## 35 (and more) Ways Data Go Bad — Stats With Cats Blog

If you plan working data analysis or processing, read the excellent post in the “stats with cats blog” titled “35 Ways Data Go Bad” post. I did experience each and every one of the 35 problems. However, this list is far from being complete. One should add the comprehensive list of Falsehoods Programmers Believe About Time.

When you take your first statistics class, your professor will be a kind person who cares about your mental well-being. OK, maybe not, but what the professor won’t do is give you real-world data sets. The data may represent things you find in the real world but the data set will be free of errors. […]

35 Ways Data Go Bad — Stats With Cats Blog

## Online data science conference on May, 28

NDR is a family of machine learning/data science conferences. Their next conference will be held online on May, 28 and the agenda looks great.

Now, I’m not super objective here, because I’m presenting at NDR July event. But look at the topics, what an impressive selection!

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## Why is forecasting s-curves hard?

Constance Crozier (@clcrozier on Twitter) shared an interesting simulation in which she tried to fit a sigmoid curve (s-curve) to predict a plateau in a time-series. The result was a very intuitive and convincing animation that shows how wrong her initial forecasts were.

The matter of fact is that this phenomenon is not new at all. My first post-University job involved fitting numerous pharmacodynamics models. We always had to keep in mind that if the available data does not account for at least 95% of the maximum effect, the model will be very much suboptimal. It took me a while, but I managed to find the reference for this phenomenon [here]. Maybe, when I have some time, I will repeat Constance Crozier’s analysis, and add confidence intervals to emphasize the point.

EDIT: I came the conclusion that the most important takaway message of this demonstration is the necessity of reporting uncertainty with any forecast, and how small the value of a forecast is without uncertainty estimations.

S-curves (or sigmoid functions) are commonly used to model the evolution of social or biological systems over time [1]. These functions start with exponential growth, then increase linearly, and finally level off (therefore end up looking like a wonky s). Many things that we think of as exponential functions will actually follow an s-curve (otherwise […]

Forecasting s-curves is hard — Constance Crozier
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## Not a wasted time

Being a freelancer data scientist, I get to talk to people about proposals that don’t materialize into projects. These conversations take time, but strangely enough, I enjoy them very much, I also find these conversations educating. How else could I have learned about a business model X, or what really happens behind the scenes of company Y?

## Career advice. A research pharmacist wants to become a data scientist.

Recently, I received an email from a pharmacist who considers becoming a data scientist. Since this is not a first (or last) similar email that I receive, I think others will find this message exchange interesting.

Here’s the original email with minor edits, followed by my response.

## The question

Hi Boris,

My name is XXXXX, and I came across your information and your advice on data science as I was researching career opportunities.

I currently work at a hospital as a research pharmacist, mainly involved in managing drugs for clinical trials.
Initially, I wanted to become a clinical pharmacist and pursued 1-year post-graduate residency training. However, it was not something I could envision myself enjoying for the rest of my career.

I then turned towards obtaining a Ph.D. in translational research, bridging the benchwork research to the bedside so that I could be at the forefront of clinical trial development and benefit patients from the rigorous stages of pre-clinical research outcomes. I much appreciate learning all the meticulous work dedicated before the development of Phase I clinical trials. However, Ph.D. in pharmaceutical sciences was overkill for what I wanted to achieve in my career (in my opinion), and I ended up completing with master’s in pharmaceutical sciences.

Since I wanted to be involved in both research and pharmacy areas in my career, I ended up where I am now, a research pharmacist.

My main job description is not any different from typical hospital pharmacists. I do have a chance of handling investigational medications, learning about new medications and clinical protocols, overseeing side effects that may be a crucial barrier in marketing the trial medications, and sometimes participating in development of drug preparation and handling for investigator-initiated trials. This does keep my job interesting and brings variety in what I do. However, I do still feel I am merely following the guidelines to prepare medications and not critically thinking to make interventions or manipulate data to see the outcomes. At this point, I am preparing to find career opportunities in the pharmaceutical industry where I will be more actively involved in clinical trial development, exchanging information about targeting the diseases and analyzing data. I believe gaining knowledge and experiences in critical characteristics for the data science field would broaden my career opportunities and interest. Still, unfortunately, I only have pharmacy background and have little to no experience in computer science, bioinformatics, or machine learning.

## The answer

First of all, thank you for asking me. I’m genuinely flattered. I assume that you found me through my blog posts, and if not, I suggest that you read at least the following posts

All my thoughts on the career path of a data scientist appear in this page https://gorelik.net/category/career-advice/

Now, specifically to your questions.

My path towards data science was through gradual evolution. Every new phase in my career used my previous experience and knowledge. From B.Sc studies in pharmacy to doctorate studies in computational drug design, from computational drug design to biomathematical modeling, from that to bioinformatics, and from that to cybersecurity. Of course, my path is not unique. I know at least three people who followed a similar career from pharmacy to data science. Maybe other people made different choices and are even more successful than I am. My first advice to everyone who wants to transition into data science is not to (see the first link in the list above). I was lucky to enter the field before it was a field, but today, we live in the age of specialization. Today we have data analysts, data engineers, machine learning engineers, NLP scientists, image processing specialists, etc. If computational modeling is something that a person likes and sees themselves doing for living, I suggest pursuing a related advanced degree with a project that involves massive modeling efforts. Examples of such degrees for a pharmacist are computational chemistry, pharmacoepidemiology, pharmacovigilance, bioinformatics. This way, one can utilize the knowledge that they already have to expand the expertise, build a reputation, and gain new knowledge. If staying in academia is not an option, consider taking a relevant real-life project. For example, if you work in a hospital, you could try identifying patterns in antibiotics usage, a correlation between demographics and hospital re-admission, … you get the idea.

Whatever you do, you will not be able to work as a data scientist if you can’t write computer programs. Modifying tutorial scripts is not enough; knowing how to feed data into models is not enough.

Also, my most significant knowledge gap is in maths. If you do go back to academia, I strongly suggest taking advantage of the opportunity and taking several math classes: at least calculus and linear algebra and, of course, statistics.

## Do you have a question for me?

If you have questions, feel free writing them here, in the comments section or writing to boris@gorelik.net

## Staying employable and relevant as a data scientist

One common wisdom is that creative jobs are immune to becoming irrelevant. This is what Brian Solis, the author of “Lifescale” says on this matter

On the positive side, historically, with every technological advancement, new jobs are created. Incredible opportunity opens up for individuals to learn new skills and create in new ways. It is your mindset, the new in-demand skills you learn, and your creativity that will assure you a bright future in the age of automation. This is not just my opinion. A thoughtful article in Harvard Business Review by Joseph Pistrui was titled, “The Future of Human Work Is Imagination, Creativity, and Strategy.” He cites research by McKinsey […]. In their research, they discovered that the more technical the work, the more replaceable it is by technology. However, work that requires imagination, creative thinking, analysis, and strategic thinking is not only more difficult to automate; it is those capabilities that are needed to guide and govern the machines.

Many people think that data science falls into the category of “creative thinking and analysis”. However, as time passes by this becomes less true. Here’s why.

As time passes by, tools become stronger, smarter, and faster. This means that a problem that could have been solved using cutting edge algorithms running by cutting edge scientists on cutting edge computers, will be solvable using a commodity product. “All you have to do” is to apply domain knowledge, select a “good enough” tool, get the results and act upon them. You’ll notice that I included two phases in quotation marks. First, “all you have to do”. I know that it’s not that simple as “just add water” but it gets simpler.

“Good enough” is also a tricky part. Selecting the right algorithm for a problem has dramatic effect on tough cases but is less important with easy ones. Think of a sorting algorithm. I remember my algorithm class professor used to talk how important it was to select the right sorting algorithm to the right problem. That was almost twenty years ago. Today, I simply write `list.sort()` and I’m done. Maybe, one day I will have to sort billions of data points in less than a second on a tiny CPU without RAM, which will force me into developing a specialized solution. But in 99.999% of cases, `list.sort()` is enough.

Back to data science. I think that in the near future, we will see more and more analogs of `list.sort()`. What does that mean to us, data scientists? I am not sure. What I’m sure is that in order to stay relevant we have to learn and evolve.

Featured image by Héctor López on Unsplash

## Cow shit, virtual patient, big data, and the future of the human species

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

## Data science tools with a graphical user interface

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 have made a modest contribution to Orange by adding a six-lines function that computes Matthews correlation coefficient.

Other tools are KNIME and Weka (none of them is natively Python).

There is also RapidMinder but I never used it.

## Bootstrapping the right way?

Many years ago, I terribly overfit a model which caused losses of a lot of shekels (a LOT). It’s not that I wasn’t aware of the potential overfitting. I was. Among other things, I used several bootstrapping simulations. It turns out that I applied the bootstrapping in a wrong way. My particular problem was that I “forgot” about confounding parameters and that I “forgot” that peeping into the future is a bad thing.

Anyhow, Yanir Seroussi, my coworker data scientist, gave a very good talk on bootstrapping.

Bootstrapping the right way is a talk I gave earlier this year at the YOW! Data conference in Sydney. You can now watch the video of the talk and have a look through the slides. The content of the talk is similar to a post I published on bootstrapping pitfalls, with some additional simulations.

The main takeaways shared in the talk are:

• Don’t compare single-sample confidence intervals by eye
• Use enough resamples (15K?)
• Use a solid bootstrapping package (e.g., Python ARCH)
• Use the right bootstrap for the job
• Consider going parametric Bayesian
• Test all the things

Testing all the things typically requires writing code, which I did for the talk. You can browse through it in this notebook. The most interesting findings from my tests are summarised by the following figure.

The figure shows how the accuracy of confidence interval estimation varies by algorithm, sample size…

View original post 405 more words

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## The third wave data scientist – a useful point of view

In 2019, it’s hard to find a data-related blogger who doesn’t write about the essence and the future of data science as a profession. Most of these posts (like this one for example) are mostly useless both for existing data scientists who think about their professional plans and for people who consider data science as their career.

Today I saw yet another post which I find very useful. In this post, Dominik Haitz identifies a “third wave data scientist.” In Dominik’s opinion, a successful data scientist has to combine four features: (1) Business mindset (2) Software engineering craftsmanship (3) Statistics and algorithmic toolbox, and (4) Soft skills. In Dominik’s classification, the business mindset is not “another skill” but the central pillar.

The professional challenges that I have been facing during the past eighteen months or so, made me realize the importance of points 1, 2, and 3 from Dominik’s list (number 4 was already very important on my personal list). However, it took reading his post to put the puzzle parts in place.

Dominik’s additional contribution to the discussion is ditching the famous data science Venn Diagram in favor of another, “business-oriented” visual which I used as the “featured image” to this post.

## 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)?

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.

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 Forbes.com 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 locallyoptimistic.com

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.

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## Back to Mississippi: Black migration in the 21st century. By Charles Earl

I wonder how this analysis of remained unnoticed by the social media

The recent election of Doug Jones […] got me thinking: What if the Black populations of Southern cities were to experience a dramatic increase? How many other elections would be impacted?

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

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.

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.

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

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.

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.

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.

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

## TL/DR

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.

Timeline:
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

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 &lt; 2:
return []
if n == 2:
return [2]
# do only odd numbers starting at 3
if sys.version_info.major &lt;= 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 &lt;= 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]:
k=3*i+1|1
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):
# http://stackoverflow.com/questions/2068372/fastest-way-to-list-all-primes-below-n-in-python/3035188#3035188
""" 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]:
k=3*i+1|1
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.

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## On algorithmic fairness & transparency

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

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## 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.

## 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 mllib.wordpress.com 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:

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).

### Conclusion

No conclusions here. It’s a reading list.

Featured image credit: https://en.wikipedia.org/wiki/Overfitting#/media/File:Overfitting.svg

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## Once again on becoming a data scientist

My stand on learning data science is known: I think that learning “data science” as a career move is a mistake. You may read this long rant of mine to learn why I think so. This doesn’t mean that I think that studying data science, in general, is a waste of time.

Let me explain this confusion. Take this blogger for example https://thegirlyscientist.com/. As of this writing, “thegirlyscientst” has only two posts: “Is my finance degree useless?” and “How in the world do I learn data science?“. This person (whom I don’t know) seems to be a perfect example of someone may learn data science tools to solve problems in their professional domain. This is exactly how my professional career evolved, and I consider myself very lucky about that. I’m a strong believer that successful data scientists outside the academia should evolve either from domain knowledge to data skills or from statistical/CS knowledge to domain-specific skills. Learning “data science” as a collection of short courses, without deep knowledge in some domain, is in my opinion, a waste of time. I’m constantly doubting myself with this respect but I haven’t seen enough evidence to change my mind. If you think I miss some point, please correct me.

## Is Data Science a Science?

Is Data Science a Science? I think that there is no data scientist who doesn’t ask his- or herself this question once in a while. I recalled this question today when I watched a fascinating lecture “Theory,  Prediction, Observation” made by Richard Feynman in 1964.  For those who don’t know, Richard Feynman was a physicist who won the Nobel Prize, and who is considered one of the greatest explainers. In that particular lecture, Prof. Feynman talked about science as a sequence of  Guess ⟶ Compute Consequences ⟶ Compare to Experiment

This is exactly what we do when we build models: we first guess what the model should be, compute the consequences (i.e. fit the parameters). Finally, we evaluate our models against observations.

My favorite quote from that lecture is

… and therefore, experiment produces troubles, every once in a while …

I strongly recommend watching this lecture. It’s one hour long, so if you don’t have time, you may listen to it while commuting. Feynman is so clear, you can get most of the information by ear only.

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## What is the best way to collect feedback after a lecture or a presentation?

I consider teaching and presenting an integral part of my job as a data scientist. One way to become better at teaching is to collect feedback from the learners. I tried different ways of collecting feedback: passing a questionnaire, Polldaddy surveys or Google forms, or simply asking (no, begging) the learners to send me an e-mail with the feedback. Nothing really worked.  The response rate was pretty low. Moreover, most of the feedback was a useless set of responses such as “it was OK”, “thank you for your time”, “really enjoyed”. You can’t translate this kind of feedback to any action.

Recently, I figured out how to collect the feedback correctly. My recipe consists of three simple ingredients.

## Collecting feedback. The recipe.

working time: 5 minutes

### Ingredients

• Open-ended mandatory questions: 1 or 2
• Post-it notes: 1 – 2 per a learner
• Preventive amnesty: to taste

### Procedure

Our goal is to collect constructive feedback. We want to improve and thus, are mainly interested in aspects that didn’t work well. In other words, we want the learners to provide constructive criticism. Sometimes, we may learn from things that worked well. You should decide whether you have enough time to ask for positive feedback. If your time is limited, skip it. Criticism is more valuable than praises.

Pass post-it notes to your learners.

Next, start with preventive amnesty, followed by mandatory questions, followed by another portion of preventive amnesty. This is what I say to my learners.

[Preventive amnesty] Criticising isn’t easy. We all tend to see criticism as an attack and to react accordingly. Nobody likes to be attacked, and nobody likes to attack. I know that you mean well. I know that you don’t want to attack me. However, I need to improve.

[Mandatory question] Please, write at least two things you would improve about this lecture/class. You cannot pass on this task. You are not allowed to say “everything is OK”. You will not leave this room unless you handle me a post-it with two things you liked the less about this class/lecture.

[Preventive amnesty] I promise that I know that you mean good. You are not attacking me, you are giving me a chance to improve.

That’s it.

When I teach using the Data Carpentry methods, each of my learners already has two post-it notes that they use to signal whether they are done with an assignment (green) or are stuck with it (red). In these cases, I ask them to use these notes to fill in their responses — one post-it note for the positive feedback, and another one for the criticism. It always works like a charm.

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## Data is the new

I stumbled upon a rant titled  Data is not the new oil — Tech Insights

You’ve heard it many times and so have I: “Data is the new oil” Well it isn’t. At least not yet. I don’t care how I get oil for my car or heating. I simply decide what to cook and where to drive when I want. I’m unconcerned which mechanism is used to refine oil […]

Funny, in my own rant “data is not the new gold“, I claimed that “oil” was a better analogy for data than gold. Obviously, any “X is the new Y” sentences are problematic but it’s still funny how we like them.

## Don’t take career advises from people who mistreat graphs this badly

Recently, I stumbled upon a report called “Understanding Today’s Chief Data Scientist” published by an HR company called Heidrick & Struggles. This document tries to draw a profile of the modern chief data scientist in today’s Big Data Era. This document contains the ugliest pieces of data visualization I have seen in my life. I can’t think of a more insulting graphical treatment of data. Publishing graph like these ones in a document that tries to discuss careers in data science is like writing a profile of a Pope candidate while accompanying it with pornographic pictures.

Before explaining my harsh attitude, let’s first ask an important question.

### What is the purpose of graphs in a report?

There are only two valid reasons to include graphs in a report. The first reason is to provide a meaningful glimpse into the document. Before a person decided whether he or she wants to read a long document, they want to know what is it about, what were the methods used, and what the results are. The best way to engage the potential reader to provide them with a set of relevant graphs (a good abstract or introduction paragraph help too). The second reason to include graphs in a document is to provide details that cannot be effectively communicating by text-only means.

That’s it! Only two reasons. Sometimes, we might add an illustration or two, to decorate a long piece of text. Adding illustrations might be a valid decision provided that they do not compete with the data and it is obvious to any reader that an illustration is an illustration.

### Let the horror begin!

The first graph in the H&S report stroke me with its absurdness.

At first glance, it looks like an overly-artistic doughnut chart. Then, you want to understand what you are looking at. “OK”, you say to yourself, “there were 100 employees who belonged to five categories. But what are those categories? Can someone tell me? Please? Maybe the report references this figure with more explanations? Nope.  Nothing. This is just a doughnut chart without a caption or a title. Without a meaning.

I continued reading.

OK, so the H&S geniuses decided to hide the origin or their bar charts. Had they been students in a dataviz course I teach, I would have given them a zero. Ooookeeyy, it’s not a college assignment, as long as we can reconstruct the meaning from the numbers and the labels, we are good, right? I tried to do just that and failed. I tried to use the numbers in the text to help me filling the missing information and failed. All in all, these two graphs are a meaningless graphical junk, exactly like the first one.

The fourth graph gave me some hope.

Sure, this graph will not get the “best dataviz” award, but at least I understand what I’m looking at. My hope was too early. The next graph was as nonsense as the first three ones.

Finally, the report authors decided that it wasn’t enough to draw smartly looking color segments enclosed in a circle. They decided to add some cool looking lines. The authors remained faithful to their decision to not let any meaning into their graphical aids.

### Can’t we treat these graphs as illustrations?

Before co-founding the life-changing StackOverflow, Joel Spolsky was, among other things, an avid blogger. His blog, JoelOnSoftware, was the first blog I started following. Joel writes mostly about the programming business and. In order not to intimidate the readers with endless text blocks, Joel tends to break the text with illustrations. In many posts, Joel uses pictures of a cute Husky as an illustration. Since JoelOnSoftware isn’t a cynology blog, nobody gets confused by the sudden appearance of a Husky. Which is exactly what an illustration is – a graphical relief that doesn’t disturb. But what would happen if Joel decided to include a meaningless class diagram? Sure a class diagram may impress the readers. The readers will also want to understand it and its connection to the text. Once they fail, they will feel angry, and rightfully so

### The bottom line

The bottom line is that people have to respect the rules of the domain they are writing about. If they don’t, their opinion cannot be trusted. That is why you should not take any pieces of advice related to data (or science) from H&S. Don’t get me wrong. It’s OK not to know the “grammar” of all the possible business domains. I, for example, know nothing about photography or dancing; my English is far from being perfect. That is why, I don’t write about photography, dancing or creative writing. I write about data science and visualization. It doesn’t mean I know everything about these fields. However, I did study a lot before I decided I could write something without ridiculing myself. So should everyone.

## The Keys to Effective Data Science Projects — Operationalize

Recently, I’ve stumbled upon an interesting series of posts about effective management of data science projects.  One of the posts in the series says:

“Operationalization” – a term only a marketer could love. It really just means “people using your solution”.

The main claim of that post is that, at some point, bringing actual users to your data science project may be more important than improving the model. This is exactly what I meant in my “when good enough is good enough” post (also available on YouTube)

## Buzzword shift

Many years ago, I tried to build something that today would have been called “Google Trends for Pubmed”. One thing that I’ve found during that process was how the emergence of HIV-related research reduced the number of cancer studies and how, several years later, the HIV research boom settled down, while letting the cancer research back.

I recalled about that project of mine when I took a look at the Google Trends data for, a once popular buzz-phrases, “data mining” and pattern recognition.  Sic transit gloria mundi.

It’s not surprising that “Data Science” was the less popular term in 2004. As I already mentioned, “Data Science” is a relatively new term. What does surprise me is the fact that in the past, “Machine Learning” was so less popular that “Data Mining”. Even more surprising is the fact that Google Trends ranks “Machine Learning” almost twice as high, as “Data Science”. I was expecting to see the opposite.

“Pattern Recognition,” that, in 2004, was as (not) popular as “Machine Learning” become even less popular today. Does that mean that nobody is searching for patterns anymore? Not at all. The 2004 pattern recognition experts are now machine learning professors senior data scientists or if they work in academia, machine learning professors.

PS: does anybody know the reason behind the apparent seasonality in “Data Mining” trends?

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## On alert fatigue

I developed an anomaly detection system for Automattic internal dashboard. When presenting this system (“When good enough is just good enough“), I used to tell that in our particular case, the cost of false alerts was almost zero. I used to explain this claim by the fact that no automatic decisions were made based on the alerts, and that the only subscribers of the alert messages were a limited group of colleagues of mine. Automattic CFO, Stu West, who was the biggest stakeholder in this project, asked me not to stop claiming the “zero cost” claim. When the CFO of the company you work for asks you to do something, you comply. So, I stopped saying “zero cost” but I still listed the error costs as a problem I can safely ignore for the time being. I didn’t fully believe Stu, which is evident from the speaker notes of my presentation deck:

I recalled about Stu’s request to stop talking about “zero cost” of false alerts today. Today, I noticed more than 10 unread messages in the Slack channel that receives my anomaly alerts. The oldest unread message was two weeks old. The only reason this could happen is that I stopped caring about the alerts because there were too many of them. I witnessed the classical case of “alert fatigue”, described in “The Boy Who Cried Wolf”, many centuries ago.

The lesson of this story is that there is no such a thing as zero-cost false alarms. Lack of specificity is a serious problem.

Feature image by Ray Hennessy

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## What’s the most important thing about communicating uncertainty?

Sigrid Keydana, in her post Plus/minus what? Let’s talk about uncertainty (talk) — recurrent null, said

What’s the most important thing about communicating uncertainty? You’re doing it

Really?

Here, for example, a graph from a blog post

The graph clearly “communicates” the uncertainty but does it really convey it? Would you consider the lines and their corresponding confidence intervals very uncertain had you not seen the points?

What if I tell you that there’s a 30% Chance of Rain Tomorrow? Will you know what it means? Will a person who doesn’t operate on numbers know what it means? The answer, to both these questions, is “no”, as is shown by Gigerenzer and his collaborators in a 2005 paper.

Communicating uncertainty is not a new problem. Until recently, the biggest “clients” of uncertainty communication research were the weather forecasters.  However, the recent “data era” introduced uncertainty to every aspect of our personal and professional lives. From credit risk to insurance premiums, from user classification to content recommendation, the uncertainty is everywhere. Simply “doing” uncertainty communication, as Sigrid Keydana from the Recurrent Null blog suggested isn’t enough. The huge public surprise caused by the 2016 US presidential election is the best evidence for that. Proper uncertainty communication is a complex topic. A good starting point to this complex topic is a paper Visualizing Uncertainty About the Future by David Spiegelhalter.

## The fastest way to get first N items in each group of a Pandas DataFrame

In my work, the speed of code writing and reading is usually more important than the speed of its execution. Right now, I’m facing a challenge of optimizing the running time of a fairly complex data science project. After a lot of profiling, I identified the major time consumers. One of such time-consuming steps involved grouping a Pandas DataFrame by a key, sorting each group by a score column, and taking first N elements in each group. The tables in this step are pretty small not more than one hundred elements. But since I have to perform this step many times, the running time accumulates to a substantial fraction.

Let’s first construct a toy example

```N = 100
x = np.random.randint(1, 5, N).astype(int)
y = np.random.rand(N)
d = pd.DataFrame(dict(x=x, y=y))
```

I’ll use %%timeit cell magic which runs a Jupyter cell many times, and measures the time it takes to run the code.

```
%%timeit
d.groupby(
'x'
).apply(
lambda t: t.head(K)
).reset_index(drop=True)

```

This is the output:

`3.19 ms ± 253 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)`

I suspected that head() was not the most efficient way to take the first lines. I tried .iloc

```
%%timeit
d.groupby(
'x'
).apply(
lambda t: t.iloc[0:K]
).reset_index(drop=True)

```
`2.92 ms ± 86.9 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)`

A 10% improvement. Not bad but not excellent either. Then I realized that Pandas groupby object have their own head function

```
%%timeit
d.groupby(
'x'
).head(
K
).reset_index(drop=True)

```
`674 µs ± 23.7 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)`

647 microseconds instead of 3.2 milliseconds. The improvement is by almost a factor of five!

It’s not enough to have the right tool, it’s important to be aware of it, and to use it right. I wonder whether there is even faster way to obtain this job.

## On machine learning, job security, professional pride, and network trolling

If you are a data scientist, I am sure you wondered whether deep neural networks will replace you at your job one day. Every time I read about reports of researchers who managed to trick neural networks, I wonder whether the researchers were thinking about their job security, or their professional pride while performing the experiments. I think that the first example of such a report is a 2014 paper by Christian Szegedy and his colleagues called “Intriguing properties of neural networks“. The main goal of this paper, so it seems, was to peek into the black box of neural networks. In one of the experiments, the authors designed minor, invisible perturbation of the original images. These perturbations diminished the classification accuracy of a trained model.

In the recent post “5 Ways to Troll Your Neural Network” Ben Orlin describes five different ways to “troll a network”.

Image credit: Figure 5 from “Intriguing properties of neural networks“.

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## Good information + bad visualization = BAD

I went through my Machine Learning tag feed. Suddenly, I stumbled upon a pie chart that looked so terrible, I was sure the post would be about bad practices in data visualization. I was wrong. The chart was there to convey some information. The problem is that it is bad in so many ways. It is very hard to appreciate the information in a post that shows charts like that. Especially when the post talks about data science that relies so much on data visualization.

I would write a post about good practices in pie charts, but Robert Kosara, of https://eagereyes.org does this so well, I don’t really think I need to reinvent the weel. Pie charts are very powerful in conveying information. Make sure you use this tool well. I strongly suggest reading everything Robert Kosara has to say on this topic.

## What are the best practices in planning & interpreting A/B tests?

Compiled by my teammate Yanir Serourssi, the following is a reading list an A/B tests that you should read even if you don’t plan to perform an A/B test anytime soon. The list is Yanir’s. The reviews are mine. Collective intelligence in action 🙂

• If you don’t pay attention, data can drive you off a cliff
In this post, Yanir lists seven common mistakes that are common to any data-based analysis. At some point, you might think that this is a list of trivial truths. Maybe it is. The fact that Yanir’s points are trivial doesn’t make them less correct. Awareness doesn’t exist without knowledge. Unfortunately, knowledge doesn’t assure awareness. Which is why reading trivial truths is a good thing to do from time to time.
• How to identify your marketing lies and start telling the truth
This post was written by Tiberio Caetano, a data science professor at the University of Sidney. If I had to summarize this post with a single phrase, that would be “confounding factors”. A confounding variable is a variable hidden from your eye that influences a measured effect. One example of a confounding variable is when you start an ad campaign for ice cream, your sales go up, and you conclude that the ad campaign was effective. What you forgot was that the ad campaign started at the beginning of the summer, when people start buying more ice cream anyhow.
See this link for a detailed textbook-quality review of confounding variables.
• Seven rules of thumb for web site experimenters
I read this review back in 2014, shortly after it was published by, among others, researchers from Microsoft and LinkedIn. Judging by the title, one would expect yet another list of trivial truths in a self-promoting product blog. This is not the case here. In this paper, you will find several real-life case studies, many references to marketing studies, and no advertising of shady products or schemes.
• A dirty dozen: Twelve common metric interpretation pitfalls in online controlled experiments
Another academic paper by Microsoft researchers. This one lists a lot of “dont’s”. Like in the previous link, every advice the authors give is based on established theory and backed up by real data.
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## Data Science or Data Hype?

In his blog post Big Data Or Big Hype? , Rick Ciesla is asking a question whether the “Big Data” phenomenon is “a real thing” or just a hype? I must admit that, until recently, I was sure that the term “Data Science” was a hype too — an overbroad term to describe various engineering and scientific activities. As time passes by, I become more and more confident that Data Science matures into a separate profession. I haven’t’ yet decided whether the word “science” is fully appropriate in this case is.

We have certainly heard a lot about Big Data in recent years, especially with regards to data science and machine learning. Just how large of a data set constitutes Big Data? What amount of data science and machine learning work involves truly stratospheric volumes of bits and bytes? There’s a survey for that, courtesy of […]

## Although it is easy to lie with statistics, it is easier to lie without

I really recommend reading this (longish) post by Tom Breur called “Data Dredging” (and following his blog. The post is dedicated to overfitting — the most scaring problem in machine learning. Overfitting is easy to do and is hard to avoid. It is a serious problem when working with “small data” but is also a problem in the big data era. Read “Data Dredging” for an overview of the problem and its possible cures.

Quoting Tom Breur:

Reality for many data scientist is that the data at hand, in particular some minority class you are predicting, are almost always in short supply. You would like to have more data, but they simply aren’t available. Still, there might be excellent business value in building the best possible model from these data, as long as you safeguard against overfitting. Happy dredging!

Happy dredging indeed.

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## Gartner: More than 40% of data science tasks will be automated by 2020. So what?

Recently, I gave a data science career advice, in which I suggested the perspective data scientists not to study data science as a career move. Two of my main arguments were (and still are):

• The current shortage of data scientists will go away, as more and more general purpose tools are developed.
• When this happens, you’d better be an expert in the underlying domain, or in the research methods. The many programs that exist today are too shallow to provide any of these.

Recently, the research company Gartner published a press release in which they claim that “More than 40 percent of data science tasks will be automated by 2020, resulting in increased productivity and broader usage of data and analytics by citizen data scientists, according to Gartner, Inc.” Gartner’s main argument is similar to mine: the emergence of ready-to-use tools, algorithm-as-a-service platforms and the such will reduce the amount of the tedious work many data scientists perform for the majority of their workday: data processing, cleaning, and transformation. There are also more and more prediction-as-a-service platforms that provide black boxes that can perform predictive tasks with ever increasing complexity. Once good plug-and-play tools are available, more and more domain owners, who are not necessary data scientists, will be able to use them to obtain reasonably good results. Without the need to employ a dedicated data scientist.

## 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.

So, what is the future of data science occupation? Will the emergence of out-of-box data science tools make data scientists obsolete? The answer depends on the data scientists, and how sustainable his or her toolbox is. In the past, bookkeeping used to rely on manual computations. Has the emergence of calculators, and later, spreadsheet programs, result in the extinction of bookkeepers as a profession? No, but most of them are now busy with tasks that require more expertise than just adding the numbers.

The similar thing will happen, IMHO, with data scientists. Some of us will develop a specialization in a business domain — gain a better understanding of some aspect of a company activity. Others will specialize in algorithm optimization and development and will join the companies for which algorithm development is the core business. Others will have to look for another career. What will be the destiny of a particular person depends mostly on their ability to adapt. Basic science, solid math foundation, and good research methodology are the key factors the determine one’s career sustainability. The many “learn data science in 3 weeks” courses might be the right step towards a career in data science. A right, small step in a very long journey.

Featured image: Alex Knight on Unsplash

## Why is it (almost) impossible to set deadlines for data science projects?

In many cases, attempts to set a deadline to a data science project result in a complete fiasco. Why is that? Why, in many software projects, managers can have a reasonable time estimate for the completion but in most data science projects they can’t? The key points to answer this question are complexity and, to a greater extent, missing information. By “complexity” I don’t (only) mean the computational complexity. By “missing information” I don’t mean dirty data. Let us take a look at these two factors, one by one.

## Complexity

Think of this. Why most properly built bridges remain functional for decades and sometimes for centuries, while the rule in every non-trivial program is that “there is always another bug?”. I read this analogy in Joel Spolsky’s post written in 2001. The answer Joel provides is:

Once you’ve written a subroutine, you can call it as often as you want. This means that almost everything we do as software developers is something that has never been done before. This is very different than what construction workers do.

There was a substantial progress in the computer engineering theory since 2001 when Joel wrote its post. We have better static analysis tools, better coverage tools, and better standard practices. Nevertheless, bug-free software only exists in Programming 101 books.

What about data science projects? Aren’t they essentially a sort of software project? Yes, they are, and as such, the above quote is relevant for them too. However, we can add another statement:

Once you’ve collected data, you can process it as often as you want. This means that almost everything we do as data scientists is something that has never been done before.

You see, to account for project uncertainty, we need to multiply the number of uncertainty factors of a software project by the number of uncertainty factors associated with the data itself. The bottom line is an exponential complexity growth.

## Missing information

Now, let’s talk about another, even bigger problem, the missing information. I’m not talking about “dirty data” — a situation where some values in the dataset are missing, input errors, or fields that change their meaning over the time. These are severe problems but not as tough as the problem I’m about to talk about in this post.

When a software engineer writes a plotting program, they know when it doesn’t work: the image is either created or not. And if the image isn’t created, the programmer knows that something wrong and has to be fixed. When a programmer writes a compression program, they know when they made a mistake: if the program does not compress a file, or if the result isn’t readable. The programmer knows that there must be a fixable bug in his or her code.

What about a data science project? Let’s say you’re starting an advertisement targetting project. The project manager gives you the information source and the performance metric. A successful model has to have a performance of 80 or more (the nature of the performance score isn’t important here). You start working. You clean your data, normalize it, build a nice decision tree, and get a score of 60, which is way too low. You explore your data, discover problems in it, retrain the tree and get 63. You talk to the team that collects the data, find more problems, build a random forest, train it and get a score of 66. You buy some computation time, create a deep learning network on AWS, train it for a week, and get 66 again.

What do you do now? Is it possible that somewhere in your code there is a bug? It certainly is. Is it possible that you can improve the performance by deploying a better model? Probably. However, it is also possible that the data does not contain enough information. The problem, of course, is that you don’t know that. In practice, you hit your head against the wall until you get the results, or give up, or fired. And this is THE most significant problem with data science (and any research) project: your problem is a black box. You only know what you know, but you have no idea what you don’t. A research project is like exploring a forest with your eyes shut: when you hit a tree, you don’t know whether this is the last tree in the forest and you’re out, or you’re in the middle of a tropical jungle.

I hope that the theoretical data science research will narrow this gap. Meanwhile, the project managers will have to live with this great degree of uncertainty.

PS. As in any opinion post, I may be mistaken. If you think I am, please let me know in the comments section below.

The xckd image: https://xkcd.com/303/ under CC-nc. The wandering man image: Illustration: a blindfolded man wandering around. By Flickr user Molly under CC-by-nc-nd

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## What is the best thing that can happen to your career?

Today, I’ve read a tweet by Sinan Aral (@sinanaral) from the MIT:

I’ve just realized that Ikigai is what happened to my career as a data scientist. There was no point in my professional life where I felt boredom or lack of motivation. Some people think that I’m good at what I’m doing. If they are right (which I hope they are), It is due to my love for what I have been doing since 2001. I am so thankful for being able to do things that I love, I care about, and am good at. Not only that, I’m being paid for that! The chart shared by Sinan Aral in his tweet should be guiding anyone in their career choices.

Featured image is taken from this article. Original image credit: Toronto Star Graphic

## Advice for aspiring data scientists and other FAQs — Yanir Seroussi

It seems that career in data science is the hottest topic many data scientists are asked about. To help an aspiring data scientist, I’m reposting here a FAQ by my teammate Yanir Seroussi

Aspiring data scientists and other visitors to this site often repeat the same questions. This post is the definitive collection of my answers to such questions (which may evolve over time). How do I become a data scientist? It depends on your situation. Before we get into it, have you thought about why you want […]

## What you need to know to start a career as a data scientist

It’s hard to overestimate how I adore StackOverflow. One of the recent blog posts on StackOverflow.blog is “What you need to know to start a career as a data scientist” by Julia Silge. Here are my reservations about that post:

### 1. It’s not that simple (part 1)

You might have seen my post “Don’t study data science as a career move; you’ll waste your time!“. Becoming a good data scientist is much more than making a decision and “studying it”.

### 2. Universal truths mean nothing

The first section in the original post is called “You’ll learn new things”. This is a universal truth. If you don’t “learn new things” every day, your professional career is stalling. Taken from the word of classification models, telling a universal truth has a very high sensitivity but very low specificity. In other words, it’s a useless waste of ink.

### 3. Not for developers only

The first section starts as follows: “When transitioning from a role as a developer to a position focused on data, …”. Most of the data scientists I know were never developers. I, for example, started as a pharmacist, computational chemist, and bioinformatician. I know several physicists, a historian and a math teacher who are now successful data scientists.

### 4. SQL skills are overrated

Another quote from the post: “Strong SQL skills are table stakes for data scientists and data engineers”. The thing is that in many cases, we use SQL mostly to retrieve data. Most of the “data scienc-y” work requires analytical tools and the flexibility that are not available in most of the SQL environments. Good familiarity with industry-standard tools and libraries are more important than knowing SQL. Statistics is way more important than knowing SQL. Julia Silge did indeed mention the tools (numpy/R) but didn’t emphasize them enough.

### 5. Communication importance is hard to overestimate

Again, quoting the post:

The ability to communicate effectively with people from diverse backgrounds is important.

Yes, Yes, and one thousand times Yes. Effective communication is a non-trivial task that is often overlooked by many professionals. Some people are born natural communicators. Some, like me, are not. If there’s one book that you can afford buying to improve your communication skills, I recommend buying “Trees, maps and theorems” by Jean-luc Doumont. This is a small, very expensive book that changed the way I communicate in my professional life.

6. It’s not that simple (part 2)

After giving some very general tips, Julia proceeds to suggest her readers checking out the data science jobs at StackOverflow Jobs site. The impression that’s made is that becoming a data scientist is a relatively simple task. It is not. At the bare minimum, I would mention several educational options that are designed for people trying to become data scientists. One such an option is Thinkful (I’m a mentor at Thinkful). Udacity and Coursera both have data science programs too. The point is that to become a data scientist, you have to study a lot. You might notice a potential contradiction between point 1 above and this paragraph. A short explanation is that becoming a data scientist takes a lot of time and effort. The post “Teach Yourself Programming in Ten Years” which was written in 2001 about programming is relevant in 2017 about data science.

Featured image is based on a photo by Jase Ess on Unsplash

## Identifying and overcoming bias in machine learning

Data scientists build models using data. Real-life data captures real-life injustice and stereotypes. Are data scientists observers whose job is to describe the world, no matter how unjust it is? Charles Earl, an excellent data scientist, and my teammate says that the answer to this question is a firm “NO.” Read the latest data.blog post to learn Charles’ arguments and best practices.

https://videopress.com/embed/jckHrKeF?hd=0&autoPlay=0&permalink=0&loop=0

Charles Earl on identifying and overcoming bias in machine learning.

## Fashion, data, science

Zalando is an e-commerce company that sells shoes, clothing and other fashion items. Zalando isn’t a small company. According to Wikipedia, it’s 2015 revenue was almost 3 billion Euro. As you might imagine, you don’t run this kind of business without proper data analysis. Recently, we had Thorsten Dietzsch, a product manager for personalization at the fashion e-commerce at Zalando, joining our team meeting to tell us about how data science works at Zalando. It was an interesting conversation, which is now publically available online.

In the first of our Data Speaker Series posts, Thorsten Dietzsch shares how data products are managed at Zalando, a fashion ecommerce company.

Featured image: By Flickr user sweetjessie from here. Under the CC BY-NC 2.0 license