I am a PhD student in Biostatistics. I have some questions about career in data science after reading the book So Good They Can't Ignore You By Cal Newport.Career capital is defined as a skill that's equally rare and valuable.The author said there were 2 types of markets, winner-take-all and auction.
- Winner-take-all market has only one type of career capital available. For example, in the field of television writing, quality of scripts is the single career capital. Another example is blogging field. It requires consistently high quality contents alone.
- Auction market has many different types of career capital and each person might generate a unique collection.
Questions:1. Which market does data science belong to?2. If you think data science has only one career capital that a skill is equally rare and valuable, i.e., winner-take-all market, what is that skill?3. If you think data science is auction market (each person might generate a unique collection of career capital),
1. List each career capital and evaluate it based on the criteria: whether it is rare and whether it is valuable.
2. Order your list of career capitals by the criteria
3. Describe persons who have different unique collection of career capitals
After reading the several posts in the forum and articles in ASA magazine, I got a list of skills and am not sure how to evaluate them because of lack of experience in industry. It is not a good answer that all of them are equally important. For me, quality is over quantity. I really do not have time to polish all skills.
- hands on real-world data
- programming
- presentation
- communication
- writing
- networking
Skills outside the list above are welcomed. But sound and considerate answers are more welcomed.For example, you organize the answer as follows (It is just an example, not what I think):Data science is auction market.1. The list of career capitals
* Hands on real-world data:
* Value: It is the reason that data scientists are hired. Thus it is valuable.
* Rareness: All data scientists in the industry have this experience. It is not rare.
* programming
* Value: It is the tool that data scientists use to get real work done. Thus it is valuable.
* Rareness: All data scientists in the industry have this experience. It is not rare.
2. The order of career capitals
1. hands on real-world data
2. Programming
Reason: Even if both of skills are both valuable but not that rare, programming can be delegated to entry-level/junior/research assistant employees but real-world data experience cannot be replaced by others. Hence, real-world data experience is more important than programming. Several colleagues of mine got promoted because of the abundance of real-world data experience even if they are not good at programming or cannot program at all.
(If there are more than 2 career capitals, the best style of answers will be pairwise comparison of all career capitals.)
3. There are 2 collections of skills
* hands on real-world data only
* People who have this skill can become leaders of huge data science projects.
* programming only
* research assistants
Because each project requires too much time for programming, they are hard to get abundant experience of real-world data like people who has the above collection.
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Yingjie Hu
Student
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