How can you use R and Python for statistical analysis in business decision-making?

- R is actually not slower than python. data.table package in R is multi-thread by default and extremely fast. Just that it has its own syntax. - Python is great too, but not so great in statistics, econometrics and visualisation. - I agree R has a steep learning curve, but once you get it, it is very easy and convenient. - In 99% of the use cases data is not huge. So, one can use R (using tidyverse) without worrying about performance at all.

I actually disagree with the line “R has a steep learning curve”. I’ve found the R community to be very welcoming and deeply passionate about the language. Combine that with easy readability of the tidyverse, I think R has potential to be used widely in business if people give it a chance.

In my previous function we were using R to perform statistical modeling of default rates and other risk related parameters. I have to admit that initially, the syntax of R was a novelty (to me as someone more used to other programming languages) but after a short while I came to appreciate the structure which was created for the purpose of statistics exactly. Since then we have expanded the use of R not only to pure modeling but also to interactive tools with web gui (with shiny), great interactive plots (via plotly) and other applications.

R and Python are powerful tools for statistical analysis in business decision-making. Here's how you can use them: 1. Data preparation 2. Exploratory data analysis (EDA) 3. Hypothesis testing 4. Regression analysis 5. Machine learning 6. Time series analysis 7. Decision support systems

I disagree R is slow. R is an interface. The methods or packages accessible from that interface can be written in C and then are very fast, especially when combined with multithreading. An example of such a package is “fastLink”, which is the fastest package in the record-linkage business.

In my experience the implementation of Python based projects is only as strong as the general technical abilities and acceptance of the organization's workforce. Suggestion: adopt a no-code solution for your organization or adopt company wide training in the tech stack you are looking to promote.

In my experience, Python has a very large base of users and support community. Compared to R it is more efficient possibly, however, still it is less efficient than some other languages such as C++ or C. Python can be also confusing due to its overly simplistic and sometimes implied syntax. This can cause issues for developers with more computing background. However, Python is a great and versatile tool. It is great to learn programming and has a superb use potential and unmatched number of data/AI/ML libraries. Python is also a great language to transfer to software/data from other fields. It is very intuitive to a non-computing background users.

In my experience in using Python, integrating with the Jypter interface is more user-friendly for newcomers and it can handle larger datasets. A lot of tutorials to learn about.

In college days, I created a cron job for price alert using python. It became my first love. I think Python has got you solution for a lot of things. It depends upon your needs which definitely is niche specific. In the paragraph you've written about less active community and less support for statistical methods and libraries. Well I don't think so. Just check github . I think python is booming.

For the future development, it is very much essential of Python language. It can use with other languages. It easy to learn & use.

Python excels in statistical analysis due to its versatility, speed in data processing, simplicity for programmers, and ample learning resources. Its extensive libraries enable robust data exploration and visualization. Python's efficiency in handling data, along with its straightforward syntax, makes it accessible to various backgrounds. The vast online community ensures quick learning. While R has its strengths, Python's broad applicability and user-friendly nature make it a compelling choice for effective statistical analysis.

I think its also a question of synergy in the workplace or with the subject. If many experts are working with the language in the subject of interest, it could be better to get familiar with that language first. In R, the econometric and statistics packages are often written by the subject experts. It can also integrate C++/C code if performance is essential. Plotting with ggplot2 is also something worth learning about for me. Python is a classic computer scripting language. IT experts build many modules to interact with operating systems and hardware. E.g., Running servers, altering firewall dynamically, circuitry control, etc.

It’s more a matter of perspective: consider learning one coding language first and then the other, instead of choosing one over the other. There are benefits to this approach: It fosters a comfortable mindset, which can increase brain efficiency. And It’s more practical in the sense that you can align with your work environment and enhance cooperation with your colleagues. However, as is commonly noted, the general rule of thumb is: if you’re focused on modeling/regression, start with R; for data mining/AI, Python is preferable. Of course, other tools should be considered as needed. For instance, if you work in a big bank that emphasizes security and prefers SAS, or you have a crush on someone who likes Matlab, then your choice is clear.

R and Python serve distinct purposes in business analytics. R's rich suite of packages makes it the preferred choice for exploratory work and complex visualizations, perfect for market research and customer insights. With its speed and expansive libraries like Pandas, Python excels in handling large datasets and predictive modeling, which is beneficial for operational decisions. When choosing, consider your end goal: R for statistical depth and visualization, Python for faster data processing, and integrating analysis with broader applications.

- Choose R for: - statistical analysis, data visualization, and bioinformatics. - Choose Python for: - a broader range of tasks, including web development, machine learning, and general- purpose programming. - integration with diverse tools and platforms beyond statistical analysis.

There are situations when we can use even both python and R. For the genomic research tasks such as sequence analysis, microarray analysis or pathway analysis we can use R . Now for some model to create clusters of the similar sequences, we can use python. Also we can deploy this whole as an application using flask or django.

One of the best ways to begin learning R or Python is to replicate an analysis that you have already performed in Excel. Load, manipulate, and clean the data. Calculate summary statistics. Make a few basic charts. Then output the results back to Excel. Follow a basic tutorial and Google and ask ChatGPT to learn as you go. The best way to learn is by doing.

It can be a highly personal and subjective opinion, but this is my own experience and what I have observed among starters. Watching videos, online courses and workshops practically do not yield results worthing the time and effort invested. Best is to read and practice coding at another window at each step starting from the hello world.

Learning is something which no one can tell you the process. Well for me consistency works. The paragraph here mentions some of the amazing tools to learn. Choose any one but be consistent with your learning path. Do small but do regularly.

R and Python provide extensive libraries and packages for statistical analysis and data manipulation. It's essential to choose the language that aligns with your team's skills and preferences. In many scenarios, a hybrid approach, leveraging the strengths of both languages for different analysis phases, can be beneficial. Remember that the key to success when using R and Python for business decisions goes beyond tool selection. It hinges on your profound understanding of the data, the specific problem, and your capacity to communicate your findings effectively to drive actionable decisions.

R is becoming more accessible. There are more shells now available that allow you to perform analyses without knowing code upfront. Much like the evolution of tools like SPSS which initially required both the knowledge of statistics along with the coding language, shells such as BlueSkyStatistics allow you to now run R without needing much coding knowledge. Additionally, AI tools such as ChatGPT or Bard will give you the needed base code to run some statistical analyses within R or Python. In essence, these "languages" are becoming more accessible. The question, as other posters have posited is, what do you want to do - which then determines which do you which to use.