Key skills include proficiency in programming languages such as Python and R, expertise in statistics and mathematics, experience with machine learning algorithms, knowledge of data visualization tools, and strong analytical and problem-solving abilities.
To be a data scientist, essential skills include programming (Python, R), a strong foundation in statistics and mathematics, experience with machine learning, proficiency in data visualization, and excellent problem-solving abilities. Continuous learning and adapting to new tools are also crucial....
A data scientist requires expertise in programming, mathematics, data wrangling, machine learning, data visualization, big data technologies, communication, and domain knowledge to analyze data and deliver actionable insights.
Yes, of course, these abilities are perhaps necessary but not essential. It is strange that nobody mentioned the applications. In my opinion, a data scientist, needs to analyze how real-world problems can be mathematically modeled and solved.
AI is a very valuable resource to get data and even build initial decision matrices, but somebody must establish the parameters
Of course, mathematics knowledge is a must, but more important is to know how to use that knowledge in practical applications.
If the scientist knows Python or R much for the better, but not essential, since a programmer can do that following the instructions of the scientist. What is important is the ideas of the scientist as well as find the way to put them in practice.
As an example, fuzzy logic is a very valuable tool, but nowadays it is normally used to find a crisp average of subjective data, which comes not from analysis, research, discussions as should be, but from the mind of a DM or group of them.
In the first case, fuzzy can be very useful, in the second case it is useless. Of course, there could be a group of DMs that obtain different subjective values, but probably not too much different from the first one, using analysis and research