Data Science: To be or not to be
Assigned Readings
- Data Scientists vs Statisticians - Medium
- Machine Learning Engineer vs. Data Scientist
- Data Science vs. Data Analytics vs. Machine Learning - Expert Talk
- What is data science vs. statistics? - The Signal
My 2¢
Before I start, here is my disclaimer:
The following are my personal opinions about the title of data scientist. They are definitely biased but I don’t mean in anyway to be neither judgmental nor discriminated.
Not All Data Scientists are Scientists
I think the title of Data Scientist is inflated quite a bit nowaday. This may be biased because English is not my native language and I tend to interpret the terminology based literally based on my language skills.
In Chinese, the professional titles with suffix “學家” (similar to English suffix “-ist” in scientist or “-ian” in statistician), are for the people who dedicate into novel research in order to expand the human’s knowledge frontier. Here are some examples:
- 科學家 scientist
- 物理學家 physicist
- 化學家 chemist
- 思想家 ideolohist
- 統計學家 statistician
- 數學家 mathematician
On the other hand, for the professionals who are experts doing something based on what we already know, their titles are emphasized by the suffix “師”. For example:
- 工程師 engineer
- 設計師 desiner
- 雕刻師 sculpturer
- 烘培師 baker
Therefore, as a Chinese native speaker, I don’t think data scientist are all true “scientists”. Instead, many of the data scientists are skilled engineers in computer science and engineering.
I have a higher standard for myself. Even if I have a data science job, I don’t automatically attribute myself as a data scientist. Thus, I decided to start this master program to polish my knowledge and skillset to be qualified as a true data scientist. Without them, I would always think myself as a data science engineer instead.
Data Scientist vs. Statistician
- The similarity, already mentioned above, is their delication to pioneer the field of data science.
- The difference is the approaches they take:
- Data scientists apply various new tools including multiple maching learning algorithms to create novel ways to solve problems we cannot solve before. For example, applying deep learning and image recognition to makes automatic driving cars possible.
- Statisticians often focus on developing or improving one machine learning algorithms by digging deeply in theory studies. Unlike data scientists, they are not satisfied with a new problem solved by using a complicated neural network. In addition, they want to know why it works in a fundmental level and what is its application limits.
In other words, data scientis are like inventors who will apply multiple tools and materials, and develope novel processes to make new things. Statisticians are like scientists who observe, experiment, formulate theories and test them so that we can get a solid understanding about this universe.
With all being said, my goal is to become a data science statistian.