Do you recall when data scientists were the talk of the town? Dubbed the sexiest job of the 21st century, they boasted a unique blend of knowledge and skills. I still remember the excitement I felt when I realized that the work I did had a name, and the warm feeling I got when I saw those cool Venn diagrams showing just how awesome data scientists were. Well, it’s time for data scientists to step aside and make way for the new heroes in town: the Prompt Engineers!

The demand for prompt engineers is soaring, and it seems like everyone is trying to become one. But what exactly is a prompt engineer, and what are my thoughts on this new profession?

Let’s take a step back in time: we started with assembly languages, and then a language called Formula Translator (better known as Fortran), which significantly lowered the barrier of entry into the field. I’m sure back then, people rolled their eyes and said that with the emergence of high-level programming languages, anyone could now take any formula and get an output, without understanding how semiconductors worked.

Fast forward to today. What do prompt engineers do? They essentially translate their domain knowledge, language understanding, and AI algorithm expertise into computer output (sounds like “ForTran,” right?). Prompt engineering is, in essence, a super-high-level programming language. Over time, I believe we’ll see dedicated tools and established standards emerge. But for now, it’s a wild, untamed frontier.

In 2017, I wrote a blog post titled “Don’t study data science as a career move; you’ll waste your time!”. Until today, this is the most read post in my blog. Now, it’s time for a new warning: “Don’t study prompt engineering as a career move; you’ll waste your time!”

Meanwhile, here’s a nice Venn diagram for you :-)

Read this to learn about well-rounded teams that can effectively collaborate and communicate. As an experienced team leader and builder, contact me to learn more about my services and how I can help you achieve better outcomes.

As a freelancer and a manager, I have worked with many companies and teams. Recently, I talked to a CEO who built a data science team that consisted of several “wonder kids” who obtained University degrees before graduating high school. The CEO was very proud of them. However, he complained that they don’t deliver as expected. This made me realize that having only superstars is not a feature but a bug.

The fact is that most of us are average, even geniuses are average in most aspects. Richard Feynman, the Nobel laureate physicist, was also a painter, musician, and an excellent teacher, but he is unique. I, for example, tend to think of myself as an excellent generalizer, leader, and communicator. However, I need help with attention to detail and deep domain-specific knowledge. To work well, I need to have pedantic specialists in my team. Why? Because, on average, I’m average.

Most “geniuses” are extremely talented in one field but still need help in others. Many tend to be individual workers, meaning their team communication is often suboptimal. Additionally, the fact that the entire team is very young also means they need more expertise in project management, inter-team communication, business orientation, or even enough real-life experience. The result: a disaster. That company got a team of solo players who don’t communicate within the team, don’t communicate with other teams, and don’t deliver on time.

What do I suggest? They say that “A’s hire A’s”. However, this doesn’t mean that each “A person” must ace the same field. A good team needs an A generalizer, an A specialist, an A communicator, and an A business expert. If you only hire “A++ specialists,” you risk ending up with a group of individuals who are “C-“ communicators.

As another CEO I consulted once told me, “genius developers can do 10x job. They also tend to enter rabbit holes, and if unattended, they can do 10x damage.” If you build a team, you cannot afford to have unbalanced expertise sets.

The bottom line is to ensure your team is diverse in its capabilities. Hiring only superstars may seem like a good idea, but it can result in a lack of collaboration, communication, and the necessary skills to succeed as a team. A diverse team with various skills and expertise is essential for achieving better outcomes.

In conclusion, avoid falling into the trap of thinking that only superstars can make a great team. Instead, focus on creating a diverse team with various skills, and you’ll be surprised at how much your team can achieve.

Read this if you are a data scientist (or another professional) worried about your career.

So many people, including me, write about how fields such as copywriting, drawing, or data science change from being accessible to a niche of highly professional individuals to a mere commodity. I claim it’s a good thing, not only for humankind but for the individual professional. Since I know nothing about drawing, I’ll talk about data science.

I started working as a data scientist a long time ago, even before the term data science was coined. Back then, my data science job included:

• writing code that implements this optimization algorithm or the other
• writing code that implements this statistical analysis or the other
• writing code that implements this machine learning technique of the other
• writing code that implements this quality metric or the other
• writing code that handles named columns
• writing code that deals with parallelization, caching, fetching data from the internet

Back then, exactly when the term data scientist was coined, I used to say “data is data”. I claimed that it didn’t matter whether you write a model that detects cancer or detects online fraud, a model that simulates two molecules in a solution or a model that simulates players in the electric appliances market. Data was data, and my job, as a data scientist was to crunch it.

Time passed by. Suddenly, I discovered one cool library, the other, and a third one … Suddenly, my job was to connect these libraries, which allowed me to be more expressive in what I could achieve. It also allowed me to concentrate better on “business logic.” Business logic is the term I use to describe all the knowledge required for the organization that pays your salary to keep doing so. If you work for a gaming company, “business logic” is the gaming psychology, competitor landscape, growth methods, and network effect. If you work for a biotech company, “business logic” is the deep understanding of disease mechanisms, biochemistry, genetics, or whatever is needed to perform the breakthrough. The fact that I don’t need to deal with “low-level coding” made me obsolete and drove me to a state where I became more specialized.

These days, we are facing a new era in knowledge commoditization. This commoditization makes our skills obsolete but also makes us more efficient in tasks that we were slow at and lets us develop new skills.

In 2017, Gartner predicted that more than 40% of data science tasks would be obsolete by 2020. Today, in 2023, I can safely say that they were right. I can also say that today, despite the recent layouts, there are much more busy data scientists than there were in 2017 or 2020.

The bottom line. Stop worrying.

Let me cite myself from 2017:

Data scientists won’t disappear as an occupation. They will be more specialized.

I’m not saying that data scientists will disappear in the way coachmen disappeared from the labor market. My claim is that data scientists will cease to be perceived as a panacea by the typical CEO/CTO/CFO. Many tasks that are now performed by the data scientists will shift to business developers, programmers, accountants and other domain owners who will learn another skill — operating with numbers using ready to use tools. An accountant can use Excel to balance a budget, identify business strengths, and visualize trends. There is no reason he or she cannot use a reasonably simple black box to forecast sales, identify anomalies, or predict churn.

This is another piece of career advice. I have more of them in my blog

Read this if you are considering hiring data scientists

I already wrote about how data science becomes a commodity.

If you read this, I guess data science is not the core part of your business. If this is the case, consider the following before you hire data scientists.

Data engineers

Your data scientists can be as good as the data you provide them. You must collect the correct data, validate it, store it well, and be able to access it easily. I have hours of “war stories” about how each component of the last message went wrong, and the company burned tons of money because of that. Data piping is a serious challenge. So, before you hire a data scientist, ask yourself whether your data engineering needs are covered.

Data analysts

Data Analysts mainly focus on the organization and interpretation of data. Unlike data scientists, Analysts don’t build predictive models or create unique algorithms. However, they identify trends and insights and present their findings clearly and understandably. Not being required to build novel models and algorithms allow them to better connect with stakeholders’ business needs and practical questions. A good data analyst will take the business problem, translate it into a data-based question, will know its potential value, and in many cases, will be able to answer it.

Boxed Solutions

Data Science as a Service is a term for boxed solutions that are constantly becoming more versatile, flexible, and affordable. I was a freelancer for a company that built its data-based product on an open-source implementation of a single optimization algorithm. They managed to run a successful company without a single data scientist for more than five years, and they started thinking of better solutions when they squeezed everything they could from their MRE. At this point, they had their data storage pipelines (data engineering), a better picture of their business (data analysts), and paying customers to finance the development of new algorithms.

How to work with data scientists?
I’ll write separate posts on this topic, but the gist is: to make sure they know your business needs. Ensure you communicate your needs and problems to them and make sure they share their efforts with you. I have seen many failed data science projects in my life. Most failed due to a lack of alignment, communication, or both.

This was another career advice post. Read more of them here.

Read this if you’re a data scientist or consider becoming one.

Almost six years ago, when Data Scientist was named the “sexiest job of the 21st century”, I wrote a blog post telling young professionals not to learn data science as a career move. My claim was that the data science field fill gets commoditized, and if you don’t possess deep (I mean DEEP) knowledge of either algorithms or the business you are working at, you will end up a mediocre coder.

Look what happened. Data science has indeed become commoditized in many fields. Many data-intence businesses work just fine without data scientists. Even I, a very experienced data scientist, got laid off because I couldn’t bring the company value that would justify my salary. People like Matthew Yglesias from https://www.slowboring.com suggest that data scientists learn how to roll a burrito or mine lithium.

Why did this happen? Well, I was right. Data science has become a commodity. Each self-respecting platform offers AI tools (I hate the term AI, by the way) such as keyword extraction, insights, predictions, anomaly detection, recommendations, and many more. Tableau, PowerBI, and even Google Sheets or Excel offer tools that were once only available through custom data and code fiddling. The Data-Science-As-A-Service niche is full of products such as https://www.pecan.ai and https://www.anodot.com. And we haven’t even started talking about the new word of the day: the GPT.

Being an experienced data scientist, people often ask for my advice and help. In the past, when this happened, I used to discuss possible custom-tailored solutions. Now, I find myself suggesting the person looking at product X or Y will solve their problems in a fraction of the time and cost.

So, what do we have? What does all that mean?

Data science has become a commodity. In the past, to get a nice salary and a sexy title, it was enough to know what training, testing, and cross-validation were. Today, you absolutely have to know the theory and be a fast and good coder. But most of all, you must hone your communication skills and learn the business of the company where you work. Only this way will you be able to ensure your efforts are always aligned with the stakeholders and that you can consistently deliver value.

This is a career advice post. Check out the career tag and the Career Advice category of this blog.

I think that I’m in love with Midjourney. Look how easy it is to be creative when you have AI at your disposal!

Tishrei is the seventh month of the Hebrew calendar that starts with Rosh-HaShana — the Hebrew New Year. It is a 30 days month that usually occurs in September-October. One interesting feature of Tishrei is the fact that it is full of holidays: Rosh-HaShana (New Year), Yom Kippur (Day of Atonement), first and last days of Sukkot (Feast of Tabernacles) **. All these days are rest days in Israel. Every holiday eve is also a *de facto rest day in many industries (high tech included). So now we have 8 resting days that add to the usual Friday/Saturday pairs, resulting in very sparse work weeks. But that’s not all: the period between the first and the last Sukkot days are mostly considered as half working days. Also, the children are at home since all the schools and kindergartens are on vacation so we will treat those days as half working days in the following analysis.

I have counted the number of business days during this 31-day period (one day before the New Year plus the entire month of Tishrei) between for a perios of several years.

Overall, this period consists of between 14 to 17 working days in a single month (31 days, mind you). This year, we only have 14 working days during the Tishrei holiday period. This is how the working/not-working time during this month looks like:

Now, having some vacation is nice, but this month is absolutely crazy. There is not a single full working week during this month. It is very similar to the constantly interrupt work day, but at a different scale.

So, next time you wonder why your Israeli colleague, customer or partner barely works during September-October, recall this post.

(*) New Year starts in the seventh’s month? I know this is confusing. That’s because we number Nissan – the month of the Exodus from Egypt as the first month.

(**)If you are an observing Jew, you should add to this list Fast of Gedalia, but we will omit it from this discussion

TL;DR Own your wins, own your failures, stay calm and make decisions. Read it. 5/5

“Extreme ownership” is a book about leadership in business written by two ex-SEAL fighters. This book is full of war stories, as in actual stories from a real war. I read this book by the recommendation (an instruction, really) of the serial entrepreneur Danny Lieberman. After three years in the Israeli Border Police and after a cumulative year-and-a-half in active IDF reserve over almost twenty years, I learned to dislike war stories strongly. Had Danny not told me, “you have to read this book,” I would have ditched it after the first couple of pages. The war stories are self-bragging, and the business case studies are oversimplified and always have a happy ending. Moreover, the connection between a war story and a business case is sometimes very artificial.

Nevertheless, I’m glad that I read this book. It has several powerful messages and shows leadership aspects that I haven’t managed to formalize in my head before.

Key points

The best leaders don’t just take responsibility for their job. They take Extreme Ownership of everything that impacts their mission. When subordinates aren’t doing what they should, leaders that exercise Extreme Ownership cannot blame the subordinates. They must first look in the mirror at themselves.

• It’s not what you preach; it’s what you tolerate

• “Relax, look around, make a call.”

This point takes me back to my days as the chief combat medic in an IDF infantry battalion (here we come, more war stories!). One day, an instructor, a very experienced paramedic, told me that the first thing a medic should do when they arrive at a scene is to take a pulse, not the pulse of the victims, but your own pulse, to make sure you’re calm and take the right decisions.

• Prioritize your problems and take care of them one at a time, the highest priority first.
• Leadership doesn’t just flow down the chain of command, but up as well.

This is a super valuable and insightful message.

The bottom line: Read it 5/5

I will skip the usual “I’m thrilled and excited…”. I’ll just say it.
As of today, I am the CTO of wizer.me, a platform for teachers and educators to create and share interactive worksheets.

On a scale of 1 to 10, how thrilled am I? 10
On a scale of 1 to 10, how terrified am I? 10
On a scale of 1 to 10, how confident am I that wizer.me will become the “next big thing” and the most significant chapter in my career? You won’t believe me, but also 10.

Today, I gave my first in-person presentation since the pandemic. It was awesome! I was talking about the study I performed with Nabeel Sulieman about data visualization in environments that use right-to-left writing systems.

I wrote about this study in the past [one, two]. Today, you may find the results of our study at http://direction-matters.com/. I hope to be able to publish the video recording of this presentation really soon.

An example of a very bad graph

Nature Medicine is a peer-reviewed journal that belongs to the very prestigious Nature group. Today, I was reading a paper that included THIS GEM.

These two graphs are so bad. It looks as if the authors had a target to squeeze as many data visualization mistakes as possible in a single piece of graphics.

Let’s take a look at the problems.

• Double Y axes. Don’t! Double axes are bad in 99% of cases (exceptions do exist, but they are rare).
• Two subgraphs that are meant to work together have different category orders and different Y-axis scales. These differences make the comparison much harder.
• Inverted Y scale in a bar chart. Wow! This is very strange. Bizarre! It took me a while to spot this. First, I tried to understand why the line of P<0.05 (the magic value of statistics) is above 0.1. Then, I realized that the right Y-axis is reversed. At first, I thought, “WTF?!” but then I understood why the authors made this decision. You see, according to the widespread statistical ritual, the lower the “P-value” is, the more significant it is considered. The value of 1 is deemed to be non-significant at all, and the value of 0 is considered “as significant as one can have.” So, in theory, the authors could have renamed the axis to “Significance” and reversed the numbers. Still, the result would not be a real “significance,” nor would the name be intuitive to anyone familiar with statistical analysis. On the other hand, they really wanted more “significant” values to be bigger than less significant ones. So, what the heck? Let’s invert the scale! Well, no, this is not a good idea
• Slanted category labels. This might be a matter of taste, but I dislike rotated and slanted labels. Turning the graph solves the need for label rotation, thus making it more readable and having zero drawbacks.

What can be done?

I don’t like criticism without improvement suggestions. Let’s see what I would have done with this graph. To make this decision, I first need to decide what I want to show. According to my understanding of the paper, the authors wish to show that the two data sets are very different in determining a specific outcome. To show that, we don’t need to depict both the P-value and variance (mainly since these two values are very much correlated). Thus, I will depict only show one metric. I will stick with the P-value.

I will keep the category order the same between the two subgraphs. Doing so will create a “table lens” effect; it will show the individual values while demonstrating the lack of correlations between the two groups. Finally, I will convert the bars into points, primarily to reduce the data-ink ratio. Two additional arguments against bar charts, in this case, are the facts that the P-values of a statistical test cannot possibly be zero and that bar charts don’t allow log-scale, in case we’ll want to use it.

The result should look like this sketch.

Haifa on Friday. Street art, atmosphere, food.

How do you properly select a colormap for a graph? What makes the rainbow color map a wrong choice, and what are the proper alternatives?

Today, I stumbled upon a lengthy post that provides an in-depth review of the theory behind our color perception. The article concentrates on quantitative colormaps but also includes information relevant to selecting proper colors for categories.

https://nightingaledvs.com/color-in-a-perceptual-uniform-way/

If you never learned the theory behind the color and are interested in data visualization, I strongly suggest investing 45-60 minutes of your life in reading this post.

TL;DR War stories and pieces of advice from the high tech industry veteran.

I read this book following recomendations by Reem Sherman, the host of the excellent (!!!) podcast Geekonomy (in Hebrew).

Ben Horowitz is a veteran manager and entrepreneur who found the company Opsware, which Hewlett-Packard acquired in 2007. This book describes Horotwitz’s journey in Opsware from the foundation to the sale. Book’s second part is a collection of advice to working and aspiring CEOs. The last part is, actually, an advertisement for Horowitz’s new project – a VC company.

Things that I liked

The behind the scenes stories are interesting and inspiring.
Ben Horowitz devoted the second part of the book to share his experience as a CEO with other actual or aspiring CEOs. I don’t work as a CEO, nor do I see myself in that position in the future. However, this part is valuable for people like me because it provides insights into how CEOs think. Moreover, “The Hard Things” is a popular book, and many managers learn from it.

Things that I didn’t like.

Ben Horowitz was a manager during the early days of the high-tech industry. As such, parts of his attitude are outdated. The most prominent example for this problem is a story that Horowitz tells, in which he asked the entire company to work 12+ hours a day, seven days a week for several months. He was very proud about this, but IMO, employees will not accept such a request in today’s climate.

The bottom line: 4/5

Tishrei is the seventh month of the Hebrew calendar that starts with Rosh-HaShana — the Hebrew New Year. It is a 30 days month that usually occurs in September-October. One interesting feature of Tishrei is the fact that it is full of holidays: Rosh-HaShana (New Year), Yom Kippur (Day of Atonement), first and last days of Sukkot (Feast of Tabernacles) **. All these days are rest days in Israel. Every holiday eve is also a *de facto rest day in many industries (high tech included). So now we have 8 resting days that add to the usual Friday/Saturday pairs, resulting in very sparse work weeks. But that’s not all: the period between the first and the last Sukkot days are mostly considered as half working days. Also, the children are at home since all the schools and kindergartens are on vacation so we will treat those days as half working days in the following analysis.

I have counted the number of business days during this 31-day period (one day before the New Year plus the entire month of Tishrei) between for a perios of several years.

Overall, this period consists of between 14 to 17 working days in a single month (31 days, mind you). This year, we only have 14 working days during the Tishrei holiday period. This is how the working/not-working time during this month looks like:

Now, having some vacation is nice, but this month is absolutely crazy. There is not a single full working week during this month. It is very similar to the constantly interrupt work day, but at a different scale.

So, next time you wonder why your Israeli colleague, customer or partner barely works during September-October, recall this post.

(*) New Year starts in the seventh’s month? I know this is confusing. That’s because we number Nissan – the month of the Exodus from Egypt as the first month.

(**)If you are an observing Jew, you should add to this list Fast of Gedalia, but we will omit it from this discussion

Usually, I keep my blog for professional news only, but this time, I’ll make an exception.

This frame is from a video that was taken a couple of days ago, less than one hour away from my home. Note how many people are there.

Some people will claim that what we see is a peaceful protest by Palestinians against the Israeli occupation. Being a son and a grandson to the Holocaust survivors, I find it hard to connect to the peacefulness of what I see. I don’t have to hear them chanting “from the River to the Sea Palestine will be free” to understand that what they, and many thousands more, really mean is “free of Jews”.

Those who know me, know that I always care with me a cheep and thin notebook which I use as an extension to my mind. Today, I opened a new notebook, and this is a good opportunity to share some links about my productivity system.

• Start with the post “The best productivity system I know”
• Failed attempt with tangible boards is here. This approach has an interesting idea behind it, but I couldn’t stick with it. YMMW
• Failed attempt with digital/analog/tangible combo is here.

I stumbled upon a great graph that tells a complex story compellingly.

Comparison of two COVID-19 waves in the UK, taken from here.

This graph compares the last two waves of COVID-19 in the United Kingdom and is shows so clearly that the new wave (that is supposedly composed of the Delta variant) is much more infections on the one hand, but on the other hand, causes much less damage. Is the more moderate damage the result of the Delta variant nature of the protective effect of the vaccination is still an open question, but the difference is still striking.

To all my friends outside Israel. Do you want to know how the majority of Israelis see the shitty situation we are in? This short video does a good job summarizing it.

https://www.facebook.com/100462013796/videos/537389057252051?__cft__[0]=AZUvYpfaSRjJg_dVRoxwC7U7jmh-t2meeDW48n-IiYtS8d-PgX5o4WGqeConOtdTyC2DY_BagXlldPPzIE4PUgaoh1T_pSh_JCOIvo7BK1NbDifQEGvD07HxuO9pFuEtZeXFpCNSWfBiiZCtxcBbeG8l

My podcast is mostly in Hebrew, but this interview was recorded in English. I hope you will enjoy it

Martin Remy has been managing teams of data engineers and data scientists for more than a decade, and he has been doing so remotely. What lessons can we learn from Martin? לינקים חשובים: https://marting.blog https://martinremy.com עמוד הפייסבוק של ההסכת: https://www.facebook.com/reayonavodapodcast/ עמוד הבית שלי https://gorelik.net/about הרשמו להסכת ב־ גוגל פודקאסטס, ספוטיפיי, אפל מיוזיק, פודבין ובכל פלטפורמה […]

רעיון 38. Managing remotely — בוריס גורליק

This week, I mark an important milestone in my professional life. It is an excellent opportunity to start a new productivity notebook and tell you about the latest evolution of the best productivity system I know.

To sum up, I use a custom variant of Mark Forster’s Final Version productivity system that uses a plain notebook to track, prioritize, and eliminate tasks. Using a physical notebook, as opposed to an electronic tool, is a massive boost in productivity, as it forces you to process your priorities in an unplugged mode, without any distractions.

When I was a freelancer, I felt forced to use a combination of a physical book and an electronic system (http://todoist.com/), but that didn’t work too well for me, the connected nature of this (and any other) app kept distracting me. I also played with a combination of a notebook and a portable kanban board. That didn’t work out for me either. So, right now, I’m back to a physical notebook with a small addition.

I now have two notebooks. The first one is a small (80 pages) soft notebook that I use to track and prioritize tasks (as in Mark Forster’s system). I also use this notebook to reflect on what’s going on, write questions to my future self, and document my decisions.

The second, larger notebook is used for note keeping, drafts and sketches. The fact that the notebook is vertically bound allows me seemingly switching from Hebrew (that is written from right to left) and English. When a sketch of a draft isn’t relevant anymore, I tear the draft pages away; and I use a small binder to keep the note pages together for future reference.

Overall, I like this combo very much and it fits my workflow well.

In January 2020, I started a new experiment. I quit what was a dream job and became a freelancer. Today, the experiment is over. This post serves as omphaloskepsis - a short reflection on what went well and what could have worked better.

What worked well?

To sum up, I declare this experiment successful. I had a chance to work with several very interesting companies. I got exposed to business models of which I wasn’t aware. Most importantly, I met new intelligent and ambitious people. I also had a chance to feel by myself how it feels to be self-employed, to see the behind-the-scenes of several freelancers and entrepreneurs. I learned to appreciate the audacity and the courage of people who don’t rely on monthly paychecks and take much more responsibility for their lives than the vast majority of the “salarymen.”

Let’s talk about money. Was it worth it in terms of \(\)$ (or ₪₪₪₪₪₪)? Objectively speaking, my financial situation remained approximately unchanged. Towards the end of the experiment, I found myself overbooked, which means that, in theory, I could have increased my income substantially. But this is only in theory. In practice, I decided to end the freelance experiment and “settle down”.

What could have been better?

So, was it peachy? Not at all. For me, being a freelancer is much more stressful than being a hired employee. The stress does not come exclusively from the need to make sure one has enough projects in the pipeline (I had enough of them, most of the time). The more significant source of stress came from the lack of focus, the need for EXTREME context switching, and the lack of a team.

I did receive one suggestion to mitigate this source of stress; however, when I heard it, I already had several job offers and was already 90% committed to accepting the position at MyBiotics.

To sum up

I’m am very happy I did this experiment. I learned a lot; I enjoyed a lot (and suffered a lot too), I met new people, and I changed the way I think about many things. Was it a good idea? Yes, it was. Should you try becoming a freelancer? How the hell can I know that? It’s your life; you enjoy the success and take the risk of failure.

I’m excited to announce that I’m joining MyBiotics Pharma Ltd as the company’s Head of Data and Bioinformatics. I have been working with this fantastic company and its remarkable people as a freelancer for fourteen fruitful months. But today, I join the MyBiotics family as a full-time member. Together, we will strive to better understanding the interactions between humans and their microbiome to improve health and well-being.

rbt

Almost one year after it was originally published, I stumbled upon this powerful post.

Today, June 10th 2020, black academic scientists are holding a strike in solidarity with Black Lives Matter protests. I strike with them and for them. This is why: I began to understand the enormity of racism against blacks thirty five years ago when I was 12 years old. A single event, in which I witnessed […]

Black lives matter

The great Robert Kosara, also known as the “eager eyes” has started publishing a series of videos he calls Chart Appreciation. In these videos, Robert takes a piece of data visualization from a reputable and known source, and discusses why this particular piece is so good, what decisions were made that made it possible, what alternatives are, and more. If you consider yourself an intermediate or advanced practitioner of data visualization, you should subscribe. Here’s one example.