We were recently joined by Prabha Thanikasalam, Senior Director, Analytics and Supply Chain Solutions at Flex.
During the hangout, there was an awesome conversation in the chat and live that followed this question.
Think about it as a marathon, not a sprint, you won’t be an expert in 6 months or a year. Build a good foundation over time by spending 45 minutes a day learning something.
Don’t keep your email inbox open all the time. This won’t work all the time, but it helps!
Try to separate the time you devote to learning new skills rather than "learning while doing" all the time. This can allow you to work faster with imperfect solutions and set aside learning a better way of doing it in dedicated time. Although it's important to keep delivering quality analysis and product, it helps me move away from perfectionist tendencies.
Getting “side-tracked” isn’t necessarily bad - sometimes we’re asked to solve the wrong question and find that out half way through
Give yourself a deadline, or if you’re working with the business meet with them to discuss the success criteria and deadlines
Encourage teams to explore rabbit holes and not be mindless “task completers.” Some of the best insights come out of curveballs we explore.
Curiosity is also key to being a great data person
Try using a pomodoro timer to keep focused time
Check out Jacqueline Nolis & Emily Robinson’s book/companion podcast, "Build a Career in Data Science", which talks about using projects as a motivator for learning new skills. Sometimes, it's better to start with a problem you want to solve rather than start with a skill you want to learn!
When starting out, try to use code as frequently as possible, and also find a project you can get really excited about. Passion can help with the late nights and tying in some of the side track thoughts to one topic or outcome.
It’s helpful to realize there are still plenty of skills that require dedicated time to do more focused learning to exclusively understand the math, methodology, and technology. For example, with Natural Language Processing, starting with the technology and then moving onto the documentation can be an order that keeps you motivated.
An audience-led conversation held on Zoom focused on data science leadership that is open to all every Thursday at 12 ET. You can check out the recordings on this site as well! Learn more on the About DSH page here:https://www.rstudio.com/data-science-hangout/about
You don't have to register to join. You can add the event to your own calendar here:https://www.addevent.com/event/Qv9211919
There are three ways to ask questions during a live session:
All of the recordings from past sessions are shared here:https://www.rstudio.com/data-science-hangout/live-calls
That's up to you. The host, Rachael usually opens up the discussion by asking the featured leader what they are most excited about in data science currently, and then we turn the questions over to the audience. Here are a few examples of questions that have come up before:
What is the divide between a business analyst and a data scientist?
What is the most unique problem you've had to solve in your industry?
My leaders refer to our BI tools when asked about data science, do you consider BI data science? Why/why not?
Did you always have managing skills/people skills, or did you have to learn them?
Could you define levels of data science maturity for companies?