, I have been involved more in the standardisation and automation of more traditional data reporting than in the needs of data modelling such as data engineering or other similar disciplines. Much of this effort has meant the use of SAS, with which I have programmed since 2000 and for which I have a licence (an expensive commodity, it has to be said), but other technologies are being explored with R, Python and Julia being among them.
Though the change in technological scope does bring an element of excitement and new interest, there is also some sadness when tried and trusted technologies meet with newer competition and valued skills are no longer as career securing as they once were. Still, there is plenty of online training out there, and I already have collected some of my thoughts on this. The learning continues and the need for repositioning is also clear.
The journey also brought some curios to my notice. One of these is This Person Does Not Exist, a website building photos of non-existent faces using machine learning. Recently, I learned of others like it such as This Artwork Does Not Exist, This Cat Does Not Exist, This Horse Does Not Exist, and This Chemical Does Not Exist. The last of these probably should be entitled "This Molecule Does Not Exist (Yet)" since it is a fictitious molecular structure that has been created and what you get is an actual moving image that spins it around in three-dimensional space. The one with dynamically generated abstract art is the main inspiration for this piece and is of more interest to me, while the other two are more explanatory, though the horse website is not so successful in its execution and one can ask why we need more cat pictures.
To some, the idea of creating fake pictures may feel a little foreboding, and that especially applies to photos of people and the livelihoods of any content creators. Nevertheless, these sources of imagery have their legitimate uses, such as decorating websites or brochures, which is where my interest is piqued. After all, there are some subjects where pictures can be scarce, so any form of decoration that enlivens an article has to have some use. While technology websites like this one can feature images too with screenshots and device photos being commonplace, they can all look like each other, hence the need for a little more variety and having pictures often increases the choice of website themes as well since so many need images to make them work or stand out. As ever, being sparing with any innovations remains in order, which is how I approach this matter as well.