Tag Archives: statistics

What careers are avaliable that require an extensive knowledge of mathematical logic?

Russian_Sherlock_Holmes_1Besides Computer Engineering / Computer Science, of course. Naturally, this is a Quora question once again.

As a good example, any profession that involves data analysis, processing or modeling, requires a solid knowledge of mathematical logic. By solid knowledge I mean finishing at least one semester course on formal logic at a university.

The reason for this is that data is represented to us as numbers, and studying numbers is governed by mathematics. Numbers belong to mathematics, they were born within mathematics. And any type of work in maths requires decent understanding of formal logic.

Therefore, not only careers in Computer Science, but also careers in data science, machine learning, quantitative finance, economics or meteorology all demand having a good knowledge of logic.

Additionally, formal logic is instrumental in any profession that requires Sherlock Holmes-level deduction and argumentation on the basis of facts. This includes lawyers and criminal investigators. They do have courses on formal logic in some of the better institutes.

Just having a good common sense can’t fully replace a course in formal logic. Since the times of the Russell or Zermelo paradoxes, it was understood that the so-called naive logic (based on unformalized common sense and the naive set theory) is self-contradictory. Good understanding of basics of formal logic prevents you from running into paradoxes in your debates.



How can a Computer Science student achieve the same level of knowledge of Statistics as that of a Statistics graduate?

This is one of those questions that I am getting asked on a regular basis. When I saw this in my Inbox mailed to me by Quora, I knew it’s time to step up and speak out 🙂

In fact, that’s a very easy question that has a straightforward answer. Of course, the answer has to be generic, because there is no way to give a specific answer addressed to an abstract “Computer Science student”.


In order to achieve the same level of knowledge in statistics as a statistics graduate, you have to put in the same total effort to learn statistics as a statistician does. There’s no shortcut.

The same principle is valid the other way around. If a mathematics and statistics graduate like myself would like to learn to code as good or better than CS students, I could only achieve this by spending a lot of time improving my coding skills.

However, in case if you are already familiar with machine learning, then you have already made substantial efforts in the area and you are definitely not a beginner.

If you continue learning and reading about those areas of statistics that are relevant to your work, you will keep getting better and there will be no limit to your improvement.




Learning and being open vs being a yes-man

There is a very important topic of being open to new ideas and being able to learn new tools related to your profession, and it amazes me that this subject causes quite a bit of misunderstanding. So I felt that it might be a good idea to express my thoughts on the matter and to clarify some misconceptions.

I work on extracting knowledge or business value from data and information. This is a truly interdisciplinary area where we use machine learning, statistics, mathematics, computer science, together with software development and business analytics. Essentially, on a daily basis.

Of course, when so many tools are involved and so many skills are required, there is noone who knows everything in this profession. Therefore, it is essential that you learn something new or perfect some of your skills on a daily basis. I followed this advice since my student days and now, after 20 years, there are still endless opportunities to learn something useful and it is extremely easy to find an area where to improve yourself.

A crucial ingredient to success of this approach is the ability to evaluate your skills adequately. You hinder your progress or even stop it completely if you get overconfident and generic in your self-evaluation. On the other hand, you are also wasting your time and efforts if you are undervaluing some specific skills that you’ve already developed. Adequacy is the key.

Now let’s give a very common example. In my interactions with dozens of departments, I probably had close to a 100 situations of this type 🙂

Imagine you’ve done a Ph.D. on hypothesis testing or published papers on classification in machine learning. And then there comes a colleague of yours who just saw a Wikipedia article on A/B testing or read some blog entry about that new hot classifier that was recently proposed in a poster at the local college conference on AI.

Your colleague is all excited, says that’s the coolest thing since sliced bread and demands that you write a seminal joint paper with him or, at least, abandon all of your current work and reorganize the company analytics to have his new favorite method at the core of the business 🙂

A typical twist in this type of stories comes from the fact that you’ve probably either already tried this method and found a better one, or you know that the method simply doesn’t work for your task. After all, sometimes this can be a mathematically provable statement. And you tell this to your colleague, as politely and constructively as possible, of course.

Now, there’s a probability that the colleague still gets upset and claims that you’re a know-it-all, and that you aren’t willing to learn new things and that you aren’t open to new opportunities.

Do not get into the trap of feeling guilty and switching to the obviously ineffective method. These remarks aren’t coming from your colleague’s brain, they come from his ego that is temporarily hurt. If you both waste your time working on something bogus, this will do no good for both of you and for the employer.

At the end, you might feel even more guilty for not stopping the madness earlier, you can damage your reputation as a domain expert, and your colleague might still blame you for the fact that his bad idea didn’t work out as he expected. A fairly unattractive perspective if you ask me 🙂

Willingness to abandon your good results and to discard your knowledge just because someone else got excited about a new buzz word, is not to be confused with openness to new ideas and willingness to learn and improve.

A proper name for the former is being a “yes-man” and a pushover. You wouldn’t learn or improve by working on a bogus method, indeed.

As for you colleague, if he is really open to new ideas, eager to learn something new, and strives for perfection, a moment like this is exactly the right time for him to prove it and to learn something from you. Something useful.