What are best practices for organizing data science teams? Having data scientists distributed through companies or having a Centre of Excellence? What are the most important skills for data scientists? Is the ability to use the most sophisticated deep learning models more important than being able to make good PowerPoint slides? These are all discussed on this episode of Data Framed.
Everyone keeps saying that there are tons of jobs for mathematicians, but if you search on job sites for "mathematician" all you get are teaching jobs. There are special words that companies use to describe math jobs such as: analytics, operations research, and data science. In this presentation I discuss what steps students can take to make them great candidates for industry, how to find the jobs that have the interesting math problems behind them, and how to answer the age old question: should I go to grad school?
Doing statistical analyses and machine learning in R requires many different components: data, code, models, outputs, and presentations. While one person can usually keep track of their own work,as you grow into a team of people it becomes more important to keep coordinated. This talk discusses the data science work I did as a director of analytics, and why R was a great tool for our team. It covers the best practices we found for working on R code together over many projects and people, and how we handle the occasional instances where we must use other languages.
I never expected my side project, TweetMashup.com, to immediately explode in popularity the moment I launched it. The site wasn't written to scale, nor was I emotionally prepared for it. This talk covers how I had to duct tape the site together to keep it working, and how quickly it entered and exited the public conscious.