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For analytics fishes that switched over to Google, I have a masters degree in engineering from UVA and over 5 years of operational data science and operations research work experience. I have finally found the courage to move to industry and could use some help in form of a referral and what a typical analytics interview entails at Google . Please help me out .
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I would stick it out now that you have already started. R is still pretty common, and I feel that it has a lot of advantages over Python for learning data analysis. RStudio is an excellent IDE, the tidyverse libraries make it very easy to manipulate and visualize data, creating statistical models is much easier, and you don’t need to worry about the object oriented aspects of Python.
Once you know what you are doing, making the jump over to python is fairly simple.
Either way, the biggest thing I would focus on is the theory that you need to do effective statistical modeling and machine learning. Languages come and go, and learning a new language is much easier if you already know what it is you are trying to accomplish.
Here is a reddit thread for a similar question that has some interesting points as well: https://www.reddit.com/r/statistics/comments/8de54s/is_r_better_than_python_at_anything_i_started/
Lots of job opportunities. There aren’t many masters in data sciences. I have found it to be very valuable so far. It’s at SMU. Syracuse also has a program and Harvard has a certification
Exactly what D2 said. I can’t understand when people start this R vs Python thing. Remember that these are just 2 languages and languages as you know will come and go. SAS was hot in the market for years before R, Python took over as far as data analytics is concerned. It is very important to pick the statistics concepts, understand the different machine learning, learning how to interpret the results because as a consultant, that’s what you are expected to deliver anyways! Learning R or Python is just a syntax thing which you will keep on getting better as you practice more. But the statistics and machine learning concepts will always be the same and that’s what you are expected to know inside out.
Go with python
R and Python don't matter. There is a three part series on Courses called, Math behind machine learning, do that and understand it.
Once you know that, picking up Python/R isn't hard. I prefer R personally to prototype, but Python is easily more widely used, and Python is among the easiest languages to pick up.
I’d pay attention to this post!
There is a big push among both the R and Python communities to increase the interoperability between the 2 systems - check out the reticulate package in R and other packages by Wes McKinney and Hadley Wickham. Choosing one over the other might not be the right approach because each has their own advantages / disadvantages and as other people have said, the most important part is understanding the underlying logic and theory - after that it is just a matter of semantics.
They are extremely different. And most of R is just knowing actual statistics and how to run each statistics algorithm in R. Once you know statistics well, should only take you a week to learn how to write something in R.
Thank you all! I am trying to go from an MIS background to more ML/analytics work and it’s just such a vast amount of stuff to cover even just to scratch the surface of this stuff.
I am currently getting my masters in data science. R for statistics and cleaning data sets. This can also be done in python. Python is a much more common requirement for data scientist positions. Python is used in machine learning and automating processes. Any of our classes that were in machine learning, natural language processing and even our database courses were all in python. R and SAS were used in our applied statistics classes. You can do a lot of similar things between the two, so some would say it’s whatever your preference is. I think Python/Java/C are somewhat more important and more widely used and will probably benefit you more in the long run. Just a different opinion
My work is now R focused and my grad work is a mix of R/Python. I much prefer R as I find it easier for forecasting and predictions. Stick with R and find those projects!
Plan is to become proficient enough to do my own pet projects outside of work and then use that exp to sell myself to projects as an analyst
PS1 thank you for the response. How are you finding that masters/ can you saw which school or tier of school? How are the job opps looking off campus recruiting?
I prefer R and only know R. But i try to stay on the stats side of things, not the data engineering side
Data Manipulation- both R and Python are Similar. I personally feel python is good for data wrangling/engineering tasks and R is better to bring data from different formats to single platform.
As far as ML stuff goes.it is mostly about algorithms not platform. If you’re good at coming up with algorithms personally R Python or SAS doesn’t matter. Only thing matters is what tools your clients want you to work on
PS1 and BTA OP. I can vouch for SMU data science program as I’m an SMU Alum.It is really good. Professor are very experienced. Eg. one of the professors is chief data scientist at FB
Depends what you want to do. I can do exploratory analysis and creative research much quicker in R. But you can't automate and deploy AWS solutions with R. Creating model endpoints and data pipelines aren't as feasible in R either.
If all you plan to do is stats, then R is fine. I personally chose Python after beginning with R because it helped me pursue other goals not possible in R, but also it has a well established set of machine learning and data mining libraries.
R has some great machine learning libraries GT1. Caret is an excellent package for modeling.
Really appreciate your reply. I was considering taking the GRE and applying to one in a few years to round out all of my self studies