u/GeologistWilling6031

Background: I am currently entering my fourth year , non- circuital branch doing my internship preparation . My genuine interest lies in Data Science and Machine Learning (I’m already comfortable with Python, SQL, Pandas, NumPy, and Scikit-learn), but I am getting some conflicting advice from peers on how to strategize my prep, I currently have my on-campus internships scheduled next semester.

I’d really appreciate some insights from folks in the industry:

1. Math Depth: I am currently learning Probability & Statistics from the STAT110 YouTube lectures since I didn't have a formal course for it in my major. It feels very theory-heavy. How much mathematical depth is realistically expected in Data Science/ML interviews versus actual industry work?
2. P&S Prioritization: Considering the vastness of Probability & Statistics, which specific topics are genuinely important to prioritize for DS/ML roles, and what can safely be skipped?
3. Interview Practice: What are the best resources or platforms to practice interview-oriented Probability & Statistics questions?
4. The Career Dilemma (SWE vs DS): Many advise targeting SWE internships first because opportunities are way more abundant, and then switching to ML internally later. Should I follow this route to maximize my placement chances, or stick to my genuine interest and focus entirely on Data Science without splitting my effort ? The reason that my peers give to apply to all companies is that atleast you will have some chance in getting selected in a good company because the number of data companies is far less the software engineering that come to our campus, and off-campus is way more difficult than on-campus internships BUT I am 100% sure that I won't be going into software engineering later.
5. DSA Requirements: I am currently practicing DSA (using Striver's sheet). How deep do I need to go into DSA specifically for Data Science roles? Is standard interview-level proficiency enough, or is competitive-programming-level expected? P.S. I have seen a good number of data companies ask DSA in online assessments like
   • Google
   • Microsoft
   • Amazon
   • Uber
   • Atlassian
   • Adobe
   • Walmart Global Tech
   • Flipkart
   • Swiggy
   • Zomato

• Internship Screening: What factors matter the most when recruiters look at applications for Data Science internships, especially for someone coming from a non-CS engineering branch?
• Standing Out: What specific skills, topics, or types of projects actually make a candidate's profile stand out from the crowd for DS/ML roles?

Thanks in advance for any guidance, I really appreciate your help !!!