What is the long term goal of a data scientist?
The long-term goal of a data scientist should be to become a subject matter expert in a particular field of data science (be it machine learning, or predictive modelling, etc). This would ensure that the individual has sufficient knowledge and skillsets to manage all aspects of data science projects; however if certain high caliber projects come up in a particular niche field of data science; the individual has the necessary credentials to support those high-visible projects.
This would, in turn, make the individual indispensable for the organization, and the same would have a positive impact on the earnings of the individual as well.
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Can I be a Data Scientist without Mathematics and Statistics?
Becoming a successful data scientist without knowledge and acumen in mathematics or statistics is highly improbable as these are the fundamental blocks of data science. All statistical models, optimization algorithms, machine learning algorithms, ...
Which is better in terms of salary and long term growth in data science and machine learning.
Data science is a more generic stream of study which encompasses data analysis, machine learning, data visualization, statistical modeling, data engineering, business intelligence, etc. Therefore, from the perspective of opportunities, data science ...
Which of the career option is better full stack developer or data scientist.
Full-Stack developers and data scientists are both extremely promising career paths in terms of future prospects, demand as well as the lucrativeness of the opportunity. However, the road to becoming a full-stack developer is completely different ...
Do data scientists need to learn web development?
Whether data scientist needs to learn web development or not largely depend on the organization that he or she is working. For larger companies, there are dedicated teams for web development who caters to the deployment of applications to the web; ...
Some tips for the for aspiring Data Scientists.
In order to become a successful data scientist, it is important not to jump the gun and directly development into statistical models and Python/R commands which could simply take inputs and provide the model output. Rather, the process should be more ...