Every good syllabus has a list of reading recommendations – but all good syllabi have different ones. In the last days, I had a closer look at them. Why? First, I wanted to understand which books my student should read to grasp the idea of data vis most efficiently, without any knowledge or experience in this field. I happened to gain some of both in the past years, so I will never fully understand how it is to be a student with no idea about data vis in the year 2015. But secondly, I also want to deepen my own knowledge about the field. I guess every person in the data vis field thinks about that from time to time: “I need to understand better what data vis is all about.” So what should they read next?

I want to answer that question with a spreadsheet that you can find here: Click to see it!. I had a look at syllabi by Scott Murray, Tamara Munzner and Alexander Lex as well as online reading lists by Alberto Cairo and Andy Kirk. All the books they recommend can be found in the spreadsheet. Scott Murray recommends only some chapters from some books, and they can be found there too; as well as Andy Kirks categorization of the books he puts on his list.

As much as I liked to see which books got recommend by most of these experts, I also wanted to check what the big crowd out there thought about all these books. With the help of kimonolabs (which is great for scraping websites in a very easy, non-programming way) I got data from Amazon about customer reviews, page number, bestseller rank, price etc. for all these books. So now one could answer the question: If I want to read as little as possible (page number) for as little money as possible (price), but with the highest approval from the experts (number of mentions in the five syllabi) and the crowd (number of customer reviews + average stars they give) and with the highest reach possible (bestseller rank)(so that other people I’ll talk to will have read that book, too) – what should I read?

Some supplementary notes about this spreadsheet:

• This sheet is editable. You’re more then welcome to add your own column with what books you would recommend, as well as additional book (data)! See that spreadsheeet as a wiki.
• The green cells are the top 15 values of each column; the red cells are the bottom 15 values. This color coding doesn’t come with a judgment: For example, old books are shown in red, but are as important as the new ones in my opinion.
• The opinions of the “experts” shouldn’t be taken too seriously. Some of them wrote their reading list months or years ago and didn’t include the newest books. Also, none of these experts have read ALL of the books – if so, they would have maybe had another opinion on what to recommend. Scott Murray also pointed out that this list is only for a student audience, not for the general data vis community. For me, the mentions in the reading experts don’t answer so much the question “Which data vis books are best?”, but rather: “Which data vis books shape the minds of future data vis people, aka students?”
• The Amazon links are partner links, so I will get a tiny amount of money when you buy something – that I will spend to buy more data vis books, of course!
• Amazon scraping seems to be tricky, so there is missing data. Multiple authors are unfortunately not considered and the dates (year) are from the latest edition, not from the first one. You’re welcome to update the data.
• The Amazon scraping is from the 1st March of 2015. Numbers will be outdated very quickly. I hope they won’t change 180 degree, but will still be a good indicator in a month or so.
• Some syllabi recommend papers; these are not included. I’d love to see such a “papers that are important for data vis”-list, though! Maybe I will work on this in the future.