DATAWORKSHOP CLUB CONF
Procrastination - the mother of all inventions
Few words about Tomasz Brzeziński presentation on DataWorkshop Club Conf 2018...
Tomasz Brzeziński graduated as statistician, hence, he started coding relatively late. Nevertheless, he got to chief data scientist position at iTaxi, thanks to extraordinary procrastinations skills. As analyst at Polsat (main Polish cable channel), he was responsible for data-driven broadcast timetable. Later, as analyst for Netsprint he learned coding and optimizing as data streams were going far beyond Excel capabilities. His appearance at Data Workshop Conf Club is devoted to procrastination, as driving force of many things.
Tomasz Brzeziński graduated as statistician, hence, he started coding relatively late. Nevertheless, he got to chief data scientist position at iTaxi, thanks to extraordinary procrastinations skills. As analyst at Polsat (main Polish cable channel), he was responsible for data-driven broadcast timetable. Later, as analyst for Netsprint he learned coding and optimizing as data streams were going far beyond Excel capabilities. His appearance at Data Workshop Conf Club is devoted to procrastination, as driving force of many things.
How has he got that far?
Actually, this is the total sum of confusing professional situations which turned out to be harsh but essential lessons. Tomasz put them into several bullet points:
- make sure that what you do is actually utilized
- what is marked as asap is not always asap
- a job where your boss just pretends to believe in analytics is a waste of time
Expanded Eisenhower's matrix by Tomasz Brzeziński
* somebody can perceive that task as important - delegate it to be fair!
** there may be not urgent, unimportant things you love doing, do not resign from them, and don't blame yourself for wasted time, enjoy!
* somebody can perceive that task as important - delegate it to be fair!
** there may be not urgent, unimportant things you love doing, do not resign from them, and don't blame yourself for wasted time, enjoy!
Procrastination is the art of doing what excites you and is important not else
For the sake of work well being ask these questions first when somebody comes with urgent analytical request:
For the sake of work well being ask these questions first when somebody comes with urgent analytical request:
- Why is it so crucial?
- What essential questions would it answer?
- Will the analysis enable us to make a decision?
Reasons for not playing Kaggle
It's not that Kaggle is just to show off, but although Tomasz sees nothing bad in competitions, his view on Kaggle is that all the problems are driven down to really unreal scenarios, purely technical and quite standard. At the same time he says that if he didn't have real business problems to solve at work, he would probably play Kaggle. The second issues is minimal difference between the winners. His judgement is that there are several equally good algorithms and the winner is just a matter of lottery pick - which one accidently fits better to the evaluation set. Furthermore, Tomasz's biggest concern is that there is no continuation of your work, you submit the code and that's it, you no longer exist in the process. That negatively impacts his feeling of sense as you have absolutely no influence on what will happen next. Finally he prefers to fix several problems well than solve one perfectly. But this is his way, if you feel that competing on Kaggle is beneficial to your development - Tomasz encourages you to do so!
What data science is not?
Concluding his presentation with a little manifesto, Tomasz states that Data Science is not a technical discipline - it incorporates technical methods but, first and foremost, it is a conceptual discipline. The focus is on understanding the point of improving a process, usually a business one. It's not about using the tools, as the idea matters - the ability how to approach a specific issue.
The ultimate take away from the presentation is that a real business case has usually nothing to do with idealized world of models - it requires non standard approach. The fact is that person knowing just technical aspects of data science without the ability to think in a conceptual way usually is on a lost position - the world isn't like the Iris data set. Having said that Tomasz adds that in order to deliver non standard approaches one must understand every step in the algorithm. Black boxes will doom you to fail sooner or later.
The ultimate take away from the presentation is that a real business case has usually nothing to do with idealized world of models - it requires non standard approach. The fact is that person knowing just technical aspects of data science without the ability to think in a conceptual way usually is on a lost position - the world isn't like the Iris data set. Having said that Tomasz adds that in order to deliver non standard approaches one must understand every step in the algorithm. Black boxes will doom you to fail sooner or later.
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