Comparing Python, R, and SPSS: Strengths, Limitations, and Use Cases (Full Transcript)

Speaker 1: Hi, hello, welcome to another video. In this video I will compare, or I will read this article which compares Python, R and SPSS. So in my opinion, SPSS is quite limited compared to the other methods of Python and R. However, you can learn SPSS in a few days, but learning Python and R can take some time. And personally, learning R seems to be easier than Python. And for Python, there are different ways of solving a problem, and the community of Python world obviously more crowded than the R. So here let's read this article that compares them. So I tried to click the table, but I'm not sure it will open, so we cannot open it. My vision in data analysis is that there is a continuum between explanatory models on one side and predictive models on the other side. The decisions you make during the modeling process depend on your goal. So yeah, obviously, if you simply want to only write an article and find some associations, then SPSS might be enough, and you don't need to learn Python, and it will slow down the process. When we are looking at SPSS and SAS, both of these languages originate from the explanatory side of data analysis. They are developed in academic environment where hypothesis testing plays a major role. This makes that they have significantly less methods and techniques in comparison to R and Python. SAS and SPSS both have data mining tools, and SPSS modular. However, these are different tools and you need extra licenses. So you need to pay for them, and actually there are not many tools available. For example, if you want to do random forest, there is one way of doing it using only SPSS. But for Python, you have more features, you can investigate more things using the code, and you can ask help from the Python community. One of the major advantages of open source tooling is that the community continuously improves and increases functionality. R was created by academics who wanted their algorithms to spread as easily as possible. Python is developed with a strong focus on business applications, not from an academic or statistical standpoint. This makes Python very powerful when algorithms are directly used in applications. Python is mostly used in data mining and machine learning applications where a data analyst doesn't need to intervene. Python is therefore also strong in analyzing images and videos. For example, we have used Python this summer to build our own autonomous driving RC car. Easiest language to use when using big data frameworks like Spark. So they mention SPSS as the other one is easy to learn and visualization. So obviously SPSS is better for visualization. So R and Python are freely available, but you may not be able to access SPSS. But there are ways to crack somehow, but I don't advise it. It seems to be you can learn both of them and you can combine them and compare the results and your experiences will help you to conclude. So I wish you enjoyed this video and wish you have a beautiful day. And you don't need to be excellent in all languages. You can just be focused on one of them and then you can solve your problems hopefully. And wish you have a beautiful day. Bye bye.