TL;DR: a nice popular science book that covers many aspects of the modern science
A Short History of Nearly Everything by Bill Bryson is a popular science book. I didn’t learn anything fundamental out of this book, but it was worth reading. I was particularly impressed by the intrigues, lies, and manipulations behind so many scientific discoveries and discoverers.
The main “selling point” of this book is that it answers the question, “how do the scientists know what they know”? How, for example, do we know the age of Earth or the skin color of the dinosaurs? The author indeed provides some insight. However, because the book tries to talk about “nearly everything,” the answer isn’t focused enough. Simon Singh’s book “Big Bang” concentrates on the cosmology and provides a better insight into the question of “how do we know what we know.”
Interesting takeaways and highlights
Of the problem that our Universe is unlikely to be created by chance: “Although the creation of Universe is very unlikely, nobody knows about failed attempts.”
The Universe is unlimited but finite (think of a circle)
Developments in chemistry were the driving force of the industrial revolution. Nevertheless, chemistry wasn’t recognized as a scientific field in its own for several decades
Yesterday, a new episode was published in the Popcorn podcast, where the host, Lior Frenkel, interviewed me. Everyone who knows me knows how much I love talking about myself and what I do. I definitely used this opportunity to talk about the world of data. Some people who listened to this episode told me that they enjoyed it a lot. If you know Hebrew, I recommend that you listen to this episode
In June 2019, I attended the NDR AI conference in Iași, Romania where I also gave a talk. Recently, the organizers uploaded the video recording to YouTube.
That was a very interesting conference, tight with interesting talks.
Next year, I plan to attend the Bucharest edition of NDR, where I will also give a talk with the working title “The biggest missed opportunity in data visualization”
One month ago, I stumbled upon a book called “Personal Kanban: Mapping Work | Navigating Life” by Jim Benson (all the book links use my affiliate code). Never before, I saw a more significant discrepancy between the value that the book gave me and its actual content.
Even before finishing the first chapter of this book, I realized that I wanted to incorporate “personal kanban” into my productivity system. The problem was that the entire book could be summarized by a blog post or by a Youtube video (such as this one). The rest of the book contains endless repetitions and praises. I recommend not reading this book, even though it strongly affected the way I work
So, what is Personal Kanban anyhow? Kanban is a productivity approach that puts all the tasks in front of a person on a whiteboard. Usually, Kanban boards are physical boards with post-it notes, but software Kanban boards are also widely known (Trello is one of them). Following are the claims that Jim Benson makes in his book that resonated with me
Many productivity approaches view personal and professional life separately. The reality is that these two aspects of our lives are not separate at all. Therefore, a productivity method needs to combine them.
Having all the critical tasks in front of your eyes helps to get the global picture. It also helps to group the tasks according to their contexts.
The act of moving notes from one place to another gives valuable tangible feedback. This feedback has many psychological benefits.
One should limit the number of work-in-progress tasks.
There are three different types of “productivity.” You are Productive when you work hard. You are Efficient when your work is actually getting done. Finally, you are Effective when you do the right job at the right time, and can repeat this process if needed.
I’m a long user of a productivity method that I adopted from Mark Forster. You may read about my process here. Having read Personal Kanban, I decided to combine it with my approach. According to the plan, I have more significant tasks on my Kanban board, which I use to make daily, weekly, and long-term plans. For the day-to-day (and hour-to-hour) taks, I still use my notebooks.
Initially, I used my whiteboard for this purpose, but something wasn’t right about it.
Having my Kanban on my home office whiteboard had two significant drawbacks. First, the whiteboard isn’t with me all the time. And what is the point of putting your tasks on board if you can’t see it? Secondly, listing everything on a whiteboard has some privacy issues. After some thoughts, I decided to migrate the Kanban to my notebook.
In this notebook, I have two spreads. The first spread is used for the backlog, and “this week” taks. The second spread has the “today,” “doing,” “wait,” and “done” columns. The fact that the notebook is smaller than the whiteboard turned out to be a useful feature. This physical limitation limits the number of tasks I put on my “today” and “doing” lists.
I organize the tasks at the beginning of my working day. The rest of the system remains unchanged. After more than a month, I’m happy with this new tangible productivity method.
A Quora user asked about data science tools with a graphical user interface. Here’s my answer. I should mention though that I don’t usually use GUI for data science. Not that I think GUIs are bad, I simply couldn’t find a tool that works well for me.
Of the many tools that exist, I like the most Orange (https://orange.biolab.si/). Orange allows the user creating data pipelines for exploration, visualization, and production but also allows editing the “raw” python code. The combination of these features makes is a powerful and flexible tool.
The major drawback of Orange (in my opinion) is that is uses its own data format and its own set of models that are not 100% compatible with the Numpy/Pandas/Sklearn ecosystem.
I work at Automattic, one of the largest distributed companies in the world. Working in a distributed company means that everybody in this company works remotely. There are currently about one thousand people working in this company from about seventy countries. As you might expect, the international nature of the company poses a communication challenge. Recently, I had a fun experience that demonstrates how different people are.
Remote work means that we use text as our primary communication tool. Moreover, since the company spans over all the time zones in the world, we mostly use asynchronous communication, which takes the form of posts in internal blogs. A couple of weeks ago, I completed a lengthy analysis and summarized it in a post that was meant to be read by the majority of the company. Being a responsible professional, I asked several people to review the draft of my report.
To my embarrassment, I discovered that I made a typo in the report title, and not just a typo: I misspelled the company name :-(. A couple of minutes after asking for a review, two of my coworkers pinged me on Slack and told me about the typo. One message was, “There is a typo in the title.” Short, simple, and concise.
The second message was much longer.
Do you want to guess what the difference between the two coworkers is? . . . . . Here’s the answer . . . . The author of the first (short) message grew up and lives in Germany. The author of the second message is American. Germany, United States, and Israel (where I am from) have very different cultural codes. Being an Israeli, I tend to communicate in a more direct and less “sweetened” way. For me, the American communication style sounds a little bit “artificial,” even though I don’t doubt the sincerity of this particular American coworker. I think that the opposite situation is even more problematic. It happened several times: I made a remark that, in my opinion, was neutral and well-intended, and later I heard comments about how I sounded too aggressive. Interestingly, all the commenters were Americans.
To sum up. People from different cultural backgrounds have different communication styles. In theory, we all know that these differences exist. In practice, we usually are unaware of them.
This is another “because you can” rant, where I claim that the fact that you can do something doesn’t mean that you necessarily need to.
This time, I will claim that sometimes, you don’t really need a legend in your graph. Let’s take a look at an example. We will plot the GDP per capita for three countries: Israel, France, and Italy. Plotting three lines isn’t a tricky task. Here’s how we do this in Python
The last line in the code above does a small magic and adds a nice legend
In Excel, we don’t even need to do anything, the legend is added for us automatically.
So, what is the problem?
What happens when a person wants to know which line represents which country? That person needs to compare the line color to the colors in the legend. Since our working memory has a limited capacity, we do one of the following. We either jump from the graph to the legends dozens of times, or we try to find a heuristic (a shortcut). Human brains don’t like working hard and always search for shortcuts (I recommend reading Daniel Kahneman’s “Think Fast and Slow” to learn more about how our brain works).
What would be the shortcut here? Well, note how the line for Israel lies mostly below the line for Italy which lies mostly below the line for France. The lines in the legend also lie one below the other. However, the line order in these two pieces of information isn’t conserved. This results in a cognitive mess; the viewer needs to work hard to decipher the graph and misses the point that you want to convey.
And if we have more lines in the graph, the situation is even worse.
Can we improve the graph?
Yes we can. The simplest way to improve the graph is to keep the right order. In Python, we do that by reordering the plotting commands.
We still have to work hard but at least we can trust our brain’s shortcut.
If we have more time
If we have some more time, we may get rid of the (classical) legend altogether.
countries = [c for c in gdp.columns if c != 'Year']
fig, ax = plt.subplots()
for i, c in enumerate(countries):
ax.plot(gdp.Year, gdp[c], '-', color=f'C{i}')
x = gdp.Year.max()
y = gdp[c].iloc[-1]
ax.text(x, y, c, color=f'C{i}', va='center')
seaborn.despine(ax=ax)
(if you don’t understand the Python in this code, I feel your pain but I won’t explain it here)
Isn’t it better? Now, the viewer doesn’t need to zap from the lines to the legend; we show them all the information at the same place. And since we already invested three minutes in making the graph prettier, why not add one more minute and make it even more awesome.
This graph is much easier to digest, compared to the first one and it also provides more useful information.
.
I agree that this is a mess. The life is tough. But if you have time, you can fix this mess too. I don’t, so I won’t bother, but Randy Olson had time. Look what he did in a similar situation.
What do we see when we look at slices of a pie chart? Angles? Areas? Arc length? The answer to this question isn’t clear and thus “experts” recommend avoiding pie charts at all.
Robert Kosara is a Senior Research Scientist at Tableau Software (you should follow his blog https://eagereyes.org), who is very active in studying pie charts. In 2016, Robert Kosara and his collaborators published a series of studies about pie charts. There is a nice post called “An Illustrated Tour of the Pie Chart Study Results” that summarizes these studies.
Last week, Robert published another paper with a pretty confident title (“Evidence for Area as the Primary Visual Cue in Pie Charts”) and a very inconclusive conclusion
While this study suggests that the charts are read by area, itis not conclusive. In particular, the possibility of pie chart usersre-projecting the chart to read them cannot be ruled out. Furtherexperiments are therefore needed to zero in on the exact mechanismby which this common chart type is read.
Kosara. “Evidence for Area as the Primary Visual Cue in Pie Charts.” OSF, 17 Oct. 2019. Web.
The previous Kosara’s studies had strong practical implications, the most important being that pie charts are not evil provided they are done correctly. However, I’m not sure what I can take from this one. As far as I understand the data, the answer to the questions in the beginning of this post are still unclear. Maybe, the “real answer” to these questions is “a combination of thereof”.
Lior Patcher is a researcher in Caltech. As many other researchers in the academy, Dr. Patcher is measured by, among other things, publications and their impact as measured by citations. In his post, Lior Patcher criticised both the current impact metrics and also their effect on citation patterns in the academic community.
PROBLEM POINTED: citations don’t really measure “actual” citations. Most of the appeared citations are “hit and run citations” i.e: people mention other people’s research without taking anything from that research.
In fact this author has cited [a certain] work in exactly the same way in several other papers which appear to be copies of each other for a total of 7 citations all of which are placed in dubious “papers”. I suppose one may call this sort of thing hit and run citation.
I think that the biggest problem with citation counts is that it costs nothing to cite a paper. When you add a research (or a post, for that matter) to your reference list, you know that most probably nobody will check whether actually read it, that nobody will check whether you got that publication correctly and that nobody will that the chances are super (SUUPER) low nobody will check whether you conclusions are right. All it takes is to click a button.
