In my last post, I discussed the importance of building your network and some strategies for effectively reaching out. I closed with emphasizing how helpful your peers or people one step ahead of you can be. But there’s a specific area where people with more resources, status, or experience can help you: sponsorship. What is sponsorship? When people discuss what they’re seeking from a more senior person in their field, they usually talk about “mentorship.
So you’ve heard you’re supposed to network. That’s the key in getting a job or establishing a reputation in your broader field, right? And it’s true that the importance of having a good network is supported by a lot of social sciences research. But if the thought of networking makes you cringe, you’re not alone. Many people equate networking to sending out millions of unsolicited Linkedin requests with no message, handing out 20 business cards at a meetup once a week, or sending emails to prominent data scientists with the subject line “Can I pick your brain?
In part one of this post, I covered how to start becoming involved in the data science community and meet people in general. But what if you read a really cool post by someone and want to follow up with them? This post offers some thoughts on how you can most effectively reach out to specific people. Two important caveats to start, both inspired by other posts on similar topics. First, to quote Trey Causey: “I am not without sin, and I’m also still figuring all this out.
About two months ago I put a call out to Rstats twitter. I had a working, short script that took 3 minutes to run. While this may be fine if you only need to run it once, I needed to run it hundreds of time for simulations. My first attempt to do so ended about four hours after I started the code, with 400 simulations left to go, and I knew I needed to get some help.
A few weeks ago, I wrote about my experience giving my first data science talk. If you’re interested, the full talk is available online, as well as the slides. In this post, I wanted to share some suggestions for managing business challenges that I didn’t have time to cover in my talk. Why Business Challenges? Why devote a whole post and half a talk to business challenges instead of, say, cutting edge deep learning papers or the shiny new language for handling Big DataTM?