To specialize, or not to specialize, that is the data scientists’ question

In my last post on data science career, I heavily promoted the idea that a data scientist needs to find his or her specialization. I back my opinion with my experience and by citing other people opinions. However, keep in mind that I am not a career advisor, I never surveyed the job market, and I might not know what I’m talking about. Moreover, despite the fact that I advocate for specialization, I think that I am more of a generalist.

Since I published the last post, I was pointed to some other posts and articles that either support or contradict my point of view. The most interesting ones are: “Why you shouldn’t be a data science generalist” and “Why Data Science Teams Need Generalists, Not Specialists“, both are very recent and very articulated but promote different points of view. Go figure

The featured image is based on a photo by Tom Parsons on Unsplash

The data science umbrella or should you study data science as a career move (the 2019 edition)?

Illustration: photo of people under umbrellas

TL/DR: Studying data science is OK as long as you know that it’s only a starting point.

Almost two years ago, I wrote a post titled “Don’t study data science as a career move.” Even today, this post is the most visited post on my blog. I was reminded about this post a couple of days ago during a team meeting in which we discussed what does a “data scientist” mean today. I re-read my original post, and I think that I was generally right, but there is a but…

The term “data science” was born as an umbrella term that meant to describe people who know programming, statistics, and business logic. We all saw those numerous Venn diagrams that tried to describe the perfect data scientist. Since its beginning, the field of “data science” has finally matured. There are more and more people that question the mere definition of data science.

Here’s what an entrepreneur Chuck Russel has to say:

Now don’t get me wrong — some of these folks are legit Data Scientists but the majority is not. I guess I’m a purist –calling yourself a scientist indicates that you practice science following a scientific method. You create hypotheses, test the hypothesis with experimental results and after proving or disproving the conjecture move on or iterate.

Screenshot of a Google image search showing many Venn diagrams
There can’t be enough Venn diagrams

Now, “create and test hypotheses” is a very vague requirement. After all, any A/B test is a process of “creating and testing hypotheses” using data. Is anyone who performs A/B tests a data scientist? I think not.
Moreover, a couple of years ago, if you wanted to run an A/B test, perform a regression analysis, build a classifier, you would have to write numerous lines of code, debug and tune it. This tedious and intriguing process certainly felt very “sciency,” and if it worked, you would have been very proud of our job. Today, on the other hand, we are lucky to have general-purpose tools that require less and less coding. I don’t remember the last time I had to implement an analysis or an algorithm from the first principles. With the vast amount of verified tools and libraries, writing an algorithm from scratch feels like a huge waste of time.
On the other hand, I spend more and more time trying to understand the “business logic” that I try to improve: why has this test fail? Who will use this algorithm and what will make them like the results? Does effort justify the potential improvement?

I (a data scientist) have all this extra time to think of a business logic thanks to the huge arsenal of generalized tools to choose from. These tools were created mostly by those data scientists whose primary job is to implement, verify, and tune algorithms. My job and the job of these data scientists is different and requires different sets of skills.

There is another ever-growing group of professionals who work hard to make sure someone can apply all those algorithms to any amount of data they feel suitable. These people know that any model is at most as good as the data it is based on. Therefore, they build systems that deliver the right information on time, distribute the data among computation nodes, and make sure no crazy “scientist” sends a production server to a non-responsive state due to a bad choice of parameters. We already have a term for professionals whose job is to build fail-proof systems. We call them engineers, or “data engineers” in this case.

The bottom line

Up till now, I mentioned three major activities that used to be covered by the data science umbrella: building new algorithms, applying algorithms to business logic, and engineering reliable data systems. I’m sure there are other areas under that umbrella that I forgot. In 2019, we reached the point where one has to decide what field of data science does one want to practice. If you consider stying data science think of it as studying medicine. The vast majority of physicians don’t end up general practitioners but rather invest at least five more years of their lives professionalize. Treat your data science studies as an entry ticket into the life-long learning process, and you’ll be OK. Otherwise, (I’m citing myself here): You might end up a mediocre Python or R programmer who can fiddle with the parameters of various machine learning libraries, one of the many. Sometimes it’s good enough. Frequently, it’s not.

PS. Here’s a one-week-old article on with very similar theses: link.

Against A/B tests

Traditional A/B testsing rests on a fundamentally flawed premise. Most of the time, version A will be better for some subgroups, and version B will be better for others. Choosing either A or B is inherentlyinferior to choosing a targeted mix of A and B.

Michael Kaminsky

The quote above is from a post by Michael Kaminsky “Against A/B tests“. I’m still not fully convinced by Michael’s thesis but it is very interesting and thought-provoking. 

Evolution of a complex graph. Part 1. What do you want to say?

Screenshot showing two slides. The first one is titled "low within-group variability". The second one is titled "High between-group variability". The graphs in the slides is the same

From time to time, people ask me for help with non-trivial data visualization tasks. A couple of weeks ago, a friend-of-a-friend-of-a-friend showed me a set of graphs with the following note:

Each row is a different use case. Each use case was tested on three separate occasions – columns 1,2,3. We hope to show that the lines in each row behave similarly, but that there are differences between the different rows.

Before looking at the graphs, note the last sentence in the above comment. Knowing what you want to show is an essential and not trivial part of a data visualization task. Specifying what is it precisely that you want to say is the first required task in any communication attempt, technical or not.

For the obvious reasons, I cannot share the original graphs that that person gave me. I managed to re-create the spirit of those graphs using a combination of randomly generated arrays.
The original graph: A 3-by-4 panel of line charts
Notice how the X- and Y- axes are aligned between all the subplots. Such alignment is a smart move that provides a shared scale and allows faster and more natural comparison between the curves. You should always try aligning your axes. If aligning isn’t possible, make sure that it is absolutely, 100%, clear that the scales are different. Slight differences are very confusing.

There are several small things that we can do to improve this graph. First, the identical legends in every subplot are a useless waste of ink and thus, of your viewers’ processing power. Since they are identical, these legends do nothing but distracting the viewer. Moreover, while I understand how a variable name such as event_prob appeared on a graph, showing such names outside technical teams is a bad practice. People who don’t share intimate knowledge with the underlying data will find human-readable labels easier to comprehend, making your message “stickier.”
Let’s improve the signal-to-noise ratio of this plot.
An improved version of the 3-by-4 grid of line charts

According to our task, each row is a different use case. Notice that I accompanied each row with a human-readable label. I didn’t use cryptic code such as group_001, age_0_10 or the such.
Now, let’s go back to the task specification. “We hope to show that the lines in each row behave similarly, but that there are differences between the separate rows.” Remember my advice to always use conclusions as graph titles? Let’s test how such a title will look like

A hypothetical screenshot. The title says: "low intra- & high inter- group variability"

Really? Is there a better way to justify the title? I claim that there is.

Let’s experiment a little bit. What will happen if we will plot all the lines on the same graph? By doing so, we might create a stronger emphasize of the similarities and the differences.

Overlapping lines that show several repetitions in four different groups
Not bad. The separate lines create some excessive noise, and the legend isn’t the best way to label multiple lines, so let’s improve the graph even further.

Curves representing four different data groups. Shaded areas represent inter-group variability

Note that meaningful ticks on the X-axis. The 30, 180, and 365-day marks provide useful anchors.

Now, let us go back to our title. “Low intra- and high inter- group variability” is, in fact, two conclusions. If you have ever read any text about technical presentations, you should remember the “one point per slide” rule. How do we solve this problem? In cases like these, I like to use the same graph in two different slides, one for each conclusion.

Screenshot showing two slides. The first one is titled "low within-group variability". The second one is titled "High between-group variability". The graphs in the slides is the same

During a presentation, I would show this graph with the first conclusion as a title. I would talk about the implications of that conclusion. Next, I will say “wait! There is more”, will promote the slide and start talking about the second conclusion.

To sum up,

First, decide what is it that you want to say. Then ask whether your graph says what you want to say. Next, emphasize what you want to say, and finally, say what you want to say.

To be continued

The case that you see in this post is a relatively easy one because it only compares four groups. What will happen if you will need to compare six, sixteen or sixty groups? I will try answering this question in one of my next posts

I will host a data visualization workshop at Israel’s biggest data science event



What: Data Visualization from default to outstanding. Test cases of tough data visualization

Why:  You would never settle for default settings of a machine learning algorithm. Instead, you would tweak them to obtain optimal results. Similarly, you should never stop with the default results you receive from a data visualization framework. Sadly, most of you do.

When: May 27, 2018 (a day before the DataScience summit)/ 13:00 – 16:00

Where:  Interdisciplinary Center (IDC) at Herzliya.

More info: here.

1. Theoretical introduction: three most common mistakes in data visualization (45 minutes)
2. Test case (LAB): Plotting several radically different time series on a single graph (45 minutes)
3. Test case (LAB): Bar chart as an effective alternative to a pie chart (45 minutes)
4. Test case (LAB): Pie chart as an effective alternative to a bar chart (45 minutes)

More words

According to the conference organizers, the yearly Data Science Summit is the biggest data science event in Israel. This year, the conference will take place in Tel Aviv on Monday, May 28. One day before the main conference, there will be a workshop day, hosted at the Herzliya Interdisciplinary Center. I’m super excited to host one of the workshops, during the afternoon session. During this workshop, we will talk about the mistakes data scientist make while visualizing their data and the way to avoid them. We will also have some fun creating various charts, comparing the results, and trying to learn from each others’ mistakes.

Register here.

Whoever owns the metric owns the results — don’t trust benchmarks

Illustration: a mechanical stopwatch in a person's palm

Other factors being equal, what language would you choose for heavy numeric computations: Python or PHP? This is not a language war but a serious question. For me, the choice seems to be obvious: I would choose Python, and I’m not the only one. In this survey, for example, 45% of data scientist use Python, compared to 24% who use PHP. The two sets of data scientists aren’t mutually exclusive, but we do see the picture.

This is why I was very surprised when a colleague of mine suggested switching to PHP due to a three times faster performance in a benchmark. I was very surprised and intrigued. Especially, when I noticed that they used a heavy number crunching for the benchmark.

In that benchmark, the authors compute prime numbers using the following Python code

def get_primes7(n):
	standard optimized sieve algorithm to get a list of prime numbers
	--- this is the function to compare your functions against! ---
	if n < 2:
		return []
	if n == 2:
		return [2]
	# do only odd numbers starting at 3
	if sys.version_info.major <= 2:
		s = range(3, n + 1, 2)
	else:  # Python 3
		s = list(range(3, n + 1, 2))
	# n**0.5 simpler than math.sqr(n)
	mroot = n ** 0.5
	half = len(s)
	i = 0
	m = 3
	while m <= mroot:
		if s[i]:
			j = (m * m - 3) // 2  # int div
			s[j] = 0
			while j =6, Returns a array of primes, 2 &lt;= p <span id="mce_SELREST_start" style="overflow:hidden;line-height:0;"></span>&lt; n &quot;&quot;&quot;
    sieve = np.ones(n//3 + (n%6==2), dtype=np.bool)
    sieve[0] = False
    for i in range(int(n**0.5)//3+1):
        if sieve[i]:
            sieve[      ((k*k)//3)      ::2*k] = False
            sieve[(k*k+4*k-2*k*(i&amp;1))//3::2*k] = False
    return np.r_[2,3,((3*np.nonzero(sieve)[0]+1)|1)]

Did you notice the problem? The code above is a pure Python code. I can't think of a good reason to use pure python code for computationally-intensive, time-sensitive tasks. When you need to crunch numbers with Python, and when the computational time is even remotely important, you will most certainly use tools that were specifically optimized for such tasks. One of the most important such tools is numpy, in which the most important loops are implemented in C++ or in Fortran. Many other packages, such as Pandas, scipy, sklearn, and others rely on numpy or other form of speed optimization.

The following snippet uses numpy to perform the same computation as the first one.

def numpy_primes(n):
    """ Input n&gt;=6, Returns a array of primes, 2 &lt;= p <span id="mce_SELREST_start" style="overflow:hidden;line-height:0;"></span>&lt; n &quot;&quot;&quot;
    sieve = np.ones(n//3 + (n%6==2), dtype=np.bool)
    sieve[0] = False
    for i in range(int(n**0.5)//3+1):
        if sieve[i]:
            sieve[      ((k*k)//3)      ::2*k] = False
            sieve[(k*k+4*k-2*k*(i&amp;1))//3::2*k] = False
    return np.r_[2,3,((3*np.nonzero(sieve)[0]+1)|1)]

On my computer, the timings to generate primes smaller than 10,000,000 is 1.97 seconds for the pure Python implementation, and 21.4 milliseconds for the Numpy version. The numpy version is 92 times faster!

What does that mean? 
Whoever owns the metric owns the results. Never trust a benchmark result before you understand how the benchmark was performed, and before making sure the benchmark was performed under the conditions that are relevant to you and your problem.



On algorithmic fairness & transparency

Illustration: large lamp sigh that says "The same for everyone" with a sunset as a background

My teammate, Charles Earl has recently attended the Conference on Fairness, Accountability, and Transparency (FAT*). The conference site is full of very interesting material, including proceedings and video recording of lectures and tutorials.

Reading through the conference proceedings, I found a very interesting paper titled “The Cost of Fairness in Binary Classification.” This paper talks about the measures one needs to take in order not use sensitive features (such as race) as the means to discrimination, with a reasonable accuracy tradeoff.

Skimming through this paper, I recalled a conversation I had about a year ago with a chief data scientist in a startup that provides short-term loans to people who need some money now. The major job of the data science team in that company was to assess the risk of a customer. From the explanation the chief data scientist gave, and from the data sources she described, it was clear that they train their model on the information whether a person is likely to receive a loan from a financial institution. When I pointed out that they exclude categories of people that are rejected but are likely to return the money. “Yes?” she said in a tone as if she couldn’t see what the problem that I tried to raise was. “Well,” I said, it’s unfair for many customers, plus you’re missing the chance to recruit customers who were rejected by others”. “We have enough potential customers,” she said. She didn’t think fairness was an issue worth talking about.


The featured image is by Søren Astrup Jørgensen from Unsplash


Five misconceptions about data science

One item on my todo list is to write a post about “three common misconceptions about data science. Today, I found this interesting post that lists misconceptions much better than I would have been able to do. Plus, they list five of them. That 67% more than I intended to do 😉

I especially liked the section called “What is a Data Scientist” that presents six Venn diagrams of a dream data scientist.

The analogy between the data scientist and a purple unicorn is still apt – finding an individual that satisfies any one of the top four diagrams above is rare.


Enjoy reading  Five Misconceptions About Data Science – Knowing What You Don’t Know — Track 2 Analytics

Overfitting reading list

Overfitting is a situation in which a model accurately describes some data but not the phenomenon that generates that data. Overfitting was a huge problem in the good old times, where each data point was expensive, and researchers operated on datasets that could fit a single A4 sheet of paper. Today, with mega- giga- and tera-bytes datasets, overfitting is … still a problem. A very painful one. Following is a short reading list on overfitting.

I would like to start with Mehmet Suzen who treats overfitting as “inaccurate meme in supervised learning

cross-validation does not prevent your model to overfit and good out-of-sample performance does not guarantee not-overfitted model.

Another blogger, whose name I couldn’t find, has two very detailed posts on overfitting:

Understanding overfitting from bias-variance trade-off and Understanding overfitting from Haussler 1988 theorem

Finally, Adrian from the “morning paper” (please don’t tell me you don’t follow that blog) has a summary of another paper, titled “Understanding deep learning requires re-thinking generalization” (I only read Adrian’s summary).


No conclusions here. It’s a reading list.

Featured image credit: