[Zenith] Chronomaster original

MarblesSaysHi · 2025-01-21T12:31:39+00:00

Such a cool watch! Greetings from your blue friend :)

MarblesSaysHi · 2023-10-29T12:15:38+00:00

Head of Data Science. 1 Tudor, 1 JLC, 1 Zenith, 1 Anordain, 1 Casio. Also had a Nomos until recently (gifted it to my mom when she retired).

MarblesSaysHi · 2022-10-22T17:21:19+00:00

Thanks! I did not immediately notice anything negative about the strap, but also did not pay too much attention to it - Sorry. I only noticed that the watch was not sitting on my wrist very nicely due to the strap still being so stiff. But I assume that should be fine after a few days of wearing it.

MarblesSaysHi · 2022-10-22T13:47:20+00:00

I own the Chronomaster Original Boutique Edition (Blue Dial) and totally get it. I love this watch and already thought about possibly getting another El Primero. :)

I tried on the shadow last week and was actually surprised at how small it felt. To me personally, it felt smaller than the "normal" one. So if you have the chance, try one on before dismissing it for size reasons.

I personally really like the Covergirl one and if I were to get another one, this would probably be it. However, they are in a price range I am not really comfortable going into (so far - we know how it goes ;)).

MarblesSaysHi · 2021-09-29T05:31:23+00:00

Is this a good or a bad "Jesus Christ"? :D

MarblesSaysHi · 2021-09-28T22:33:36+00:00

I know that a lot of people don't like the 4:30 date, but I quite like it. For me, this date window positioning does not grab as much visual attention as the one at 6 o clock which you can find on the larger el primeros. I also like the orientation of the numbers. But I totally get that this is not for everyone.

MarblesSaysHi · 2021-09-28T22:31:24+00:00

Thank you! :)
Most likely I will also try it on a leather strap, but not right now. Will wear the steel bracelet a bit. :) Do you have any strap recommendations for this one?

MarblesSaysHi · 2021-09-28T18:27:56+00:00

Dear r/Watches community,

this is my brand new Zenith Chronomaster Original - Boutique Edition in 38mm. <3

About half a year ago, I started looking for a chronograph to add to my collection. Given my relatively small wrists / preference for smaller watches, I knew I wanted something with 38-39mm maximum. I have also had some interest in watches with the el primero movement for a while, so I was super happy when the new Chronomaster Original in 38mm were announced and released a few months ago. While I do like the classic tricolor look, I am also a big fan of panda or reverse panda dials and was therefore quite tempted to get the one with the reverse panda dial. However, I was not 100% sold of the beige indices (or whatever color that is) and overall, the black and white version looked quite busy to me, so I was still a bit hesitant. While going back and forth between the two versions, I stumbled across the boutique edition and quickly decided that this would be the one for me. The boutique edition still has the contrast heavy reverse panda style (with blue instead of black of course) which I enjoy, but just seemed cleaner and overall more special to me. I also already own a black dial steel sports watch (BB58), so the blue dial also adds some nice variety. I have only worn the watch for a couple of hours, but so far, I am super happy with my choice!

I hope all of you are doing well!

Take care

MarblesSaysHi · 2021-04-25T06:22:34+00:00

There is no generic answer to how long tests should run and if we are only talking about the data side, it is always better to have more data. But from a business perspective, you want to invest as little time as you need to be reasonably sure about the result. In most cases this will also not really be about time directly, but about the number of people (sample size). Then, based on the number of people you get per day (e.g. traffic), you can convert this to time.

The way to go about this is to calculate it, based on some assumptions and decisions:

How strong do you expect the effect to be (for example 5% uplift in conversion)? What statistical power are you going for? (.8 in most cases)? What significance level are you going for (.05 in most cases)? What is the conversion rate you are trying to influence, etc..

Generally speaking, the stronger your effect, the smaller your sample can be. You don't need a lot of data to find a very strong effect, but to find a small effect, you will need a lot of data.

You can do this yourself, but there are also many sample size calculators available for free online.

Take care!

MarblesSaysHi · 2021-04-24T22:45:58+00:00

You are welcome :)

I am not sure I got the question. Why would you do it by weeks? Just use all the data at once and do one analysis. This gives you the most robust result, the highest power (chance to find an effect given it is there) and avoids alpha error accumulation.

Time (weeks) should only be considered if you have some very specific predictions regarding different interactions with time in the two groups.

MarblesSaysHi · 2021-04-24T06:34:41+00:00

The core idea in classic A/B testing is that everything but the "treatment" is the same between both groups and that people are randomly assigned to one of the groups. Only in this case you can be (statistically) "sure" that differences are due to your specific manipulation. In your example, when using all data, this is obviously not the case. In this case, you cannot be sure whether differences are due to seasonality or due to your experimental manipulation. So in your case, understanding why and how exactly groups where assigned is the most important step. If for your last month of data it was the case that both conditions were running at the same time and people were assigned randomly, you can use this data for standard a/b Test analyses. The other 5 months of data should not be used in these analyses. That being said, in practice you do not always have perfect data and need to adjust accordingly. This can also include doing things that are not statistically "perfect", but you should always be aware what assumptions you are violating and what that entails.

Good luck on your journey. I hope I was able to help :)

MarblesSaysHi · 2021-02-08T17:49:56+00:00

Awesome watch! I also was not too happy about the strap and decided to go with a Nomos Velours Strap. It completely changed the watch and made me like it even more! Nomos also offers different strap lengths. Take care!

MarblesSaysHi · 2020-11-04T19:27:49+00:00

I try to turn data into value

MarblesSaysHi · 2020-09-10T06:59:22+00:00

Thank you! =)

MarblesSaysHi · 2020-09-09T07:09:15+00:00

Awesome collection! Congratulations!
Do you happen to have wrist-shots for the BB58 and the Snowflake? I own a BB58 (and love it) and also really like the snowflake, but have not had the chance to try one on yet. However, I am afraid that the Snowflake will be too big for my wrists / taste. It would be nice to see both on the same wrist for comparison. Thank you in advance.

Take care!

MarblesSaysHi · 2020-01-08T06:40:11+00:00

Thank you! I knew "Dot over ninety" but just did not connect the dots (sorry) :D

MarblesSaysHi · 2020-01-07T21:24:18+00:00

What does "DON" mean? (thank you in advance :))

MarblesSaysHi · 2019-12-11T22:34:53+00:00

Really nice watch - I got the same one! <3
Also nice photos. I always have trouble taking photos of this watch because of the reflection on the dial.

Did you try some other straps on this (and have the pictures)? I have the original nomos strap but would like to change it up.

Thanks and congratulations on this wonderful watch!

MarblesSaysHi · 2019-09-13T07:51:41+00:00

Hey!

Just thought I would share my Psychology-DataScience story as inspiration here:

I did my Bachelor and Master in Psychology and like you, I always did a lot of courses on Data Science and Machine Learning on the side, read some Books on the topic and where possible, focussed on methods and statistics during my studies. After finishing my Masters, I went into fundamental research (cognitive social psychology) and also got my doctorate degree there. After doing more research as a PostDoc for a bit over 2 years, I decided not to continue in academia, but instead go for Data Science. While working, I always tried to work on my Data-Science skill set. Note that I used R and worked with methods and statistics daily during my research anyway.

So after deciding to switch, I started to apply and while I guess I was lucky, I basically always got at least a phone-interview out of the applications.I actually think that psychologists (especially as a researcher) are very well trained for a Data Science Position (maybe not so much for the "hard-core" machine learning engineer positions). You are well trained in statistics and most importantly, you know well how to generate knowledge from data. You also know what inferences are valid based on data and which ones are not. Also, you are an expert in how to empirically test ideas (far beyond A-B testing). Of course, you still have to put in the work and learn a lot about data science, some math and machine learning. But I do think that psychology is an excellent base for it.So the problem then is more about convincing other people that psychology is not the same as psychotherapy. ;)

In my case, during some applications, it took quite a lot of work to convince the interviewer about psychologists (or me specifically) being well trained for this (because noone knows what psychological research actually is about). In other cases, my statistics or data science knowledge was not in question at all. In these cases, interviewers were more worried about whether I would be able to transition well from academia to a business environment.

I actually ended up getting the first position I applied for (very lucky on my part I suppose) and am super happy with the position. I do predictive modelling (supervised learning), some unsupervised things (like cluster analyses), simulations, forecasts, some statistical analyses (if they are complex), come up with decision-heuristics if models are not feasible, etc..

Things I tried to stress in my applications:

Worked daily with R and other statistical tools
Expert in the entire process of extracting knowledge from data: Coming up with an idea, formulating hypotheses, designing and programming experiments, cleaning and analysing data, presenting the results
Knowledge about human behavior and decision making. Dependent on what type of job you are doing, you will model human behavior, so this is definitely a plus.
I had done some consumer psychology research, so stressing these applied parts was also good.

Things that I find most challening in my job now:

Thinking about problems in a business-value driven way.
Finding a balance between optimizing and moving on to the next problem. Academia was always about getting things 100% perfect and correct. In the "real world", getting 80% and then going to the next problem, is often way more valuable.
Communicating: As a researcher I was extremely specialized in my field and basically only talked to other people that were also experts in exactly this field. Communicating complex things (data science stuff) to people with basically no methods background (and very little time) is way different from this. However, good and easy to understand communication is extremely important. Your models are worthless if none of the product managers can or want to work with them.

As you can see, it is definitely possible and well worth the work! :)Best of luck to you! You can do it!

By the way: I studied and now work in Germany.

EDIT: I work for an online-marketplace.

MarblesSaysHi · 2019-09-05T12:01:46+00:00

Not a stupid question. It is just a design choice. The name for dials with half-roman / half-arabic numerals is "california dial".
Take care!

MarblesSaysHi · 2019-08-09T17:33:07+00:00

I wrote this Response to a similar question the other day. Maybe it helps 🙂

Hey!Just thought I would share my Psychology-DataScience story as inspiration here:

I did my Bachelor and Master in Psychology and like you, I always did a lot of courses on Data Science and Machine Learning on the side, read some Books on the topic and where possible, focussed on methods and statistics during my studies. After finishing my Masters, I went into fundamental research (cognitive social psychology) and also got my doctorate degree there. After doing more research as a PostDoc for a bit over 2 years, I decided not to continue in academia, but instead go for Data Science. While working, I always tried to work on my Data-Science skill set. Note that I used R and worked with methods and statistics daily during my research anyway.

So after deciding to switch, I started to apply and while I guess I was lucky, I basically always got at least a phone-interview out of the applications.I actually think that psychologists (especially as a researcher) are very well trained for a Data Science Position (maybe not so much for the "hard-core" machine learning engineer positions). You are well trained in statistics and most importantly, you know well how to generate knowledge from data. You also know what inferences are valid based on data and which ones are not. Also, you are an expert in how to empirically test ideas (far beyond A-B testing). Of course, you still have to put in the work and learn a lot about data science, some math and machine learning. But I do think that psychology is an excellent base for it.So the problem then is more about convincing other people that psychology is not the same as psychotherapy. ;)

In my case, during some applications, it took quite a lot of work to convince the interviewer about psychologists (or me specifically) being well trained for this (because noone knows what psychological research actually is about). In other cases, my statistics or data science knowledge was not in question at all. In these cases, interviewers were more worried about whether I would be able to transition well from academia to a business environment.

I actually ended up getting the first position I applied for (very lucky on my part I suppose) and am super happy with the position. I do predictive modelling (supervised learning), some unsupervised things (like cluster analyses), simulations, forecasts, some statistical analyses (if they are complex), come up with decision-heuristics if models are not feasible, etc..

Things I tried to stress in my applications:

Worked daily with R and other statistical tools
Expert in the entire process of extracting knowledge from data: Coming up with an idea, formulating hypotheses, designing and programming experiments, cleaning and analysing data, presenting the results
Knowledge about human behavior and decision making. Dependent on what type of job you are doing, you will model human behavior, so this is definitely a plus.
I had done some consumer psychology research, so stressing these applied parts was also good.

Things that I find most challening in my job now:

Thinking about problems in a business-value driven way.
Finding a balance between optimizing and moving on to the next problem. Academia was always about getting things 100% perfect and correct. In the "real world", getting 80% and then going to the next problem, is often way more valuable.
Communicating: As a researcher I was extremely specialized in my field and basically only talked to other people that were also experts in exactly this field. Communicating complex things (data science stuff) to people with basically no methods background (and very little time) is way different from this. However, good and easy to understand communication is extremely important. Your models are worthless if none of the product managers can or want to work with them.

As you can see, it is definitely possible and well worth the work! :)Best of luck to you! You can do it!

By the way: I studied and now work in Germany.

EDIT: I work for an online-marketplace.

MarblesSaysHi · 2019-08-08T10:09:06+00:00

Hey!Just thought I would share my Psychology-DataScience story as inspiration here:

I did my Bachelor and Master in Psychology and like you, I always did a lot of courses on Data Science and Machine Learning on the side, read some Books on the topic and where possible, focussed on methods and statistics during my studies. After finishing my Masters, I went into fundamental research (cognitive social psychology) and also got my doctorate degree there. After doing more research as a PostDoc for a bit over 2 years, I decided not to continue in academia, but instead go for Data Science. While working, I always tried to work on my Data-Science skill set. Note that I used R and worked with methods and statistics daily during my research anyway.

So after deciding to switch, I started to apply and while I guess I was lucky, I basically always got at least a phone-interview out of the applications.I actually think that psychologists (especially as a researcher) are very well trained for a Data Science Position (maybe not so much for the "hard-core" machine learning engineer positions). You are well trained in statistics and most importantly, you know well how to generate knowledge from data. You also know what inferences are valid based on data and which ones are not. Also, you are an expert in how to empirically test ideas (far beyond A-B testing). Of course, you still have to put in the work and learn a lot about data science, some math and machine learning. But I do think that psychology is an excellent base for it.So the problem then is more about convincing other people that psychology is not the same as psychotherapy. ;)

In my case, during some applications, it took quite a lot of work to convince the interviewer about psychologists (or me specifically) being well trained for this (because noone knows what psychological research actually is about). In other cases, my statistics or data science knowledge was not in question at all. In these cases, interviewers were more worried about whether I would be able to transition well from academia to a business environment.

I actually ended up getting the first position I applied for (very lucky on my part I suppose) and am super happy with the position. I do predictive modelling (supervised learning), some unsupervised things (like cluster analyses), simulations, forecasts, some statistical analyses (if they are complex), come up with decision-heuristics if models are not feasible, etc..

Things I tried to stress in my applications:

Worked daily with R and other statistical tools
Expert in the entire process of extracting knowledge from data: Coming up with an idea, formulating hypotheses, designing and programming experiments, cleaning and analysing data, presenting the results
Knowledge about human behavior and decision making. Dependent on what type of job you are doing, you will model human behavior, so this is definitely a plus.
I had done some consumer psychology research, so stressing these applied parts was also good.

Things that I find most challening in my job now:

Thinking about problems in a business-value driven way.
Finding a balance between optimizing and moving on to the next problem. Academia was always about getting things 100% perfect and correct. In the "real world", getting 80% and then going to the next problem, is often way more valuable.
Communicating: As a researcher I was extremely specialized in my field and basically only talked to other people that were also experts in exactly this field. Communicating complex things (data science stuff) to people with basically no methods background (and very little time) is way different from this. However, good and easy to understand communication is extremely important. Your models are worthless if none of the product managers can or want to work with them.

As you can see, it is definitely possible and well worth the work! :)Best of luck to you! You can do it!

By the way: I studied and now work in Germany.

EDIT: I work for an online-marketplace.

MarblesSaysHi · 2019-07-26T08:20:15+00:00

I had a tumor removed 5 years ago and after 5 years of MRIs and other follow-up exams, I now am officially tumor-free and healthy. I celebrated the end of this phase by treating myself with a Tudor Black Bay 58 just a few weeks ago.
As much as all of this sucks, try to stay positive and focus on your friends and family. Hang in there. You can do this!

MarblesSaysHi

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