3 answers
Andrea
Student
Hollywood, Maryland
1
Question
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What traits do people need to be successful in your line of work?
Data-Science and research
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3 answers
Brian Goldman
COO
12
Answers
Miami, Florida
Updated
Brian’s Answer
I know you asked for traits, but I will answer in a slightly different way that may also be helpful if considering these careers...
For Investment Research, you need strong analytical skills and a solid understanding of accounting, but what people often fail to realize is that if you want to become a lead analyst, relationship skills are also very important because there is a high degree of client interaction.
For data scientists, what I often observe holding folks back from advancing in this field is a lack of presentation skills, particularly the ability to get out of the weeds of statistics to simplify the "so what" message in a clear and concise manner to folks who are not experts in statistics and data science methods. This is an opportunity to differentiate.
For Investment Research, you need strong analytical skills and a solid understanding of accounting, but what people often fail to realize is that if you want to become a lead analyst, relationship skills are also very important because there is a high degree of client interaction.
For data scientists, what I often observe holding folks back from advancing in this field is a lack of presentation skills, particularly the ability to get out of the weeds of statistics to simplify the "so what" message in a clear and concise manner to folks who are not experts in statistics and data science methods. This is an opportunity to differentiate.
Drew Givens
Finance System & Processes Change Management
12
Answers
Kapaa, Hawaii
Updated
Drew’s Answer
Agree with Brian and Thomas. To be successful in any line of work, there must be an internal passion and burning desire to make a difference and contribute to making things better. Storytelling is probably the most important skill for a data scientist, above and beyond the technical knowledge of using and applying tools correctly. Brian references presentation skills, which is part of storytelling. It is important to understand your audience and their motivations, as well as the vulnerabilities of your data. Be clear about assumptions and limitations. Each project gives you an opportunity to establish and build your trustworthiness. Break that trust, and it will be difficult to get any audience to listen. With these things in place, you will have a long and successful career in data science.
Sumitra Biswal
Cybersecurity specialist
22
Answers
Bengaluru, Karnataka, India
Updated
Sumitra’s Answer
Hello Andrea
Thank you for bringing up this question. Although we emphasize on upskilling ourselves in field-oriented subjects, one of the most important factor people miss out is the trait that can be inculcated individually.
As a professional working in the intersection of Cybersecurity and Artificial Intelligence, I have observed that in order to be able to resolve a typical problem, one needs to understand underlying factors and fundamentals of the domain.
As for data science and research which is a part of Artificial Intelligence and has strong applications in cybersecurity, I can ascertain that traits like strong analytical thinking and assessing a given problem from its fundamental aspects rather than the surface alone, can make one confident and efficient in the field. For instance, in today's world while literally anyone can develop an application using machine learning, there are rarely few practitioners who truly understand how a certain model was trained, algorithm fueling the model, typical benefits and drawbacks of choosing a certain model over another and so on. Hence, it is important to imbibe features within that can help in critical thinking and analyzing to the root of a given problem statement in data science and research.
I hope I was able to help!
Thank you for bringing up this question. Although we emphasize on upskilling ourselves in field-oriented subjects, one of the most important factor people miss out is the trait that can be inculcated individually.
As a professional working in the intersection of Cybersecurity and Artificial Intelligence, I have observed that in order to be able to resolve a typical problem, one needs to understand underlying factors and fundamentals of the domain.
As for data science and research which is a part of Artificial Intelligence and has strong applications in cybersecurity, I can ascertain that traits like strong analytical thinking and assessing a given problem from its fundamental aspects rather than the surface alone, can make one confident and efficient in the field. For instance, in today's world while literally anyone can develop an application using machine learning, there are rarely few practitioners who truly understand how a certain model was trained, algorithm fueling the model, typical benefits and drawbacks of choosing a certain model over another and so on. Hence, it is important to imbibe features within that can help in critical thinking and analyzing to the root of a given problem statement in data science and research.
I hope I was able to help!