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What would I need to do in order to get a Data Science internship in college?
Are there any technical skills I need to be an expert on? Prior experience?
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6 answers
Updated
Seda’s Answer
I'd recommend taking relevant courses ( databases, statistics, machine learning,etc) during college and work on a few applications to demonstrate your skills. Building a portfolio and a good network is the key. Reach out to your college alumni that have data science or relevant jobs for referrals and inquiries. Good luck!
Thank you so much, Seda!
Jessica
Updated
Adit’s Answer
While in College, it would help to take courses which are related to Data Science. These include courses on Statistics, Python, R, Machine Learning.
There are plenty of technical skills which would help to get an internship. Chiefly, they are:
(1) Python
(2) R
(3) Machine Learning and the important algorithms involved.
(4) If you already have expertise in a language like Java or C#, then look at the packages and libraries related to Machine Learning in that language and build applications using them.
(5) Aside from building applications, it would be important to understand the underlying concepts on what is being solved and how Machine Learning and Data Science help to come up with elegant solutions to tough problems.
There are plenty of technical skills which would help to get an internship. Chiefly, they are:
(1) Python
(2) R
(3) Machine Learning and the important algorithms involved.
(4) If you already have expertise in a language like Java or C#, then look at the packages and libraries related to Machine Learning in that language and build applications using them.
(5) Aside from building applications, it would be important to understand the underlying concepts on what is being solved and how Machine Learning and Data Science help to come up with elegant solutions to tough problems.
Updated
Lavanya’s Answer
1. I would suggest taking courses such as Machine Learning and also focus on your coding skills (SQL and Python are very relevant right now).
2. A good portfolio with all the projects that you have worked on in your spare time is a must if you don't have prior experience. (Download a dataset and start working on it. Find patterns through data visualization and do a short write up)
3. Start networking early.
All the best.
2. A good portfolio with all the projects that you have worked on in your spare time is a must if you don't have prior experience. (Download a dataset and start working on it. Find patterns through data visualization and do a short write up)
3. Start networking early.
All the best.
Updated
Hadi’s Answer
If you are studying in a field closely related to Data Science (some universities offer Data Science programs), you should already have all the technical fundamentals you need to land an internship. In this case, you'd need to i) build and grow your professional networks through school programs and LinkedIn, ii) present yourself through a portfolio of well designed projects with real data. You can use your college projects or start from scratch. You'd need to include your code in Github (you should create an account if you don't already have one).
If you are studying in a relevant quantitative field like physics, engineering, etc, you'd first need to build your fundamentals. To do that, you'd can i) read relevant books (An Introduction to Statistical Learning - https://www.statlearning.com/ is a good one), ii) take online courses (Udacity or Udemy are good options). After that, you'd need to do the two things I mentioned in the above paragraph, i.e. build your professional network and present yourself using a portfolio of the projects.
In the above cases, I assumed you have exposure to programming languages like Python or R. If you don't that's a must, and I'd highly recommend starting with Python.
After you have built your portfolio, you'd need to prepare for the interviews. Many data science positions would require a coding interview or a case study. For the coding interview, you'd need to practice a lot. To prepare, the book "Cracking the Coding Interview" is a good one.
Bonus point: It is highly recommended to participate in data science competition like Kaggle. People who've done well in those competition would have a clear edge.
Good luck!
If you are studying in a relevant quantitative field like physics, engineering, etc, you'd first need to build your fundamentals. To do that, you'd can i) read relevant books (An Introduction to Statistical Learning - https://www.statlearning.com/ is a good one), ii) take online courses (Udacity or Udemy are good options). After that, you'd need to do the two things I mentioned in the above paragraph, i.e. build your professional network and present yourself using a portfolio of the projects.
In the above cases, I assumed you have exposure to programming languages like Python or R. If you don't that's a must, and I'd highly recommend starting with Python.
After you have built your portfolio, you'd need to prepare for the interviews. Many data science positions would require a coding interview or a case study. For the coding interview, you'd need to practice a lot. To prepare, the book "Cracking the Coding Interview" is a good one.
Bonus point: It is highly recommended to participate in data science competition like Kaggle. People who've done well in those competition would have a clear edge.
Good luck!
Updated
Patrick’s Answer
hi - a solid foundation in statistics, decent coding skills in relevant languages, combined with the curiosity, creativity and motivation needed to solve complex challenges.
Updated
Thomas’s Answer
There is no one-size-fits-all answer to this question, as the best way to get a Data Science internship will vary depending on your background and experience. However, some tips on how to land a Data Science internship include:
1. demonstrating interest in data science and analytics through coursework, projects, and extracurricular activities.
2. networking with professionals working in data science roles. I suggest LinkedIn.
3. developing strong programming skills (e.g., Python, R) and statistics skills.
1. demonstrating interest in data science and analytics through coursework, projects, and extracurricular activities.
2. networking with professionals working in data science roles. I suggest LinkedIn.
3. developing strong programming skills (e.g., Python, R) and statistics skills.