I am trying to study prob-stats from Statistics 110, but it's too complex and has all kinds of stuff like beta and gamma distributions. Are these integral stuff necessary for data science internship tests? Are there any other alternatives?
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- $\begingroup$ Please edit the question to limit it to a specific problem with enough detail to identify an adequate answer. $\endgroup$Community– Community Bot2025-05-10 02:03:38 +00:00Commented May 10 at 2:03
- 1$\begingroup$ Kindly explain what Statistics 110 is. Statistics is also all about distribution, so this theoretical knowledge forms the basis of the foundation. Also ,what internship are you looking at? Research Internship? Corporate Internship? $\endgroup$The_Data_Scientist_Man– The_Data_Scientist_Man2025-05-12 05:31:26 +00:00Commented May 12 at 5:31
- $\begingroup$ It's a course by Harvard available on YT, and I am looking for a corporate internship... $\endgroup$yoloboy_24– yoloboy_242025-05-19 18:52:09 +00:00Commented May 19 at 18:52
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0 Welcome to the data science stackexchange !
You don't give enough information in the question, despite requests for further details, and as such all we can do is answer generally.
Generally speaking, YES, you need a solid understanding of probability and statistics in order to be an effective data scientist.
It seems that you are interested in applying for DS internships. These are usually very competitive and you are very likely to come up against applicants that have a strong background in maths, statistics and probability. I would be surprised if you were able to demonstrate abilities that can make up for the lack of this knowledge. Some of these skills are "soft" skills including:
- communication at an appropriate level with team members and stakeholders,
- exceptional understanding of the business(es) and business logic/processes that you will be working on/with (substantive domain experience/knowledge),
while others are technical skills, such as:
- an extensive and deep understanding of programming/coding using Python, including software development concepts such as object oriented design and SOLID principles,
- a extensive and deep understanding of (modern) databases and data warehousing,
- experience deploying and monitoring projects. For example a CI/CD pipeline using github pages that trigger a build when changes are pushed, along with integration with a monitoring tool such as DataDog,
- good source control skills, including how to handle conflicts and merging of pull requests
The above list might be regarded as overkill, and it would be for a typical applicant but the point is that without the maths skills, you have to demonstrate skills that make up for that missing knowledge, and one way to do that is to show fantastic technical skills that would be very unexpected for someone in your position. The following, somewhat simplistic and overused, graphic is a useful description of the required skills for data science.
