Table of Contents
What are your weaknesses with examples?
Examples of weaknesses on the job
- Inexperience with specific software or a non-essential skill.
- Tendency to take on too much responsibility.
- Nervousness about public speaking.
- Hesitancy about delegating tasks.
- Discomfort taking big risks.
- Impatience with bureaucracies.
What are the possible weaknesses?
List of Weaknesses
- Not taking criticism well.
- Impatient.
- Lazy.
- Easily bored.
- Procrastinate.
- Persistent.
- Takes things personally.
- Strong willed.
What is your strength data scientist?
A passion for solving problems. A data scientist needs to go beyond identifying and analyzing a problem – he or she needs to solve it. The successful data scientists I have worked with don’t just process the biggest data or implement the most advanced algorithm, they solve the problem.
Can you answer data scientist interview questions without technical skills?
Regardless of the company and business field, you can’t possibly answer data scientist interview questions without the knowledge and technical skills, such as: NLP algorithms.
How to answer “what’s your biggest weakness?
If modern job interviews have taught us anything, it’s that the correct answer to the question “What’s your biggest weakness?” is “I work too hard.” Clearly, it’d be ludicrous to actually talk about our weaknesses, right? Why would we want to mention what we can’t yet do?
Are You struggling to land an awesome data scientist job?
Landing an awesome data scientist job isn’t just a luck of the draw. Above all, it’s a matter of preparation. But even if you’re an aspiring data scientist who’s super dedicated to the task, you might find yourself struggling in the process. Why? The reasons are two-fold:
Why is it so hard to do big data analysis?
One is the difficulty in scaling an analysis or a predictive model to large datasets. Most of us don’t have access to a computing cluster and don’t want to put up money for a personal supercomputer. This means that when we learn new methods, we tend to apply them to small, well-behaved datasets.