Algobeans is authored by Annalyn Ng (University of Cambridge) and Kenneth Soo (Stanford University). We noticed that while data science is increasingly used to improve workplace decisions, many people do not have a good understanding of how it works. Our goal is to support anyone who wants to get started in data science. Each tutorial covers the important functions and assumptions of a data science technique, without any math or jargon. We also illustrate these techniques with real-world data and examples.
Our posts are our own, and do not represent the views of our past/present employers.
Annalyn Ng
Annalyn Ng is an AI/ML specialist at Google Cloud. Her data science career spans across commercial and public sectors; she has held roles in Amazon, Disney Research, as well as in the Singapore government, specifically in the manpower and military ministries. Annalyn earned her bachelor’s degree from the University of Michigan (Ann Arbor), where she also volunteered as an undergraduate statistics tutor, and subsequently completed her MPhil degree at the University of Cambridge.
Kenneth Soo
Kenneth Soo has 7 years of experience applying data science to public policy for the Singapore government, and he currently leads a team managing bilateral relations with European partners. During the COVID-19 pandemic, he drove nationwide digitalisation initiatives for Singapore’s Smart Nation and Digital Government Office, including the implementation of contact tracing systems. He completed his MS degree in Statistics at Stanford University, and he was the top student for all three years of his undergraduate class in Mathematics, Operational Research, Statistics and Economics (MORSE) at the University of Warwick.
Pssst… We also wrote a book. Numsense! Data Science for the Layman (no math added) has been used by top universities like Stanford and Cambridge as introductory reference text. Grab it at just $3.99 on Amazon.
Algobeans 
Hi Annalyn and Kenneth, just wanted to let you know that I’ve several business friends who read your book — and absolutely loved it! They are now able to identify which algorithms to use for different problems. To explain things like decision trees or regression to non-math majors is not easy. Thank you for your fantastic tutorials that help more people level up in this field.
In response to Emanuele’s comment below, I’ve no doubt that if the book was indeed filled with stats and data munging, no layman would last beyond the first chapter. This book’s appeal is in its tactful simplicity.
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hi,
I bought your book, and after having completed the read, I think it’s very good as analytcs book but far from data science. In the book there are very poor information about statistic, data munging and visualization. Examples are useful just for machine learning, but rarely they are really connected to problems of data science.
Hi Emanuele and Sorry for my English
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Hi Emanuele, thanks for your feedback! Yes, as stated in our description, our book is best for beginners, or business managers who just need to understand the concepts. We provide a comprehensive introduction, which has been chosen by schools such as Stanford and Cambridge as reference text for their students. If you’re looking for a full tutorial on executing analyses, coding visuals, or cleaning data, there are many other excellent books on the market. Feel free to email us if you need recommendations 🙂
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First of all, I appreciated your book, so I’m glad you suggested me other books.
My previous message is not a criticism, but a suggestion to improve the next version of the book because I think the data science covers not only analytic topics, but it is also necessary to show all the processes of data science, with the same descriptive level used on machine learning algorithms about other topics. Such as “data quality”, the technique to get it and a correct way to display visualization to a good communication.
Thank you and Sorry for my English
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Hi Emanuele, certainly! We’re always thinking of new topics to cover and we value your suggestions. Indeed a large part of actual data analysis is data cleaning and communicating findings.
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Hi, I just got this as a prospective text to use on a PG Data Journalism course, I’ve found the R repo but is there any other teaching material, slides etc available?
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great materials, thank you…. want to do one that helps to guide which methods/approaches are more relevant in which circumstances? just an idea – a “What method/approach should i use?’ tutorial for the layman (like me)?
thanks again!
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maybe a simplified version of something like this?
http://scikit-learn.org/dev/tutorial/machine_learning_map/index.html
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What a superb idea, thank you!
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This is pretty cool.. Thanks for sharing! 🙂
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@me from NYC: Something on similar lines that you might like or find helpful: http://blog.kaggle.com/2016/07/21/approaching-almost-any-machine-learning-problem-abhishek-thakur/
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Very useful, thanks Naveen you’re great!
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