Table of Contents
Why do I need to learn statistics?
To summarize, the five reasons to study statistics are to be able to effectively conduct research, to be able to read and evaluate journal articles, to further develop critical thinking and analytic skills, to act a an informed consumer, and to know when you need to hire outside statistical help.
Why is learning statistics important?
Can I learn AI in data science?
An AI/ML practitioner should learn various aspects of data science and machine learning to apply in real-world settings to meet tangible business objectives. The best place to start is to learn tools like Python, related data science libraries and techniques like data extraction and data wrangling.
Is data science replaced by AI?
Although technology is minimizing human workload, in some cases, it is stealing the job from humans. In the worst-case scenario, AI is taking over data science jobs from mankind in 2021. While artificial intelligence is moving all the heavy rocks, it still induces an array of fear among people.
What do you need to learn to become a data scientist?
In order to fill this vacuum in supply, you need to learn Data Science and its underlying fields. Data Science is not a standalone field. It is comprised of several sub-fields. These subfields are Statistics, Mathematics, Computer Science and Core Knowledge.
Why is the salary of a data scientist so high?
It is mainly due to the dearth in Data Scientists resulting in a huge income bubble. Since Data Science requires a person to be proficient and knowledgeable in several fields like Statistics, Mathematics and Computer Science, the learning curve is quite steep. Therefore, the value of a Data Scientist is very high in the market.
What is statistics for data science?
Statistics is a collection of principles and parameters for gaining information in order to make decisions when faced with uncertainty. When someone asks me, “What kind of statistics should I know to become a good Data Scientist?”
Why do industries need data scientists?
While the huge abundance of data can prove useful for the industries, the problem lies in the ability to use this data. As mentioned above, data is fuel but it is a raw fuel that needs to be converted into useful fuel for the industries. In order to make this raw fuel useful, industries require Data Scientists.