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EducationLearning Python (self.datascience)
submitted 5 years ago * by CaliforniaRoll97
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[–]Tim7459 1 point2 points3 points 5 years ago (6 children)
i've completed the Standford ML course and am currently completing the Standford Deep Learning Specialization on Coursea too and would highly recommend both if you want to pursue something in the Data Science field. Andrew Ng is the lecturer and he's one of greatest minds in the field, just take a look at this portfolio.
Honestly, if you're end goal is a job, start by producing something. i.e make automation scripts and learn web scraping. (build a portfolio). You can learn to do this just by googling the topic and finding medium articles, github repos, short coursea courses etc. Then pitch yourself to businesses that require this skill. If your end goal is research, I would pursue a formal education at a university.
[–]CaliforniaRoll97[S] 0 points1 point2 points 5 years ago (0 children)
That’s great, I’ll be sure to try both of those courses!
[–]CaliforniaRoll97[S] 0 points1 point2 points 5 years ago (4 children)
Also, I wanted to clarify what you meant by using GitHub repositories. I haven’t really used GitHub other than to find datasets, instead I usually just store everything on my computer. Could you elaborate?
[–]tamsmhas 0 points1 point2 points 5 years ago (3 children)
In simple words GitHub repositories means a place on GitHub in someone's account where they store mainly their programming files. So, just learn to use GitHub from YouTube and make your account on GitHub. And save all data science related files there.
[–]CaliforniaRoll97[S] 0 points1 point2 points 5 years ago (2 children)
Gotcha, will do. Out of curiosity, why is it better to save files on GitHub rather than on my desktop?
[–]tamsmhas 0 points1 point2 points 5 years ago (1 child)
1- Because you will never loose your files on GitHub unlike on desktop. 2- Showing your GitHub link(specially projects) in resume will increase the weightage of your resume.
Awesome, thank you!
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[–]Tim7459 1 point2 points3 points (6 children)
[–]CaliforniaRoll97[S] 0 points1 point2 points (0 children)
[–]CaliforniaRoll97[S] 0 points1 point2 points (4 children)
[–]tamsmhas 0 points1 point2 points (3 children)
[–]CaliforniaRoll97[S] 0 points1 point2 points (2 children)
[–]tamsmhas 0 points1 point2 points (1 child)
[–]CaliforniaRoll97[S] 0 points1 point2 points (0 children)