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
I’m a senior data scientist at Amazon Web Services, based in the San Francisco Bay Area. I graduated with an MPhil from the University of Cambridge. My past stints include statistics tutoring at the University of Michigan (Ann Arbor), research with Disney’s behavioral sciences team, and analyzing data to inform public policy for the Singapore government.
I am fluent in English and Mandarin Chinese, and can converse in Japanese—a skill gleaned from watching too much anime. I was also a competitive archer representing Singapore in regional competitions.
Kenneth Soo
I completed my MS in Statistics at Stanford University, and was the top student for all 3 years of my MORSE degree at the University of Warwick (MORSE: Mathematics, Operational Research, Statistics, and Economics).
At University of Warwick, I was a research assistant with the Operational Research & Management Sciences Group working on bi-objective robust optimization with applications in networks subject to random failures.
I am interested in data analytics, and I am constantly honing my skills through reading and practice. From 2015 to 2016, I participated in a series of 4 data-related competitions organized by the Ministry of Defence (Singapore), Monetary Authority of Singapore, Singtel, and ConneXionsAsia, developing machine learning models as well as privacy protection methods, eventually placing podium finishes (top 3) for all of them.
I enjoy traveling and have visited over 20 countries by the age of 23. I hope to use my skills to enhance policy-making in the government and improve the lives of ordinary citizens.
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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