How many times have you heard the phrase “X is better than Y for data science”? This is a very common misconception among data scientists, and a very broad definition of data science as a whole. For data science to be impactful, it needs to be credible, agile, and durable. To be able to do this, we need to embrace the differences between R vs. Python. Maybe you prefer R for data wrangling and Python for modeling – that’s great! Why should serious data science be stifled for the sake of language loyalty? Data science teams need to use the wealth of tools available to them to deliver the most impactful results. This webinar will be a discussion among data science leaders, debunking this common myth.